Deploy Quantum Finance Analyzer

.gitignore

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+__pycache__/
+*.pyc

README.md

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 ---
 title: Quantum Finance Analyzer
-emoji: 📊
+emoji: "⚗"
 colorFrom: indigo
-colorTo: green
+colorTo: purple
 sdk: gradio
-sdk_version: 6.2.0
+sdk_version: "4.44.0"
 app_file: app.py
 pinned: false
 license: mit
-short_description: CrewAI for searching through quantum finance applications
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Quantum Finance Feasibility Analyzer
+
+An interactive tool for evaluating quantum computing applications in finance.
+Assess whether your quantum finance ideas are viable on current NISQ hardware.
+
+## Features
+
+- **Resource Estimation**: Calculate quantum resources for QAOA, Amplitude Estimation, and Grover's Search
+- **Classical Benchmarks**: Compare quantum approaches against classical implementations (portfolio optimization, option pricing)
+- **Use Case Analysis**: Pre-built feasibility analysis for common quantum finance applications
+- **Custom Evaluation**: Evaluate your own quantum finance ideas with quick analysis or full CrewAI multi-agent evaluation
+
+## Supported Quantum Hardware
+
+- IBM Osprey (433 qubits)
+- IBM Condor (1121 qubits)
+- IonQ Forte (36 qubits)
+- Google Sycamore (72 qubits)
+
+## Use Cases
+
+1. **Portfolio Optimization (QAOA)** - Quantum optimization for asset allocation
+2. **Option Pricing (Amplitude Estimation)** - Quantum speedup for derivatives pricing
+3. **Fraud Detection (Quantum ML)** - Variational quantum classifiers for anomaly detection
+4. **Risk Analysis (Quantum Monte Carlo)** - VaR calculations with quantum acceleration
+5. **Credit Scoring (VQC)** - Quantum-enhanced credit risk models
+
+## Local Development
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+## CLI Usage
+
+```bash
+python main.py --idea "Your quantum finance idea"
+python main.py --batch  # Run batch evaluation
+```
+
+## Built With
+
+- [CrewAI](https://crewai.com) - Multi-agent AI framework
+- [Gradio](https://gradio.app) - Web interface
+- [Qiskit](https://qiskit.org) - Quantum computing framework

app.py

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+#!/usr/bin/env python3
+"""
+Gradio Demo App for Quantum Finance Feasibility Analysis Framework.
+
+This app provides an interactive interface for:
+- Quantum resource estimation (QAOA, Amplitude Estimation, Grover's)
+- Classical benchmark comparisons (portfolio optimization, option pricing)
+- Multi-agent evaluation of quantum finance ideas
+- Pre-built use case demonstrations
+"""
+
+import sys
+from pathlib import Path
+
+# Add src to path
+sys.path.insert(0, str(Path(__file__).parent / "src"))
+
+import gradio as gr
+import numpy as np
+import pandas as pd
+
+from tools import (
+    QuantumResourceEstimator,
+    ClassicalPortfolioOptimizer,
+    OptionPricer,
+    FinanceDataCollector,
+)
+
+
+# ============================================================================
+# QUANTUM RESOURCE ESTIMATION TAB
+# ============================================================================
+
+def estimate_qaoa_resources(hardware: str, num_assets: int, p_layers: int):
+    """Estimate QAOA resources for portfolio optimization."""
+    hardware_map = {
+        "IBM Osprey (433 qubits)": "ibm_osprey",
+        "IBM Condor (1121 qubits)": "ibm_condor",
+        "IonQ Forte (36 qubits)": "ionq_forte",
+        "Google Sycamore (72 qubits)": "google_sycamore",
+    }
+
+    estimator = QuantumResourceEstimator(hardware_map[hardware])
+    result = estimator.estimate_qaoa_resources(num_assets=int(num_assets), p_layers=int(p_layers))
+
+    # Format output
+    feasibility = "FEASIBLE" if result['feasible_on_hardware'] else "NOT FEASIBLE"
+    feasibility_color = "green" if result['feasible_on_hardware'] else "red"
+
+    output = f"""## QAOA Resource Estimation Results
+
+### Hardware: {hardware}
+
+| Metric | Value |
+|--------|-------|
+| Qubits Required | {result['qubits_required']} |
+| Qubits Available | {result['qubits_available']} |
+| 1-Qubit Gates | {result['total_1q_gates']:,} |
+| 2-Qubit Gates | {result['total_2q_gates']:,} |
+| Circuit Depth | {result['circuit_depth']} |
+| Execution Time | {result['execution_time_us']:.2f} us |
+| Coherence Time | {result['coherence_time_us']:.0f} us |
+| Success Probability | {result['success_probability']:.2e} |
+
+### Feasibility: **{feasibility}**
+
+**Bottleneck:** {result['bottleneck']}
+"""
+    return output
+
+
+def estimate_amplitude_resources(hardware: str, precision_bits: int, oracle_qubits: int):
+    """Estimate Amplitude Estimation resources for option pricing."""
+    hardware_map = {
+        "IBM Osprey (433 qubits)": "ibm_osprey",
+        "IBM Condor (1121 qubits)": "ibm_condor",
+        "IonQ Forte (36 qubits)": "ionq_forte",
+        "Google Sycamore (72 qubits)": "google_sycamore",
+    }
+
+    estimator = QuantumResourceEstimator(hardware_map[hardware])
+    result = estimator.estimate_amplitude_estimation_resources(
+        precision_bits=int(precision_bits),
+        num_qubits_oracle=int(oracle_qubits)
+    )
+
+    feasibility = "FEASIBLE" if result['feasible_on_hardware'] else "NOT FEASIBLE"
+
+    output = f"""## Amplitude Estimation Resource Results
+
+### Hardware: {hardware}
+
+| Metric | Value |
+|--------|-------|
+| Qubits Required | {result['qubits_required']} |
+| Qubits Available | {result['qubits_available']} |
+| Oracle Calls | {result['oracle_calls']:,} |
+| 2-Qubit Gates | {result['total_2q_gates']:,} |
+| Execution Time | {result['execution_time_us']:.2f} us |
+| Coherence Time | {result['coherence_time_us']:.0f} us |
+| Success Probability | {result['success_probability']:.2e} |
+
+### Feasibility: **{feasibility}**
+
+**Bottleneck:** {result['bottleneck']}
+
+**Note:** Amplitude estimation provides quadratic speedup over classical Monte Carlo
+for option pricing, but requires fault-tolerant quantum computers for practical precision.
+"""
+    return output
+
+
+def estimate_grover_resources(hardware: str, search_space_log: int):
+    """Estimate Grover's search resources for fraud detection."""
+    hardware_map = {
+        "IBM Osprey (433 qubits)": "ibm_osprey",
+        "IBM Condor (1121 qubits)": "ibm_condor",
+        "IonQ Forte (36 qubits)": "ionq_forte",
+        "Google Sycamore (72 qubits)": "google_sycamore",
+    }
+
+    search_space_size = 10 ** int(search_space_log)
+    estimator = QuantumResourceEstimator(hardware_map[hardware])
+    result = estimator.estimate_grover_resources(search_space_size=search_space_size)
+
+    feasibility = "FEASIBLE" if result['feasible_on_hardware'] else "NOT FEASIBLE"
+
+    output = f"""## Grover's Search Resource Results
+
+### Hardware: {hardware}
+### Search Space: 10^{search_space_log} = {search_space_size:,} records
+
+| Metric | Value |
+|--------|-------|
+| Qubits Required | {result['qubits_required']} |
+| Qubits Available | {result['qubits_available']} |
+| Grover Iterations | {result['grover_iterations']:,} |
+| 2-Qubit Gates | {result['total_2q_gates']:,} |
+| Execution Time | {result['execution_time_us']:.2f} us |
+| Coherence Time | {result['coherence_time_us']:.0f} us |
+| Success Probability | {result['success_probability']:.2e} |
+
+### Feasibility: **{feasibility}**
+
+**Classical Speedup:** {result['classical_speedup']}
+
+**Bottleneck:** {result['bottleneck']}
+"""
+    return output
+
+
+# ============================================================================
+# CLASSICAL BENCHMARKS TAB
+# ============================================================================
+
+def run_portfolio_optimization(n_assets: int, n_trials: int):
+    """Run classical portfolio optimization benchmark."""
+    collector = FinanceDataCollector(
+        tickers=FinanceDataCollector.DEFAULT_TICKERS[:int(n_assets)]
+    )
+
+    prices = collector.fetch_historical_data(period="1y")
+    returns = collector.calculate_returns(prices)
+    cov_matrix = collector.calculate_covariance_matrix(returns)
+    exp_returns = collector.calculate_expected_returns(returns)
+
+    optimizer = ClassicalPortfolioOptimizer(
+        exp_returns.values,
+        cov_matrix.values
+    )
+
+    result = optimizer.optimize_markowitz()
+    timing = optimizer.benchmark_timing(n_trials=int(n_trials))
+
+    # Format weights table
+    weights_df = pd.DataFrame({
+        'Asset': exp_returns.index,
+        'Weight': [f"{w:.4f}" for w in result['weights']],
+        'Expected Return': [f"{r:.4f}" for r in exp_returns.values]
+    })
+
+    # Top allocations
+    top_indices = np.argsort(result['weights'])[-5:][::-1]
+    top_assets = [(exp_returns.index[i], result['weights'][i]) for i in top_indices]
+
+    output = f"""## Classical Portfolio Optimization Results
+
+### Portfolio Statistics
+
+| Metric | Value |
+|--------|-------|
+| Number of Assets | {n_assets} |
+| Expected Annual Return | {result['expected_return']:.4f} ({result['expected_return']*100:.2f}%) |
+| Annual Volatility | {result['volatility']:.4f} ({result['volatility']*100:.2f}%) |
+| Sharpe Ratio | {result['sharpe_ratio']:.4f} |
+| Solver | {result['solver']} |
+
+### Top 5 Portfolio Allocations
+
+| Asset | Weight |
+|-------|--------|
+"""
+    for asset, weight in top_assets:
+        output += f"| {asset} | {weight:.4f} ({weight*100:.2f}%) |\n"
+
+    output += f"""
+### Timing Benchmark ({n_trials} trials)
+
+| Metric | Value |
+|--------|-------|
+| Mean Time | {timing['mean_time_ms']:.3f} ms |
+| Std Dev | {timing['std_time_ms']:.3f} ms |
+| Min Time | {timing['min_time_ms']:.3f} ms |
+| Max Time | {timing['max_time_ms']:.3f} ms |
+
+**Quantum Comparison:** QAOA would require ~{n_assets} qubits with O(n^2) 2-qubit gates per layer.
+Classical optimization at this scale is highly efficient and quantum advantage is unlikely.
+"""
+    return output
+
+
+def run_option_pricing(spot: float, strike: float, maturity: float, rate: float, volatility: float, n_paths: int):
+    """Run option pricing comparison."""
+    # Black-Scholes analytical
+    bs_price = OptionPricer.black_scholes_call(
+        S=spot, K=strike, T=maturity, r=rate, sigma=volatility
+    )
+
+    # Monte Carlo
+    mc_result = OptionPricer.monte_carlo_call(
+        S=spot, K=strike, T=maturity, r=rate, sigma=volatility,
+        n_paths=int(n_paths)
+    )
+
+    # Timing benchmark
+    import time
+    times = []
+    for _ in range(10):
+        start = time.perf_counter()
+        OptionPricer.monte_carlo_call(
+            S=spot, K=strike, T=maturity, r=rate, sigma=volatility,
+            n_paths=int(n_paths)
+        )
+        times.append(time.perf_counter() - start)
+
+    mean_time = np.mean(times) * 1000
+
+    output = f"""## Option Pricing Comparison
+
+### Parameters
+
+| Parameter | Value |
+|-----------|-------|
+| Spot Price (S) | ${spot:.2f} |
+| Strike Price (K) | ${strike:.2f} |
+| Time to Maturity | {maturity:.2f} years |
+| Risk-Free Rate | {rate*100:.2f}% |
+| Volatility | {volatility*100:.2f}% |
+
+### Results
+
+| Method | Price | Notes |
+|--------|-------|-------|
+| Black-Scholes (Analytical) | ${bs_price:.4f} | Exact solution |
+| Monte Carlo ({n_paths:,} paths) | ${mc_result['price']:.4f} | Std Error: {mc_result['std_error']:.6f} |
+
+**95% Confidence Interval:** ${mc_result['confidence_interval_95'][0]:.4f} - ${mc_result['confidence_interval_95'][1]:.4f}
+
+### Monte Carlo Timing (10 trials)
+
+| Metric | Value |
+|--------|-------|
+| Mean Time | {mean_time:.3f} ms |
+| Paths/Second | {int(n_paths)/(mean_time/1000):,.0f} |
+
+### Quantum Advantage Analysis
+
+**Amplitude Estimation** promises quadratic speedup over Monte Carlo:
+- Classical: O(1/epsilon^2) paths for precision epsilon
+- Quantum: O(1/epsilon) oracle calls
+
+For {volatility*100:.0f}% vol option with 0.01% precision:
+- Classical Monte Carlo: ~100M paths needed
+- Quantum (theoretical): ~10K oracle calls
+
+**Reality Check:** Current NISQ hardware cannot achieve the required circuit depth
+for meaningful amplitude estimation. Error rates and coherence times are limiting factors.
+"""
+    return output
+
+
+# ============================================================================
+# USE CASES TAB
+# ============================================================================
+
+TEST_CASES = [
+    {
+        'id': 'portfolio_optimization',
+        'name': 'Portfolio Optimization (QAOA)',
+        'idea': 'Quantum portfolio optimization using QAOA for a 100-asset portfolio with mean-variance objective',
+        'category': 'optimization',
+        'description': 'Use QAOA to find optimal portfolio weights minimizing variance for target return.'
+    },
+    {
+        'id': 'option_pricing',
+        'name': 'Option Pricing (Amplitude Estimation)',
+        'idea': 'Quantum amplitude estimation for pricing European call options with 8-bit precision',
+        'category': 'pricing',
+        'description': 'Use quantum amplitude estimation to price derivatives faster than Monte Carlo.'
+    },
+    {
+        'id': 'fraud_detection',
+        'name': 'Fraud Detection (Quantum ML)',
+        'idea': 'Quantum machine learning classifier for real-time credit card fraud detection',
+        'category': 'ml',
+        'description': 'Use variational quantum classifiers for pattern recognition in transactions.'
+    },
+    {
+        'id': 'risk_analysis',
+        'name': 'Risk Analysis (Quantum Monte Carlo)',
+        'idea': 'Quantum Monte Carlo for Value-at-Risk (VaR) calculation on a derivatives portfolio',
+        'category': 'risk',
+        'description': 'Use quantum speedup for risk metric calculations.'
+    },
+    {
+        'id': 'credit_scoring',
+        'name': 'Credit Scoring (VQC)',
+        'idea': 'Variational quantum classifier for credit scoring using customer financial data',
+        'category': 'ml',
+        'description': 'Use VQC for binary classification of creditworthiness.'
+    }
+]
+
+
+def analyze_use_case(case_name: str):
+    """Analyze a pre-built use case."""
+    case = next((c for c in TEST_CASES if c['name'] == case_name), None)
+    if not case:
+        return "Use case not found"
+
+    # Run resource estimation based on category
+    estimator = QuantumResourceEstimator('ibm_osprey')
+
+    if case['category'] == 'optimization':
+        resources = estimator.estimate_qaoa_resources(num_assets=100, p_layers=3)
+        algorithm = "QAOA"
+        classical_time = "~10ms with scipy"
+    elif case['category'] == 'pricing':
+        resources = estimator.estimate_amplitude_estimation_resources(
+            precision_bits=8, num_qubits_oracle=20
+        )
+        algorithm = "Amplitude Estimation"
+        classical_time = "~100ms Monte Carlo (1M paths)"
+    elif case['category'] == 'risk':
+        resources = estimator.estimate_amplitude_estimation_resources(
+            precision_bits=10, num_qubits_oracle=30
+        )
+        algorithm = "Quantum Monte Carlo"
+        classical_time = "~1s Monte Carlo (10M paths)"
+    else:  # ml
+        resources = estimator.estimate_qaoa_resources(num_assets=50, p_layers=5)
+        algorithm = "Variational Quantum Classifier"
+        classical_time = "~5ms classical ML inference"
+
+    feasibility = "FEASIBLE" if resources['feasible_on_hardware'] else "NOT FEASIBLE"
+
+    output = f"""## Use Case Analysis: {case['name']}
+
+### Description
+{case['description']}
+
+### Proposed Idea
+> {case['idea']}
+
+### Algorithm: {algorithm}
+
+### Resource Requirements (IBM Osprey)
+
+| Metric | Value |
+|--------|-------|
+| Qubits Required | {resources['qubits_required']} |
+| Qubits Available | {resources['qubits_available']} |
+| Total 2-Qubit Gates | {resources.get('total_2q_gates', 'N/A'):,} |
+| Execution Time | {resources['execution_time_us']:.2f} us |
+| Coherence Time | {resources['coherence_time_us']:.0f} us |
+| Success Probability | {resources['success_probability']:.2e} |
+
+### Feasibility Assessment: **{feasibility}**
+
+**Bottleneck:** {resources['bottleneck']}
+
+### Quantum vs Classical Comparison
+
+| Aspect | Quantum | Classical |
+|--------|---------|-----------|
+| Execution Time | {resources['execution_time_us']:.2f} us (per shot) | {classical_time} |
+| Error Rate | {(1-resources['success_probability'])*100:.2f}% failure | Deterministic |
+| Scalability | Limited by coherence | Memory-bound |
+
+### Verdict
+
+"""
+    if resources['feasible_on_hardware']:
+        output += """**Potentially viable** on current hardware, but likely no practical advantage over
+classical methods due to:
+- High shot count requirements for statistical accuracy
+- Variational parameter optimization overhead
+- Limited problem sizes that fit in coherence window
+"""
+    else:
+        output += f"""**Not viable** on current NISQ hardware due to:
+- {resources['bottleneck']}
+
+**Timeline:** This application likely requires fault-tolerant quantum computers
+(estimated 2030+ for practical advantage).
+"""
+
+    return output
+
+
+# ============================================================================
+# IDEA EVALUATION TAB (CrewAI Integration)
+# ============================================================================
+
+def evaluate_idea_quick(idea: str):
+    """Quick evaluation without full CrewAI (for demo purposes)."""
+    if not idea.strip():
+        return "Please enter a quantum finance idea to evaluate."
+
+    # Analyze the idea based on keywords
+    idea_lower = idea.lower()
+
+    # Determine algorithm type
+    if any(word in idea_lower for word in ['portfolio', 'optimization', 'qaoa']):
+        algorithm = "QAOA"
+        estimator = QuantumResourceEstimator('ibm_osprey')
+        # Extract asset count if mentioned
+        import re
+        match = re.search(r'(\d+)[- ]?asset', idea_lower)
+        n_assets = int(match.group(1)) if match else 50
+        resources = estimator.estimate_qaoa_resources(num_assets=n_assets, p_layers=3)
+
+    elif any(word in idea_lower for word in ['option', 'pricing', 'amplitude', 'derivative']):
+        algorithm = "Amplitude Estimation"
+        estimator = QuantumResourceEstimator('ibm_osprey')
+        resources = estimator.estimate_amplitude_estimation_resources(
+            precision_bits=8, num_qubits_oracle=20
+        )
+
+    elif any(word in idea_lower for word in ['fraud', 'detection', 'search', 'grover']):
+        algorithm = "Grover's Search / Quantum ML"
+        estimator = QuantumResourceEstimator('ibm_osprey')
+        resources = estimator.estimate_grover_resources(search_space_size=1_000_000)
+
+    elif any(word in idea_lower for word in ['risk', 'var', 'monte carlo', 'simulation']):
+        algorithm = "Quantum Monte Carlo"
+        estimator = QuantumResourceEstimator('ibm_osprey')
+        resources = estimator.estimate_amplitude_estimation_resources(
+            precision_bits=10, num_qubits_oracle=25
+        )
+
+    elif any(word in idea_lower for word in ['credit', 'scoring', 'classifier', 'ml', 'machine learning']):
+        algorithm = "Variational Quantum Classifier"
+        estimator = QuantumResourceEstimator('ibm_osprey')
+        resources = estimator.estimate_qaoa_resources(num_assets=30, p_layers=5)
+
+    else:
+        algorithm = "General Variational Algorithm"
+        estimator = QuantumResourceEstimator('ibm_osprey')
+        resources = estimator.estimate_qaoa_resources(num_assets=20, p_layers=2)
+
+    feasibility = "FEASIBLE" if resources['feasible_on_hardware'] else "NOT FEASIBLE"
+
+    # Generate assessment scores
+    scores = {
+        'algorithm_fit': 0.7 if resources['feasible_on_hardware'] else 0.3,
+        'hardware_readiness': resources['success_probability'],
+        'classical_comparison': 0.2 if not resources['feasible_on_hardware'] else 0.5,
+        'business_viability': 0.4 if resources['feasible_on_hardware'] else 0.1,
+    }
+
+    output = f"""## Quick Feasibility Analysis
+
+### Your Idea
+> {idea}
+
+### Identified Algorithm: {algorithm}
+
+### Hardware Feasibility (IBM Osprey)
+
+| Metric | Value |
+|--------|-------|
+| Qubits Required | {resources['qubits_required']} |
+| Qubits Available | {resources['qubits_available']} |
+| Execution Time | {resources['execution_time_us']:.2f} us |
+| Coherence Time | {resources['coherence_time_us']:.0f} us |
+| Success Probability | {resources['success_probability']:.2e} |
+
+### Overall Feasibility: **{feasibility}**
+
+**Primary Bottleneck:** {resources['bottleneck']}
+
+### Assessment Scores (0-1 scale)
+
+| Dimension | Score | Interpretation |
+|-----------|-------|----------------|
+| Algorithm Fit | {scores['algorithm_fit']:.2f} | {"Good match" if scores['algorithm_fit'] > 0.5 else "Poor match"} |
+| Hardware Readiness | {scores['hardware_readiness']:.2e} | {"Ready" if scores['hardware_readiness'] > 0.01 else "Not ready"} |
+| Classical Advantage | {scores['classical_comparison']:.2f} | {"Potential" if scores['classical_comparison'] > 0.4 else "Unlikely"} |
+| Business Viability | {scores['business_viability']:.2f} | {"Viable" if scores['business_viability'] > 0.3 else "Premature"} |
+
+### Recommendations
+
+"""
+
+    if resources['feasible_on_hardware']:
+        output += """1. **Proof-of-concept possible** - The idea can be demonstrated on current hardware
+2. **Focus on small instances** - Limit problem size to stay within coherence window
+3. **Benchmark carefully** - Compare against optimized classical implementations
+4. **Hybrid approach** - Consider quantum-classical hybrid strategies
+"""
+    else:
+        output += """1. **Scale down** - Reduce problem size to fit hardware constraints
+2. **Wait for hardware** - Better quantum computers expected in 2-5 years
+3. **Classical alternatives** - Current classical methods likely more practical
+4. **Research angle** - Frame as algorithmic research rather than near-term application
+"""
+
+    output += """
+---
+*Note: This is a quick analysis. For comprehensive multi-agent evaluation,
+use the full CrewAI framework via CLI: `python main.py --idea "your idea"`*
+"""
+
+    return output
+
+
+def run_full_evaluation(idea: str):
+    """Run full CrewAI evaluation (may take several minutes)."""
+    if not idea.strip():
+        return "Please enter a quantum finance idea to evaluate."
+
+    try:
+        from quantum_finance_crew import QuantumFinanceCrew
+
+        crew = QuantumFinanceCrew()
+        result = crew.evaluate(idea, verbose=False)
+
+        output = f"""## Full Multi-Agent Evaluation
+
+### Your Idea
+> {idea}
+
+### CrewAI Analysis Complete
+
+**Tasks Completed:** {result.get('tasks_completed', 'N/A')}
+
+### Final Assessment
+
+{result.get('result', 'No result available')}
+
+---
+*Evaluated by: Quantum Algorithm Expert, Error Correction Expert,
+Classical Computing Expert, and Finance Domain Expert*
+"""
+        return output
+
+    except Exception as e:
+        return f"""## Evaluation Error
+
+Could not run full CrewAI evaluation: {str(e)}
+
+**Possible causes:**
+- Missing API keys (OPENAI_API_KEY or ANTHROPIC_API_KEY)
+- CrewAI configuration issues
+
+**Alternative:** Use the Quick Analysis above for instant resource estimation.
+"""
+
+
+# ============================================================================
+# BUILD GRADIO APP
+# ============================================================================
+
+def create_app():
+    """Create the Gradio application."""
+
+    with gr.Blocks(
+        title="Quantum Finance Feasibility Analyzer",
+        theme=gr.themes.Soft()
+    ) as app:
+
+        gr.Markdown("""
+# Quantum Finance Feasibility Analyzer
+
+An interactive tool for evaluating quantum computing applications in finance.
+Assess whether your quantum finance ideas are viable on current NISQ hardware.
+
+**Features:**
+- Quantum resource estimation for QAOA, Amplitude Estimation, and Grover's Search
+- Classical benchmark comparisons (portfolio optimization, option pricing)
+- Pre-built use case analysis
+- Custom idea evaluation
+        """)
+
+        with gr.Tabs():
+            # Tab 1: Resource Estimation
+            with gr.TabItem("Resource Estimation"):
+                gr.Markdown("## Quantum Resource Calculator")
+                gr.Markdown("Estimate the quantum resources required for different finance algorithms.")
+
+                with gr.Tabs():
+                    # QAOA
+                    with gr.TabItem("QAOA (Portfolio Optimization)"):
+                        with gr.Row():
+                            with gr.Column():
+                                qaoa_hardware = gr.Dropdown(
+                                    choices=[
+                                        "IBM Osprey (433 qubits)",
+                                        "IBM Condor (1121 qubits)",
+                                        "IonQ Forte (36 qubits)",
+                                        "Google Sycamore (72 qubits)"
+                                    ],
+                                    value="IBM Osprey (433 qubits)",
+                                    label="Target Hardware"
+                                )
+                                qaoa_assets = gr.Slider(
+                                    minimum=5, maximum=200, value=50, step=5,
+                                    label="Number of Assets"
+                                )
+                                qaoa_layers = gr.Slider(
+                                    minimum=1, maximum=10, value=3, step=1,
+                                    label="QAOA Layers (p)"
+                                )
+                                qaoa_btn = gr.Button("Estimate Resources", variant="primary")
+                            with gr.Column():
+                                qaoa_output = gr.Markdown()
+
+                        qaoa_btn.click(
+                            estimate_qaoa_resources,
+                            inputs=[qaoa_hardware, qaoa_assets, qaoa_layers],
+                            outputs=qaoa_output
+                        )
+
+                    # Amplitude Estimation
+                    with gr.TabItem("Amplitude Estimation (Option Pricing)"):
+                        with gr.Row():
+                            with gr.Column():
+                                ae_hardware = gr.Dropdown(
+                                    choices=[
+                                        "IBM Osprey (433 qubits)",
+                                        "IBM Condor (1121 qubits)",
+                                        "IonQ Forte (36 qubits)",
+                                        "Google Sycamore (72 qubits)"
+                                    ],
+                                    value="IBM Osprey (433 qubits)",
+                                    label="Target Hardware"
+                                )
+                                ae_precision = gr.Slider(
+                                    minimum=2, maximum=12, value=8, step=1,
+                                    label="Precision Bits"
+                                )
+                                ae_oracle = gr.Slider(
+                                    minimum=5, maximum=50, value=20, step=5,
+                                    label="Oracle Qubits"
+                                )
+                                ae_btn = gr.Button("Estimate Resources", variant="primary")
+                            with gr.Column():
+                                ae_output = gr.Markdown()
+
+                        ae_btn.click(
+                            estimate_amplitude_resources,
+                            inputs=[ae_hardware, ae_precision, ae_oracle],
+                            outputs=ae_output
+                        )
+
+                    # Grover's Search
+                    with gr.TabItem("Grover's Search (Fraud Detection)"):
+                        with gr.Row():
+                            with gr.Column():
+                                grover_hardware = gr.Dropdown(
+                                    choices=[
+                                        "IBM Osprey (433 qubits)",
+                                        "IBM Condor (1121 qubits)",
+                                        "IonQ Forte (36 qubits)",
+                                        "Google Sycamore (72 qubits)"
+                                    ],
+                                    value="IBM Osprey (433 qubits)",
+                                    label="Target Hardware"
+                                )
+                                grover_space = gr.Slider(
+                                    minimum=3, maximum=9, value=6, step=1,
+                                    label="Search Space Size (10^x records)"
+                                )
+                                grover_btn = gr.Button("Estimate Resources", variant="primary")
+                            with gr.Column():
+                                grover_output = gr.Markdown()
+
+                        grover_btn.click(
+                            estimate_grover_resources,
+                            inputs=[grover_hardware, grover_space],
+                            outputs=grover_output
+                        )
+
+            # Tab 2: Classical Benchmarks
+            with gr.TabItem("Classical Benchmarks"):
+                gr.Markdown("## Classical Finance Benchmarks")
+                gr.Markdown("Compare quantum approaches against classical implementations.")
+
+                with gr.Tabs():
+                    with gr.TabItem("Portfolio Optimization"):
+                        with gr.Row():
+                            with gr.Column():
+                                port_assets = gr.Slider(
+                                    minimum=5, maximum=25, value=15, step=1,
+                                    label="Number of Assets"
+                                )
+                                port_trials = gr.Slider(
+                                    minimum=10, maximum=500, value=100, step=10,
+                                    label="Timing Trials"
+                                )
+                                port_btn = gr.Button("Run Optimization", variant="primary")
+                            with gr.Column():
+                                port_output = gr.Markdown()
+
+                        port_btn.click(
+                            run_portfolio_optimization,
+                            inputs=[port_assets, port_trials],
+                            outputs=port_output
+                        )
+
+                    with gr.TabItem("Option Pricing"):
+                        with gr.Row():
+                            with gr.Column():
+                                opt_spot = gr.Number(value=100, label="Spot Price ($)")
+                                opt_strike = gr.Number(value=100, label="Strike Price ($)")
+                                opt_maturity = gr.Slider(
+                                    minimum=0.1, maximum=2.0, value=1.0, step=0.1,
+                                    label="Time to Maturity (years)"
+                                )
+                                opt_rate = gr.Slider(
+                                    minimum=0.01, maximum=0.10, value=0.05, step=0.01,
+                                    label="Risk-Free Rate"
+                                )
+                                opt_vol = gr.Slider(
+                                    minimum=0.1, maximum=0.5, value=0.2, step=0.05,
+                                    label="Volatility"
+                                )
+                                opt_paths = gr.Slider(
+                                    minimum=10000, maximum=1000000, value=100000, step=10000,
+                                    label="Monte Carlo Paths"
+                                )
+                                opt_btn = gr.Button("Price Option", variant="primary")
+                            with gr.Column():
+                                opt_output = gr.Markdown()
+
+                        opt_btn.click(
+                            run_option_pricing,
+                            inputs=[opt_spot, opt_strike, opt_maturity, opt_rate, opt_vol, opt_paths],
+                            outputs=opt_output
+                        )
+
+            # Tab 3: Use Cases
+            with gr.TabItem("Use Cases"):
+                gr.Markdown("## Pre-Built Use Case Analysis")
+                gr.Markdown("Explore detailed feasibility analysis for common quantum finance applications.")
+
+                with gr.Row():
+                    with gr.Column():
+                        use_case = gr.Dropdown(
+                            choices=[c['name'] for c in TEST_CASES],
+                            value=TEST_CASES[0]['name'],
+                            label="Select Use Case"
+                        )
+                        use_case_btn = gr.Button("Analyze Use Case", variant="primary")
+                    with gr.Column():
+                        use_case_output = gr.Markdown()
+
+                use_case_btn.click(
+                    analyze_use_case,
+                    inputs=[use_case],
+                    outputs=use_case_output
+                )
+
+            # Tab 4: Idea Evaluation
+            with gr.TabItem("Evaluate Your Idea"):
+                gr.Markdown("## Custom Idea Evaluation")
+                gr.Markdown("Enter your quantum finance idea for feasibility analysis.")
+
+                with gr.Row():
+                    with gr.Column():
+                        idea_input = gr.Textbox(
+                            lines=4,
+                            placeholder="Example: Quantum optimization for rebalancing a 50-asset ETF portfolio using QAOA with real-time market data...",
+                            label="Your Quantum Finance Idea"
+                        )
+                        with gr.Row():
+                            quick_btn = gr.Button("Quick Analysis", variant="primary")
+                            full_btn = gr.Button("Full CrewAI Evaluation", variant="secondary")
+
+                        gr.Markdown("""
+**Quick Analysis:** Instant resource estimation and feasibility check.
+
+**Full CrewAI Evaluation:** Comprehensive multi-agent analysis (requires API keys, may take several minutes).
+                        """)
+
+                    with gr.Column():
+                        idea_output = gr.Markdown()
+
+                quick_btn.click(
+                    evaluate_idea_quick,
+                    inputs=[idea_input],
+                    outputs=idea_output
+                )
+
+                full_btn.click(
+                    run_full_evaluation,
+                    inputs=[idea_input],
+                    outputs=idea_output
+                )
+
+        gr.Markdown("""
+---
+**Quantum Finance Feasibility Analysis Framework**
+
+Built with CrewAI for multi-agent evaluation. Resource estimates based on current NISQ hardware specifications.
+
+*For command-line usage:* `python main.py --help`
+        """)
+
+    return app
+
+
+# Main entry point
+if __name__ == "__main__":
+    app = create_app()
+    app.launch()

config/agents.yaml

@@ -0,0 +1,48 @@
+quantum_algorithm_expert:
+  role: "Quantum Algorithm Expert"
+  goal: "Assess whether proposed finance applications map to known quantum algorithms (QAOA, VQE, Grover's, Amplitude Estimation) and estimate their computational complexity on NISQ hardware"
+  backstory: |
+    You are a leading expert in quantum algorithms with deep knowledge of variational quantum
+    eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), Grover's search,
+    and quantum amplitude estimation. You understand the theoretical speedups these algorithms
+    offer and their practical limitations on near-term intermediate-scale quantum (NISQ) devices.
+    You critically evaluate claims of quantum advantage and can identify when classical
+    algorithms remain competitive.
+  verbose: true
+  allow_delegation: true
+
+quantum_error_correction_expert:
+  role: "Quantum Error Correction Expert"
+  goal: "Evaluate the impact of noise, decoherence, and error rates on proposed quantum finance applications using NISQ hardware constraints"
+  backstory: |
+    You specialize in quantum error correction and noise modeling for NISQ devices. You understand
+    current hardware limitations including qubit counts (<1,000 qubits), gate fidelities (99-99.9%),
+    coherence times, and connectivity constraints. You can estimate circuit depth limits and
+    determine whether proposed applications are feasible given realistic error budgets.
+    You are familiar with error mitigation techniques and can assess their effectiveness.
+  verbose: true
+  allow_delegation: true
+
+classical_computing_expert:
+  role: "Classical Computing Expert"
+  goal: "Provide rigorous classical baselines and benchmarks to compare against proposed quantum solutions, identifying when classical methods are sufficient"
+  backstory: |
+    You are an expert in classical optimization, machine learning, and high-performance computing.
+    You know the state-of-the-art in classical solvers (Gurobi, CPLEX), GPU-accelerated computing,
+    tensor network methods, and Monte Carlo simulations. You can estimate classical runtime
+    complexity and identify when quantum approaches offer no practical advantage over
+    well-optimized classical implementations.
+  verbose: true
+  allow_delegation: true
+
+finance_domain_expert:
+  role: "Finance Domain Expert"
+  goal: "Evaluate the commercial viability, regulatory fit, and practical constraints of quantum finance applications from an industry perspective"
+  backstory: |
+    You are a senior quantitative finance professional with expertise in portfolio optimization,
+    derivatives pricing, risk management, and fraud detection. You understand regulatory
+    requirements (Basel III/IV, SEC rules), latency constraints in trading, and the practical
+    challenges of deploying new technologies in financial institutions. You can assess whether
+    proposed quantum solutions address real business needs and fit within existing infrastructure.
+  verbose: true
+  allow_delegation: true

config/tasks.yaml

@@ -0,0 +1,108 @@
+analyze_quantum_feasibility:
+  description: |
+    Analyze the quantum algorithm feasibility for the proposed finance application: {idea}
+
+    Evaluate:
+    1. Which quantum algorithms (QAOA, VQE, Grover's, Amplitude Estimation, etc.) could apply
+    2. Theoretical complexity improvements over classical approaches
+    3. Required qubit count and circuit depth estimates
+    4. Known limitations and open problems for this algorithm class
+
+    Provide a feasibility score (0-1) with detailed justification.
+  expected_output: |
+    A structured analysis containing:
+    - Applicable quantum algorithms with rationale
+    - Complexity analysis (quantum vs classical)
+    - Resource requirements (qubits, gates, depth)
+    - Feasibility score with justification
+    - Key bottlenecks and risks
+  agent: quantum_algorithm_expert
+
+assess_nisq_constraints:
+  description: |
+    Assess the NISQ hardware constraints for implementing: {idea}
+
+    Consider:
+    1. Current hardware capabilities (IBM, IonQ, Google - 2024-2026 roadmaps)
+    2. Noise impact on algorithm performance
+    3. Error mitigation strategies and their overhead
+    4. Realistic timeline for hardware sufficient to run this application
+
+    Provide a hardware readiness score (0-1).
+  expected_output: |
+    A structured assessment containing:
+    - Hardware requirements vs current capabilities
+    - Noise sensitivity analysis
+    - Recommended error mitigation approaches
+    - Hardware readiness score with timeline estimate
+    - Critical hardware milestones needed
+  agent: quantum_error_correction_expert
+
+benchmark_classical_alternatives:
+  description: |
+    Benchmark classical alternatives for: {idea}
+
+    Analyze:
+    1. Best classical algorithms and solvers for this problem
+    2. State-of-the-art performance on relevant benchmarks
+    3. GPU/TPU acceleration potential
+    4. Quantum-inspired classical methods (tensor networks, etc.)
+
+    Provide a classical competitiveness score (0-1, where 1 = classical is fully sufficient).
+  expected_output: |
+    A structured benchmark containing:
+    - Best classical approaches with performance data
+    - Scalability analysis
+    - Hardware costs (classical vs quantum)
+    - Classical competitiveness score
+    - Crossover point estimate (if any)
+  agent: classical_computing_expert
+
+evaluate_business_viability:
+  description: |
+    Evaluate the business viability of: {idea}
+
+    Consider:
+    1. Real business value and use case fit
+    2. Regulatory and compliance implications
+    3. Integration with existing financial infrastructure
+    4. Cost-benefit analysis (quantum vs classical deployment)
+    5. Risk tolerance of financial institutions for new technology
+
+    Provide a commercial viability score (0-1).
+  expected_output: |
+    A structured evaluation containing:
+    - Business value assessment
+    - Regulatory considerations
+    - Integration challenges
+    - Cost-benefit analysis
+    - Commercial viability score with timeline
+    - Recommended next steps
+  agent: finance_domain_expert
+
+synthesize_final_assessment:
+  description: |
+    Synthesize all expert analyses into a final feasibility assessment for: {idea}
+
+    Integrate findings from:
+    - Quantum algorithm feasibility analysis
+    - NISQ hardware constraints assessment
+    - Classical benchmarking results
+    - Business viability evaluation
+
+    Determine overall quantum advantage potential and provide actionable recommendations.
+  expected_output: |
+    A comprehensive final report containing:
+    - Executive summary (2-3 sentences)
+    - Overall feasibility score (0-1) with breakdown
+    - Quantum advantage assessment (Yes/No/Hybrid with justification)
+    - Key bottlenecks ranked by severity
+    - Recommended alternatives if quantum not viable
+    - Actionable next steps
+    - Timeline estimate for viable implementation
+  agent: quantum_algorithm_expert
+  context:
+    - analyze_quantum_feasibility
+    - assess_nisq_constraints
+    - benchmark_classical_alternatives
+    - evaluate_business_viability

main.py

@@ -0,0 +1,216 @@
+#!/usr/bin/env python3
+"""
+Main entry point for the Quantum Finance Feasibility Analysis Framework.
+
+Usage:
+    python main.py                          # Run demo evaluation
+    python main.py --idea "Your idea here"  # Evaluate specific idea
+    python main.py --batch                  # Run batch evaluation on test cases
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+# Add src to path
+sys.path.insert(0, str(Path(__file__).parent / "src"))
+
+from quantum_finance_crew import QuantumFinanceCrew
+from tools import (
+    QuantumResourceEstimator,
+    collect_experiment_data,
+    ClassicalPortfolioOptimizer
+)
+from utils import MetricsCollector, EvaluationMetrics
+
+
+# Standard test cases for experiments
+TEST_CASES = [
+    {
+        'id': 'portfolio_optimization',
+        'idea': 'Quantum portfolio optimization using QAOA for a 100-asset portfolio with mean-variance objective',
+        'category': 'optimization'
+    },
+    {
+        'id': 'option_pricing',
+        'idea': 'Quantum amplitude estimation for pricing European call options with 8-bit precision',
+        'category': 'pricing'
+    },
+    {
+        'id': 'fraud_detection',
+        'idea': 'Quantum machine learning classifier for real-time credit card fraud detection',
+        'category': 'ml'
+    },
+    {
+        'id': 'risk_analysis',
+        'idea': 'Quantum Monte Carlo for Value-at-Risk (VaR) calculation on a derivatives portfolio',
+        'category': 'risk'
+    },
+    {
+        'id': 'credit_scoring',
+        'idea': 'Variational quantum classifier for credit scoring using customer financial data',
+        'category': 'ml'
+    }
+]
+
+
+def run_resource_estimation_demo():
+    """Demonstrate quantum resource estimation capabilities."""
+    print("\n" + "=" * 60)
+    print("QUANTUM RESOURCE ESTIMATION DEMO")
+    print("=" * 60)
+
+    estimator = QuantumResourceEstimator('ibm_osprey')
+
+    # Portfolio optimization
+    print("\n1. QAOA for 50-Asset Portfolio (p=3 layers):")
+    print("-" * 50)
+    result = estimator.estimate_qaoa_resources(num_assets=50, p_layers=3)
+    for key, value in result.items():
+        print(f"   {key}: {value}")
+
+    # Option pricing
+    print("\n2. Amplitude Estimation for Option Pricing (8-bit precision):")
+    print("-" * 50)
+    result = estimator.estimate_amplitude_estimation_resources(
+        precision_bits=8, num_qubits_oracle=20
+    )
+    for key, value in result.items():
+        print(f"   {key}: {value}")
+
+    # Fraud detection
+    print("\n3. Grover's Search for Pattern Matching (1M records):")
+    print("-" * 50)
+    result = estimator.estimate_grover_resources(search_space_size=1_000_000)
+    for key, value in result.items():
+        print(f"   {key}: {value}")
+
+
+def run_classical_benchmark_demo():
+    """Demonstrate classical benchmarking capabilities."""
+    print("\n" + "=" * 60)
+    print("CLASSICAL BENCHMARK DEMO")
+    print("=" * 60)
+
+    print("\nCollecting financial data...")
+    data = collect_experiment_data(n_assets=15)
+    print(f"Loaded {data['n_observations']} days of data for {data['n_assets']} assets")
+
+    print("\nRunning classical portfolio optimization:")
+    print("-" * 50)
+    optimizer = ClassicalPortfolioOptimizer(
+        data['expected_returns'].values,
+        data['covariance_matrix'].values
+    )
+    result = optimizer.optimize_markowitz()
+    print(f"   Expected Return: {result['expected_return']:.4f}")
+    print(f"   Volatility: {result['volatility']:.4f}")
+    print(f"   Sharpe Ratio: {result['sharpe_ratio']:.4f}")
+
+    print("\nTiming benchmark (100 runs):")
+    timing = optimizer.benchmark_timing(n_trials=100)
+    print(f"   Mean time: {timing['mean_time_ms']:.3f} ms")
+    print(f"   Std time: {timing['std_time_ms']:.3f} ms")
+
+
+def run_crew_evaluation(idea: str, verbose: bool = True):
+    """Run the full crew evaluation on an idea."""
+    print("\n" + "=" * 60)
+    print("QUANTUM FINANCE FEASIBILITY ANALYSIS")
+    print("=" * 60)
+    print(f"\nIdea: {idea}")
+    print("-" * 60)
+
+    crew = QuantumFinanceCrew()
+    result = crew.evaluate(idea, verbose=verbose)
+
+    print("\n" + "=" * 60)
+    print("EVALUATION COMPLETE")
+    print("=" * 60)
+
+    return result
+
+
+def run_batch_evaluation(verbose: bool = False):
+    """Run evaluation on all test cases."""
+    print("\n" + "=" * 60)
+    print("BATCH EVALUATION - ALL TEST CASES")
+    print("=" * 60)
+
+    crew = QuantumFinanceCrew()
+    metrics_collector = MetricsCollector()
+
+    results = []
+    for i, case in enumerate(TEST_CASES):
+        print(f"\n[{i+1}/{len(TEST_CASES)}] Evaluating: {case['id']}")
+        print(f"    {case['idea'][:60]}...")
+
+        metrics = metrics_collector.start_evaluation(case['idea'])
+
+        try:
+            result = crew.evaluate(case['idea'], verbose=verbose)
+            metrics.total_agent_rounds = result.get('tasks_completed', 0)
+            result['case_id'] = case['id']
+            results.append(result)
+        except Exception as e:
+            print(f"    ERROR: {e}")
+            result = {'case_id': case['id'], 'error': str(e)}
+            results.append(result)
+
+        metrics.complete()
+
+    print("\n" + "=" * 60)
+    print("BATCH EVALUATION SUMMARY")
+    print("=" * 60)
+    summary = metrics_collector.get_summary_statistics()
+    for key, value in summary.items():
+        print(f"   {key}: {value}")
+
+    return results
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='Quantum Finance Feasibility Analysis Framework'
+    )
+    parser.add_argument(
+        '--idea', type=str,
+        help='Specific quantum finance idea to evaluate'
+    )
+    parser.add_argument(
+        '--batch', action='store_true',
+        help='Run batch evaluation on all test cases'
+    )
+    parser.add_argument(
+        '--demo', action='store_true',
+        help='Run resource estimation and benchmark demos'
+    )
+    parser.add_argument(
+        '--verbose', '-v', action='store_true',
+        help='Enable verbose output'
+    )
+
+    args = parser.parse_args()
+
+    if args.demo:
+        run_resource_estimation_demo()
+        run_classical_benchmark_demo()
+    elif args.batch:
+        run_batch_evaluation(verbose=args.verbose)
+    elif args.idea:
+        run_crew_evaluation(args.idea, verbose=args.verbose)
+    else:
+        # Default: run demo
+        print("Quantum Finance Feasibility Analysis Framework")
+        print("=" * 50)
+        print("\nUsage:")
+        print("  python main.py --demo          # Resource estimation demos")
+        print("  python main.py --idea 'text'   # Evaluate specific idea")
+        print("  python main.py --batch         # Batch evaluate test cases")
+        print("\nRunning demo mode...")
+        run_resource_estimation_demo()
+        run_classical_benchmark_demo()
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,23 @@
+# Core framework
+crewai>=1.7.0
+crewai-tools>=1.7.0
+
+# Quantum simulation
+qiskit>=1.0.0
+pennylane>=0.35.0
+
+# Finance data and analysis
+yfinance>=0.2.36
+pandas>=2.0.0
+numpy>=1.24.0
+scipy>=1.10.0
+
+# Visualization
+matplotlib>=3.7.0
+
+# Web interface
+gradio>=4.0.0
+
+# Utilities
+pyyaml>=6.0
+python-dotenv>=1.0.0

src/__init__.py

@@ -0,0 +1,8 @@
+"""
+Quantum Finance Feasibility Analysis Framework
+
+An agentic AI framework for evaluating the feasibility of near-term
+quantum computing applications in finance.
+"""
+
+__version__ = "0.1.0"

src/quantum_finance_crew/__init__.py

@@ -0,0 +1,11 @@
+"""
+Quantum Finance Feasibility Crew
+
+An agentic AI framework for evaluating the feasibility of near-term
+quantum computing applications in finance.
+"""
+
+from .crew import QuantumFinanceCrew
+
+__version__ = "0.1.0"
+__all__ = ["QuantumFinanceCrew"]

src/quantum_finance_crew/crew.py

@@ -0,0 +1,174 @@
+"""
+Main CrewAI implementation for quantum finance feasibility analysis.
+"""
+
+import yaml
+from pathlib import Path
+from crewai import Agent, Crew, Task, Process
+
+
+class QuantumFinanceCrew:
+    """
+    Multi-agent crew for evaluating quantum finance application feasibility.
+
+    This crew consists of four specialized agents:
+    - Quantum Algorithm Expert: Evaluates algorithm applicability and complexity
+    - Quantum Error Correction Expert: Assesses NISQ hardware constraints
+    - Classical Computing Expert: Benchmarks classical alternatives
+    - Finance Domain Expert: Evaluates business viability
+    """
+
+    def __init__(self, config_path: str = None):
+        """Initialize the crew with configuration."""
+        if config_path is None:
+            config_path = Path(__file__).parent.parent.parent / "config"
+        else:
+            config_path = Path(config_path)
+
+        self.agents_config = self._load_yaml(config_path / "agents.yaml")
+        self.tasks_config = self._load_yaml(config_path / "tasks.yaml")
+
+        self.agents = self._create_agents()
+        self.evaluation_history = []
+
+    def _load_yaml(self, path: Path) -> dict:
+        """Load YAML configuration file."""
+        with open(path, 'r') as f:
+            return yaml.safe_load(f)
+
+    def _create_agents(self) -> dict:
+        """Create all agents from configuration."""
+        agents = {}
+
+        for agent_name, config in self.agents_config.items():
+            agents[agent_name] = Agent(
+                role=config['role'],
+                goal=config['goal'],
+                backstory=config['backstory'],
+                verbose=config.get('verbose', True),
+                allow_delegation=config.get('allow_delegation', True)
+            )
+
+        return agents
+
+    def _create_tasks(self, idea: str) -> list:
+        """Create tasks for evaluating a specific idea."""
+        tasks = []
+        task_objects = {}
+
+        # Create tasks in order (context dependencies handled)
+        task_order = [
+            'analyze_quantum_feasibility',
+            'assess_nisq_constraints',
+            'benchmark_classical_alternatives',
+            'evaluate_business_viability',
+            'synthesize_final_assessment'
+        ]
+
+        for task_name in task_order:
+            config = self.tasks_config[task_name]
+            agent_name = config['agent']
+
+            # Build context from previous tasks if specified
+            context = []
+            if 'context' in config:
+                context = [task_objects[ctx] for ctx in config['context'] if ctx in task_objects]
+
+            task = Task(
+                description=config['description'].format(idea=idea),
+                expected_output=config['expected_output'],
+                agent=self.agents[agent_name],
+                context=context if context else None
+            )
+
+            task_objects[task_name] = task
+            tasks.append(task)
+
+        return tasks
+
+    def evaluate(self, idea: str, verbose: bool = True) -> dict:
+        """
+        Evaluate a quantum finance application idea.
+
+        Args:
+            idea: Description of the proposed quantum finance application
+            verbose: Whether to print detailed progress
+
+        Returns:
+            Dictionary containing the evaluation results and metrics
+        """
+        tasks = self._create_tasks(idea)
+
+        crew = Crew(
+            agents=list(self.agents.values()),
+            tasks=tasks,
+            process=Process.sequential,
+            verbose=verbose
+        )
+
+        result = crew.kickoff()
+
+        # Store evaluation in history
+        evaluation = {
+            'idea': idea,
+            'result': result,
+            'tasks_completed': len(tasks)
+        }
+        self.evaluation_history.append(evaluation)
+
+        return evaluation
+
+    def batch_evaluate(self, ideas: list, verbose: bool = False) -> list:
+        """
+        Evaluate multiple quantum finance application ideas.
+
+        Args:
+            ideas: List of idea descriptions to evaluate
+            verbose: Whether to print detailed progress
+
+        Returns:
+            List of evaluation results
+        """
+        results = []
+        for i, idea in enumerate(ideas):
+            print(f"\n{'='*60}")
+            print(f"Evaluating idea {i+1}/{len(ideas)}: {idea[:50]}...")
+            print('='*60)
+
+            result = self.evaluate(idea, verbose=verbose)
+            results.append(result)
+
+        return results
+
+    def get_evaluation_summary(self) -> dict:
+        """Get summary statistics of all evaluations."""
+        return {
+            'total_evaluations': len(self.evaluation_history),
+            'ideas_evaluated': [e['idea'] for e in self.evaluation_history]
+        }
+
+
+def main():
+    """Example usage of the QuantumFinanceCrew."""
+    crew = QuantumFinanceCrew()
+
+    # Example finance use cases to evaluate
+    test_ideas = [
+        "Quantum portfolio optimization using QAOA for a 100-asset portfolio",
+        "Quantum Monte Carlo for option pricing on European call options",
+        "Quantum machine learning for credit card fraud detection"
+    ]
+
+    # Evaluate first idea as demo
+    print("Quantum Finance Feasibility Analysis Framework")
+    print("=" * 50)
+    print(f"\nEvaluating: {test_ideas[0]}")
+
+    result = crew.evaluate(test_ideas[0])
+    print("\n" + "=" * 50)
+    print("EVALUATION COMPLETE")
+    print("=" * 50)
+
+
+if __name__ == "__main__":
+    main()

src/tools/__init__.py

@@ -0,0 +1,25 @@
+"""
+Tools for quantum finance feasibility analysis.
+"""
+
+from .quantum_tools import (
+    QuantumResourceEstimator,
+    NISQNoiseModel,
+    run_qaoa_simulation
+)
+from .finance_tools import (
+    FinanceDataCollector,
+    ClassicalPortfolioOptimizer,
+    OptionPricer,
+    collect_experiment_data
+)
+
+__all__ = [
+    'QuantumResourceEstimator',
+    'NISQNoiseModel',
+    'run_qaoa_simulation',
+    'FinanceDataCollector',
+    'ClassicalPortfolioOptimizer',
+    'OptionPricer',
+    'collect_experiment_data'
+]

src/tools/finance_tools.py

@@ -0,0 +1,365 @@
+"""
+Finance data tools using Yahoo Finance and analysis utilities.
+
+Provides utilities for:
+- Historical price data retrieval
+- Portfolio optimization benchmarks
+- Option pricing models
+- Risk metrics calculation
+"""
+
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+from typing import Optional
+
+
+class FinanceDataCollector:
+    """Collect and process financial data for quantum finance experiments."""
+
+    # Default benchmark assets
+    DEFAULT_TICKERS = [
+        'AAPL', 'MSFT', 'GOOGL', 'AMZN', 'META',  # Tech
+        'JPM', 'BAC', 'GS', 'MS', 'C',  # Finance
+        'JNJ', 'PFE', 'UNH', 'MRK', 'ABBV',  # Healthcare
+        'XOM', 'CVX', 'COP', 'SLB', 'EOG',  # Energy
+        'PG', 'KO', 'PEP', 'WMT', 'COST'  # Consumer
+    ]
+
+    def __init__(self, tickers: Optional[list] = None):
+        """Initialize with list of tickers to track."""
+        self.tickers = tickers or self.DEFAULT_TICKERS
+
+    def fetch_historical_data(
+        self,
+        start_date: str = None,
+        end_date: str = None,
+        period: str = "1y"
+    ) -> pd.DataFrame:
+        """
+        Fetch historical price data using yfinance.
+
+        Args:
+            start_date: Start date (YYYY-MM-DD format)
+            end_date: End date (YYYY-MM-DD format)
+            period: Alternative to start/end - e.g., "1y", "6mo", "1mo"
+
+        Returns:
+            DataFrame with adjusted close prices
+        """
+        try:
+            import yfinance as yf
+
+            if start_date and end_date:
+                data = yf.download(
+                    self.tickers,
+                    start=start_date,
+                    end=end_date,
+                    progress=False
+                )
+            else:
+                data = yf.download(
+                    self.tickers,
+                    period=period,
+                    progress=False
+                )
+
+            # Extract adjusted close prices
+            if len(data) == 0:
+                print("No data from yfinance, using synthetic data...")
+                return self._generate_synthetic_data()
+
+            if 'Adj Close' in data.columns.get_level_values(0):
+                prices = data['Adj Close']
+            else:
+                prices = data['Close']
+
+            # If prices are empty, use synthetic
+            if prices.empty or prices.isna().all().all():
+                print("Empty price data, using synthetic data...")
+                return self._generate_synthetic_data()
+
+            return prices
+
+        except (ImportError, Exception) as e:
+            print(f"yfinance error ({e}), using synthetic data...")
+            return self._generate_synthetic_data()
+
+    def _generate_synthetic_data(self, days: int = 252) -> pd.DataFrame:
+        """Generate synthetic price data for testing when yfinance unavailable."""
+        np.random.seed(42)
+        dates = pd.date_range(end=datetime.now(), periods=days, freq='D')
+
+        data = {}
+        for ticker in self.tickers:
+            # Geometric Brownian Motion simulation
+            initial_price = np.random.uniform(50, 500)
+            returns = np.random.normal(0.0005, 0.02, days)
+            prices = initial_price * np.cumprod(1 + returns)
+            data[ticker] = prices
+
+        return pd.DataFrame(data, index=dates)
+
+    def calculate_returns(self, prices: pd.DataFrame) -> pd.DataFrame:
+        """Calculate daily returns from price data."""
+        return prices.pct_change(fill_method=None).dropna()
+
+    def calculate_covariance_matrix(self, returns: pd.DataFrame) -> pd.DataFrame:
+        """Calculate annualized covariance matrix."""
+        return returns.cov() * 252  # Annualize
+
+    def calculate_expected_returns(self, returns: pd.DataFrame) -> pd.Series:
+        """Calculate annualized expected returns."""
+        return returns.mean() * 252  # Annualize
+
+
+class ClassicalPortfolioOptimizer:
+    """Classical portfolio optimization for benchmarking quantum approaches."""
+
+    def __init__(self, expected_returns: np.ndarray, covariance_matrix: np.ndarray):
+        """
+        Initialize optimizer.
+
+        Args:
+            expected_returns: Expected returns vector
+            covariance_matrix: Covariance matrix of returns
+        """
+        self.mu = expected_returns
+        self.sigma = covariance_matrix
+        self.n_assets = len(expected_returns)
+
+    def optimize_markowitz(self, target_return: float = None) -> dict:
+        """
+        Solve Markowitz mean-variance optimization.
+
+        Args:
+            target_return: Target portfolio return (if None, maximize Sharpe)
+
+        Returns:
+            Optimization results
+        """
+        try:
+            from scipy.optimize import minimize
+
+            def portfolio_volatility(weights):
+                return np.sqrt(weights @ self.sigma @ weights)
+
+            def negative_sharpe(weights):
+                ret = weights @ self.mu
+                vol = portfolio_volatility(weights)
+                return -ret / vol if vol > 0 else 0
+
+            # Constraints: weights sum to 1, all weights >= 0
+            constraints = [{'type': 'eq', 'fun': lambda w: np.sum(w) - 1}]
+            bounds = [(0, 1) for _ in range(self.n_assets)]
+
+            # Initial guess: equal weights
+            w0 = np.ones(self.n_assets) / self.n_assets
+
+            if target_return is not None:
+                constraints.append({
+                    'type': 'eq',
+                    'fun': lambda w: w @ self.mu - target_return
+                })
+                result = minimize(
+                    portfolio_volatility, w0,
+                    method='SLSQP', bounds=bounds, constraints=constraints
+                )
+            else:
+                result = minimize(
+                    negative_sharpe, w0,
+                    method='SLSQP', bounds=bounds, constraints=constraints
+                )
+
+            optimal_weights = result.x
+            portfolio_return = optimal_weights @ self.mu
+            portfolio_vol = portfolio_volatility(optimal_weights)
+            sharpe = portfolio_return / portfolio_vol if portfolio_vol > 0 else 0
+
+            return {
+                'weights': optimal_weights,
+                'expected_return': portfolio_return,
+                'volatility': portfolio_vol,
+                'sharpe_ratio': sharpe,
+                'optimization_success': result.success,
+                'solver': 'scipy.optimize.minimize (SLSQP)'
+            }
+
+        except ImportError:
+            return {'error': 'scipy not available'}
+
+    def benchmark_timing(self, n_trials: int = 100) -> dict:
+        """
+        Benchmark classical optimization timing.
+
+        Args:
+            n_trials: Number of optimization runs
+
+        Returns:
+            Timing statistics
+        """
+        import time
+
+        times = []
+        for _ in range(n_trials):
+            start = time.perf_counter()
+            self.optimize_markowitz()
+            times.append(time.perf_counter() - start)
+
+        return {
+            'n_assets': self.n_assets,
+            'n_trials': n_trials,
+            'mean_time_ms': np.mean(times) * 1000,
+            'std_time_ms': np.std(times) * 1000,
+            'min_time_ms': np.min(times) * 1000,
+            'max_time_ms': np.max(times) * 1000
+        }
+
+
+class OptionPricer:
+    """Classical option pricing for benchmarking quantum amplitude estimation."""
+
+    @staticmethod
+    def black_scholes_call(S: float, K: float, T: float, r: float, sigma: float) -> float:
+        """
+        Black-Scholes European call option price.
+
+        Args:
+            S: Current stock price
+            K: Strike price
+            T: Time to maturity (years)
+            r: Risk-free rate
+            sigma: Volatility
+
+        Returns:
+            Call option price
+        """
+        from scipy.stats import norm
+
+        d1 = (np.log(S/K) + (r + 0.5*sigma**2)*T) / (sigma*np.sqrt(T))
+        d2 = d1 - sigma*np.sqrt(T)
+
+        return S * norm.cdf(d1) - K * np.exp(-r*T) * norm.cdf(d2)
+
+    @staticmethod
+    def monte_carlo_call(
+        S: float, K: float, T: float, r: float, sigma: float,
+        n_paths: int = 100000
+    ) -> dict:
+        """
+        Monte Carlo European call option pricing.
+
+        Args:
+            S: Current stock price
+            K: Strike price
+            T: Time to maturity (years)
+            r: Risk-free rate
+            sigma: Volatility
+            n_paths: Number of simulation paths
+
+        Returns:
+            Price estimate with confidence interval
+        """
+        np.random.seed(42)
+
+        # Simulate terminal prices
+        Z = np.random.standard_normal(n_paths)
+        ST = S * np.exp((r - 0.5*sigma**2)*T + sigma*np.sqrt(T)*Z)
+
+        # Calculate payoffs
+        payoffs = np.maximum(ST - K, 0)
+        discounted = np.exp(-r*T) * payoffs
+
+        price = np.mean(discounted)
+        std_error = np.std(discounted) / np.sqrt(n_paths)
+
+        return {
+            'price': price,
+            'std_error': std_error,
+            'confidence_interval_95': (price - 1.96*std_error, price + 1.96*std_error),
+            'n_paths': n_paths
+        }
+
+    @staticmethod
+    def benchmark_monte_carlo_timing(
+        n_paths_list: list = None,
+        n_trials: int = 10
+    ) -> list:
+        """Benchmark Monte Carlo timing for different path counts."""
+        import time
+
+        if n_paths_list is None:
+            n_paths_list = [1000, 10000, 100000, 1000000]
+
+        results = []
+        for n_paths in n_paths_list:
+            times = []
+            for _ in range(n_trials):
+                start = time.perf_counter()
+                OptionPricer.monte_carlo_call(
+                    S=100, K=100, T=1, r=0.05, sigma=0.2,
+                    n_paths=n_paths
+                )
+                times.append(time.perf_counter() - start)
+
+            results.append({
+                'n_paths': n_paths,
+                'mean_time_ms': np.mean(times) * 1000,
+                'std_time_ms': np.std(times) * 1000
+            })
+
+        return results
+
+
+def collect_experiment_data(n_assets: int = 25) -> dict:
+    """
+    Collect data for quantum finance experiments.
+
+    Args:
+        n_assets: Number of assets to include
+
+    Returns:
+        Dictionary with prices, returns, and optimization inputs
+    """
+    collector = FinanceDataCollector(
+        tickers=FinanceDataCollector.DEFAULT_TICKERS[:n_assets]
+    )
+
+    print(f"Fetching data for {n_assets} assets...")
+    prices = collector.fetch_historical_data(period="1y")
+    returns = collector.calculate_returns(prices)
+    cov_matrix = collector.calculate_covariance_matrix(returns)
+    exp_returns = collector.calculate_expected_returns(returns)
+
+    return {
+        'prices': prices,
+        'returns': returns,
+        'covariance_matrix': cov_matrix,
+        'expected_returns': exp_returns,
+        'n_assets': n_assets,
+        'n_observations': len(returns)
+    }
+
+
+if __name__ == "__main__":
+    # Demo data collection and classical benchmarking
+    print("Finance Data Collection Demo")
+    print("=" * 50)
+
+    data = collect_experiment_data(n_assets=10)
+    print(f"Collected {data['n_observations']} days of data for {data['n_assets']} assets")
+
+    print("\nClassical Portfolio Optimization:")
+    print("-" * 50)
+    optimizer = ClassicalPortfolioOptimizer(
+        data['expected_returns'].values,
+        data['covariance_matrix'].values
+    )
+    result = optimizer.optimize_markowitz()
+    print(f"Expected Return: {result['expected_return']:.4f}")
+    print(f"Volatility: {result['volatility']:.4f}")
+    print(f"Sharpe Ratio: {result['sharpe_ratio']:.4f}")
+
+    print("\nOptimization Timing Benchmark:")
+    timing = optimizer.benchmark_timing(n_trials=100)
+    print(f"Mean time: {timing['mean_time_ms']:.3f} ms")

src/tools/quantum_tools.py

@@ -0,0 +1,365 @@
+"""
+Quantum simulation tools using Qiskit and PennyLane.
+
+Provides utilities for:
+- QAOA circuit simulation
+- VQE implementations
+- Noise modeling for NISQ assessment
+- Resource estimation
+"""
+
+import numpy as np
+from typing import Optional
+
+
+class QuantumResourceEstimator:
+    """Estimate quantum resources required for finance applications."""
+
+    # Current NISQ hardware constraints (2024-2026 estimates)
+    HARDWARE_SPECS = {
+        'ibm_osprey': {
+            'qubits': 433,
+            'gate_fidelity_1q': 0.9996,
+            'gate_fidelity_2q': 0.99,
+            'coherence_time_us': 100,
+            'gate_time_1q_ns': 35,
+            'gate_time_2q_ns': 300,
+        },
+        'ibm_condor': {
+            'qubits': 1121,
+            'gate_fidelity_1q': 0.9995,
+            'gate_fidelity_2q': 0.985,
+            'coherence_time_us': 80,
+            'gate_time_1q_ns': 35,
+            'gate_time_2q_ns': 300,
+        },
+        'ionq_forte': {
+            'qubits': 36,
+            'gate_fidelity_1q': 0.9999,
+            'gate_fidelity_2q': 0.995,
+            'coherence_time_us': 10000000,  # Ion traps have very long coherence
+            'gate_time_1q_ns': 10000,
+            'gate_time_2q_ns': 200000,
+        },
+        'google_sycamore': {
+            'qubits': 72,
+            'gate_fidelity_1q': 0.9985,
+            'gate_fidelity_2q': 0.995,
+            'coherence_time_us': 20,
+            'gate_time_1q_ns': 25,
+            'gate_time_2q_ns': 32,
+        }
+    }
+
+    def __init__(self, hardware: str = 'ibm_osprey'):
+        """Initialize with target hardware specifications."""
+        if hardware not in self.HARDWARE_SPECS:
+            raise ValueError(f"Unknown hardware: {hardware}. Choose from {list(self.HARDWARE_SPECS.keys())}")
+        self.hardware = hardware
+        self.specs = self.HARDWARE_SPECS[hardware]
+
+    def estimate_qaoa_resources(self, num_assets: int, p_layers: int = 1) -> dict:
+        """
+        Estimate resources for QAOA portfolio optimization.
+
+        Args:
+            num_assets: Number of assets in portfolio
+            p_layers: Number of QAOA layers (depth parameter)
+
+        Returns:
+            Dictionary with resource estimates
+        """
+        # QAOA for portfolio optimization typically requires n qubits for n assets
+        qubits_required = num_assets
+
+        # Gates per layer: roughly O(n^2) for cost Hamiltonian + O(n) for mixer
+        two_qubit_gates_per_layer = num_assets * (num_assets - 1) // 2
+        one_qubit_gates_per_layer = num_assets * 2
+
+        total_2q_gates = two_qubit_gates_per_layer * p_layers
+        total_1q_gates = one_qubit_gates_per_layer * p_layers
+
+        # Circuit depth estimate
+        circuit_depth = p_layers * (num_assets + 2)
+
+        # Total execution time
+        exec_time_ns = (total_1q_gates * self.specs['gate_time_1q_ns'] +
+                        total_2q_gates * self.specs['gate_time_2q_ns'])
+        exec_time_us = exec_time_ns / 1000
+
+        # Error probability estimate
+        success_prob = (self.specs['gate_fidelity_1q'] ** total_1q_gates *
+                       self.specs['gate_fidelity_2q'] ** total_2q_gates)
+
+        # Feasibility check
+        feasible = (
+            qubits_required <= self.specs['qubits'] and
+            exec_time_us < self.specs['coherence_time_us'] and
+            success_prob > 0.01  # At least 1% success probability
+        )
+
+        return {
+            'qubits_required': qubits_required,
+            'qubits_available': self.specs['qubits'],
+            'total_1q_gates': total_1q_gates,
+            'total_2q_gates': total_2q_gates,
+            'circuit_depth': circuit_depth,
+            'execution_time_us': exec_time_us,
+            'coherence_time_us': self.specs['coherence_time_us'],
+            'success_probability': success_prob,
+            'feasible_on_hardware': feasible,
+            'bottleneck': self._identify_bottleneck(
+                qubits_required, exec_time_us, success_prob
+            )
+        }
+
+    def estimate_amplitude_estimation_resources(
+        self,
+        precision_bits: int,
+        num_qubits_oracle: int
+    ) -> dict:
+        """
+        Estimate resources for quantum amplitude estimation (option pricing).
+
+        Args:
+            precision_bits: Number of bits of precision required
+            num_qubits_oracle: Qubits needed for the oracle (problem encoding)
+
+        Returns:
+            Dictionary with resource estimates
+        """
+        # Total qubits: oracle + precision register
+        qubits_required = num_qubits_oracle + precision_bits
+
+        # Amplitude estimation requires O(2^precision) oracle calls
+        oracle_calls = 2 ** precision_bits
+
+        # Assume oracle has O(n^2) gates
+        gates_per_oracle = num_qubits_oracle ** 2
+        total_2q_gates = oracle_calls * gates_per_oracle
+
+        # Execution time
+        exec_time_us = (total_2q_gates * self.specs['gate_time_2q_ns']) / 1000
+
+        # Success probability
+        success_prob = self.specs['gate_fidelity_2q'] ** total_2q_gates
+
+        feasible = (
+            qubits_required <= self.specs['qubits'] and
+            exec_time_us < self.specs['coherence_time_us'] * 0.5 and
+            success_prob > 0.001
+        )
+
+        return {
+            'qubits_required': qubits_required,
+            'qubits_available': self.specs['qubits'],
+            'oracle_calls': oracle_calls,
+            'total_2q_gates': total_2q_gates,
+            'execution_time_us': exec_time_us,
+            'coherence_time_us': self.specs['coherence_time_us'],
+            'success_probability': success_prob,
+            'feasible_on_hardware': feasible,
+            'bottleneck': self._identify_bottleneck(
+                qubits_required, exec_time_us, success_prob
+            )
+        }
+
+    def estimate_grover_resources(self, search_space_size: int) -> dict:
+        """
+        Estimate resources for Grover's search (fraud detection).
+
+        Args:
+            search_space_size: Size of search space N
+
+        Returns:
+            Dictionary with resource estimates
+        """
+        # Qubits: log2(N) for encoding
+        qubits_required = int(np.ceil(np.log2(search_space_size)))
+
+        # Grover iterations: O(sqrt(N))
+        iterations = int(np.ceil(np.sqrt(search_space_size) * np.pi / 4))
+
+        # Gates per iteration: O(n) for oracle + O(n) for diffusion
+        gates_per_iteration = qubits_required * 4
+        total_2q_gates = iterations * gates_per_iteration
+
+        exec_time_us = (total_2q_gates * self.specs['gate_time_2q_ns']) / 1000
+        success_prob = self.specs['gate_fidelity_2q'] ** total_2q_gates
+
+        feasible = (
+            qubits_required <= self.specs['qubits'] and
+            exec_time_us < self.specs['coherence_time_us'] and
+            success_prob > 0.01
+        )
+
+        return {
+            'qubits_required': qubits_required,
+            'qubits_available': self.specs['qubits'],
+            'grover_iterations': iterations,
+            'total_2q_gates': total_2q_gates,
+            'execution_time_us': exec_time_us,
+            'coherence_time_us': self.specs['coherence_time_us'],
+            'success_probability': success_prob,
+            'feasible_on_hardware': feasible,
+            'classical_speedup': f"O(sqrt(N)) vs O(N)",
+            'bottleneck': self._identify_bottleneck(
+                qubits_required, exec_time_us, success_prob
+            )
+        }
+
+    def _identify_bottleneck(
+        self,
+        qubits_required: int,
+        exec_time_us: float,
+        success_prob: float
+    ) -> str:
+        """Identify the primary bottleneck for feasibility."""
+        bottlenecks = []
+
+        if qubits_required > self.specs['qubits']:
+            bottlenecks.append(f"Qubit count ({qubits_required} > {self.specs['qubits']})")
+
+        if exec_time_us > self.specs['coherence_time_us']:
+            ratio = exec_time_us / self.specs['coherence_time_us']
+            bottlenecks.append(f"Coherence time (circuit {ratio:.1f}x longer than coherence)")
+
+        if success_prob < 0.01:
+            bottlenecks.append(f"Gate errors (success prob {success_prob:.2e})")
+
+        return "; ".join(bottlenecks) if bottlenecks else "None - appears feasible"
+
+
+class NISQNoiseModel:
+    """Model noise effects on NISQ quantum circuits."""
+
+    def __init__(self, depolarizing_rate: float = 0.01, measurement_error: float = 0.02):
+        """
+        Initialize noise model.
+
+        Args:
+            depolarizing_rate: Single-qubit depolarizing error rate
+            measurement_error: Measurement error probability
+        """
+        self.depolarizing_rate = depolarizing_rate
+        self.measurement_error = measurement_error
+
+    def estimate_output_fidelity(self, num_gates: int, num_qubits: int) -> float:
+        """
+        Estimate output state fidelity after noisy circuit execution.
+
+        Args:
+            num_gates: Total number of gates in circuit
+            num_qubits: Number of qubits
+
+        Returns:
+            Estimated fidelity (0 to 1)
+        """
+        # Simplified noise model: each gate reduces fidelity
+        gate_fidelity = (1 - self.depolarizing_rate) ** num_gates
+
+        # Measurement errors
+        meas_fidelity = (1 - self.measurement_error) ** num_qubits
+
+        return gate_fidelity * meas_fidelity
+
+    def required_shots_for_precision(
+        self,
+        target_precision: float,
+        success_probability: float
+    ) -> int:
+        """
+        Calculate required measurement shots for target precision.
+
+        Args:
+            target_precision: Desired precision (e.g., 0.01 for 1%)
+            success_probability: Probability of successful circuit execution
+
+        Returns:
+            Number of shots required
+        """
+        # Using Hoeffding bound: shots >= 1/(2 * precision^2 * success_prob)
+        if success_probability < 1e-10:
+            return float('inf')
+
+        shots = int(np.ceil(1 / (2 * target_precision**2 * success_probability)))
+        return min(shots, 10**9)  # Cap at 1 billion shots
+
+
+def run_qaoa_simulation(num_assets: int = 10, p_layers: int = 1) -> dict:
+    """
+    Run a simplified QAOA simulation for portfolio optimization.
+
+    This is a demonstration of the simulation capability.
+    Full implementation would use Qiskit or PennyLane.
+
+    Args:
+        num_assets: Number of assets
+        p_layers: QAOA depth
+
+    Returns:
+        Simulation results
+    """
+    try:
+        import pennylane as qml
+
+        # Create a simple QAOA-style circuit
+        dev = qml.device('default.qubit', wires=min(num_assets, 10))
+
+        @qml.qnode(dev)
+        def qaoa_circuit(gamma, beta):
+            # Initial superposition
+            for i in range(min(num_assets, 10)):
+                qml.Hadamard(wires=i)
+
+            # Simplified cost layer
+            for i in range(min(num_assets, 10) - 1):
+                qml.CNOT(wires=[i, i+1])
+                qml.RZ(gamma, wires=i+1)
+                qml.CNOT(wires=[i, i+1])
+
+            # Mixer layer
+            for i in range(min(num_assets, 10)):
+                qml.RX(beta, wires=i)
+
+            return qml.expval(qml.PauliZ(0))
+
+        # Run with sample parameters
+        result = qaoa_circuit(0.5, 0.3)
+
+        return {
+            'status': 'success',
+            'expectation_value': float(result),
+            'num_qubits_used': min(num_assets, 10),
+            'simulator': 'pennylane.default.qubit'
+        }
+
+    except ImportError:
+        return {
+            'status': 'pennylane_not_available',
+            'message': 'PennyLane not installed. Install with: pip install pennylane'
+        }
+    except Exception as e:
+        return {
+            'status': 'error',
+            'message': str(e)
+        }
+
+
+if __name__ == "__main__":
+    # Demo resource estimation
+    estimator = QuantumResourceEstimator('ibm_osprey')
+
+    print("QAOA Resource Estimation for 50-asset portfolio:")
+    print("-" * 50)
+    result = estimator.estimate_qaoa_resources(num_assets=50, p_layers=3)
+    for key, value in result.items():
+        print(f"  {key}: {value}")
+
+    print("\nAmplitude Estimation for Option Pricing (8-bit precision):")
+    print("-" * 50)
+    result = estimator.estimate_amplitude_estimation_resources(
+        precision_bits=8, num_qubits_oracle=20
+    )
+    for key, value in result.items():
+        print(f"  {key}: {value}")

src/utils/__init__.py

@@ -0,0 +1,21 @@
+"""
+Utility modules for quantum finance framework.
+"""
+
+from .evaluation import (
+    EvaluationMetrics,
+    MetricsCollector,
+    HallucinationChecker,
+    calculate_expert_alignment,
+    extract_scores_from_text,
+    LITERATURE_CONSENSUS
+)
+
+__all__ = [
+    'EvaluationMetrics',
+    'MetricsCollector',
+    'HallucinationChecker',
+    'calculate_expert_alignment',
+    'extract_scores_from_text',
+    'LITERATURE_CONSENSUS'
+]

src/utils/evaluation.py

@@ -0,0 +1,377 @@
+"""
+Evaluation metrics for the quantum finance feasibility framework.
+
+Metrics defined per plan.md:
+1. Expert Alignment: Agreement with literature/expert consensus
+2. Hallucination Rate: Factual accuracy of agent outputs
+3. Computational Efficiency: Agent rounds and time per evaluation
+"""
+
+import re
+import time
+from dataclasses import dataclass, field
+from typing import Optional
+from datetime import datetime
+
+
+@dataclass
+class EvaluationMetrics:
+    """Container for evaluation metrics of a single run."""
+    idea: str
+    start_time: datetime = field(default_factory=datetime.now)
+    end_time: Optional[datetime] = None
+
+    # Computational efficiency metrics
+    total_agent_rounds: int = 0
+    total_tokens_used: int = 0
+    wall_clock_time_seconds: float = 0.0
+
+    # Quality metrics (to be assessed manually or via comparison)
+    expert_alignment_score: Optional[float] = None  # 0-1 scale
+    hallucination_count: int = 0
+    factual_claims_count: int = 0
+
+    # Output scores from agents
+    quantum_feasibility_score: Optional[float] = None
+    hardware_readiness_score: Optional[float] = None
+    classical_competitiveness_score: Optional[float] = None
+    business_viability_score: Optional[float] = None
+    overall_score: Optional[float] = None
+
+    def complete(self):
+        """Mark evaluation as complete and calculate duration."""
+        self.end_time = datetime.now()
+        self.wall_clock_time_seconds = (self.end_time - self.start_time).total_seconds()
+
+    @property
+    def hallucination_rate(self) -> Optional[float]:
+        """Calculate hallucination rate as fraction of factual claims."""
+        if self.factual_claims_count == 0:
+            return None
+        return self.hallucination_count / self.factual_claims_count
+
+    def to_dict(self) -> dict:
+        """Convert to dictionary for serialization."""
+        return {
+            'idea': self.idea,
+            'start_time': self.start_time.isoformat(),
+            'end_time': self.end_time.isoformat() if self.end_time else None,
+            'total_agent_rounds': self.total_agent_rounds,
+            'total_tokens_used': self.total_tokens_used,
+            'wall_clock_time_seconds': self.wall_clock_time_seconds,
+            'expert_alignment_score': self.expert_alignment_score,
+            'hallucination_rate': self.hallucination_rate,
+            'quantum_feasibility_score': self.quantum_feasibility_score,
+            'hardware_readiness_score': self.hardware_readiness_score,
+            'classical_competitiveness_score': self.classical_competitiveness_score,
+            'business_viability_score': self.business_viability_score,
+            'overall_score': self.overall_score
+        }
+
+
+class MetricsCollector:
+    """Collect and aggregate metrics across multiple evaluations."""
+
+    def __init__(self):
+        self.evaluations: list[EvaluationMetrics] = []
+
+    def start_evaluation(self, idea: str) -> EvaluationMetrics:
+        """Start tracking a new evaluation."""
+        metrics = EvaluationMetrics(idea=idea)
+        self.evaluations.append(metrics)
+        return metrics
+
+    def get_summary_statistics(self) -> dict:
+        """Calculate summary statistics across all evaluations."""
+        if not self.evaluations:
+            return {'error': 'No evaluations recorded'}
+
+        completed = [e for e in self.evaluations if e.end_time is not None]
+
+        if not completed:
+            return {'error': 'No completed evaluations'}
+
+        times = [e.wall_clock_time_seconds for e in completed]
+        rounds = [e.total_agent_rounds for e in completed]
+
+        alignment_scores = [e.expert_alignment_score for e in completed
+                          if e.expert_alignment_score is not None]
+        overall_scores = [e.overall_score for e in completed
+                         if e.overall_score is not None]
+
+        import numpy as np
+
+        return {
+            'total_evaluations': len(completed),
+            'timing': {
+                'mean_seconds': np.mean(times),
+                'std_seconds': np.std(times),
+                'min_seconds': np.min(times),
+                'max_seconds': np.max(times)
+            },
+            'agent_rounds': {
+                'mean': np.mean(rounds),
+                'std': np.std(rounds),
+                'min': np.min(rounds),
+                'max': np.max(rounds)
+            },
+            'expert_alignment': {
+                'mean': np.mean(alignment_scores) if alignment_scores else None,
+                'n_rated': len(alignment_scores)
+            },
+            'overall_scores': {
+                'mean': np.mean(overall_scores) if overall_scores else None,
+                'std': np.std(overall_scores) if overall_scores else None
+            }
+        }
+
+
+# Ground truth for validation against literature consensus
+LITERATURE_CONSENSUS = {
+    'portfolio_optimization_qaoa': {
+        'quantum_advantage_near_term': False,
+        'feasible_on_nisq': True,  # Can run, but no advantage
+        'bottleneck': 'noise_and_classical_competition',
+        'recommended_approach': 'hybrid',
+        'references': [
+            'Egger et al. (2020) - Quantum Computing for Finance',
+            'Herman et al. (2022) - Portfolio Optimization Survey'
+        ]
+    },
+    'option_pricing_amplitude_estimation': {
+        'quantum_advantage_near_term': False,
+        'feasible_on_nisq': False,  # Requires error correction
+        'bottleneck': 'circuit_depth_and_error_correction',
+        'recommended_approach': 'classical_monte_carlo',
+        'references': [
+            'Stamatopoulos et al. (2020) - Option Pricing using QC',
+            'Chakrabarti et al. (2021) - Threshold for Quantum Speedup'
+        ]
+    },
+    'fraud_detection_quantum_ml': {
+        'quantum_advantage_near_term': False,
+        'feasible_on_nisq': True,  # Can run, limited scale
+        'bottleneck': 'data_loading_and_classical_ml_strength',
+        'recommended_approach': 'classical_ml',
+        'references': [
+            'Schuld & Petruccione (2021) - ML with Quantum Computers',
+            'Tang (2019) - Quantum-inspired classical algorithms'
+        ]
+    },
+    'risk_analysis_quantum_monte_carlo': {
+        'quantum_advantage_near_term': False,
+        'feasible_on_nisq': False,
+        'bottleneck': 'quadratic_speedup_insufficient_with_noise',
+        'recommended_approach': 'gpu_accelerated_classical',
+        'references': [
+            'Woerner & Egger (2019) - Quantum Risk Analysis',
+            'Miyamoto & Shiohara (2022) - Bermudan Option Pricing'
+        ]
+    },
+    'credit_scoring_vqc': {
+        'quantum_advantage_near_term': False,
+        'feasible_on_nisq': True,
+        'bottleneck': 'barren_plateaus_and_expressibility',
+        'recommended_approach': 'classical_ensemble_methods',
+        'references': [
+            'McClean et al. (2018) - Barren Plateaus',
+            'Cerezo et al. (2021) - Variational Quantum Algorithms'
+        ]
+    }
+}
+
+
+def calculate_expert_alignment(
+    agent_output: dict,
+    use_case_key: str
+) -> dict:
+    """
+    Calculate alignment between agent output and literature consensus.
+
+    Args:
+        agent_output: Structured output from the agent crew
+        use_case_key: Key into LITERATURE_CONSENSUS dict
+
+    Returns:
+        Alignment analysis with score
+    """
+    if use_case_key not in LITERATURE_CONSENSUS:
+        return {
+            'error': f'Unknown use case: {use_case_key}',
+            'available_cases': list(LITERATURE_CONSENSUS.keys())
+        }
+
+    consensus = LITERATURE_CONSENSUS[use_case_key]
+    alignment_checks = []
+
+    # Check quantum advantage assessment
+    if 'quantum_advantage' in agent_output:
+        agent_says_advantage = agent_output.get('quantum_advantage', False)
+        consensus_advantage = consensus['quantum_advantage_near_term']
+        alignment_checks.append({
+            'criterion': 'quantum_advantage_assessment',
+            'agent': agent_says_advantage,
+            'consensus': consensus_advantage,
+            'aligned': agent_says_advantage == consensus_advantage
+        })
+
+    # Check feasibility assessment
+    if 'feasible_on_nisq' in agent_output:
+        agent_feasible = agent_output.get('feasible_on_nisq', False)
+        consensus_feasible = consensus['feasible_on_nisq']
+        alignment_checks.append({
+            'criterion': 'nisq_feasibility',
+            'agent': agent_feasible,
+            'consensus': consensus_feasible,
+            'aligned': agent_feasible == consensus_feasible
+        })
+
+    # Calculate alignment score
+    if alignment_checks:
+        aligned_count = sum(1 for c in alignment_checks if c['aligned'])
+        alignment_score = aligned_count / len(alignment_checks)
+    else:
+        alignment_score = None
+
+    return {
+        'use_case': use_case_key,
+        'alignment_checks': alignment_checks,
+        'alignment_score': alignment_score,
+        'consensus_references': consensus['references']
+    }
+
+
+def extract_scores_from_text(text: str) -> dict:
+    """
+    Extract numerical scores from agent output text.
+
+    Looks for patterns like:
+    - "score: 0.7"
+    - "feasibility score (0-1): 0.65"
+    - "Rating: 7/10"
+    """
+    scores = {}
+
+    # Pattern: "X score: 0.Y" or "X score (0-1): 0.Y"
+    score_pattern = r'(\w+(?:\s+\w+)?)\s+score\s*(?:\([^)]+\))?\s*[:=]\s*([\d.]+)'
+    for match in re.finditer(score_pattern, text, re.IGNORECASE):
+        label = match.group(1).lower().replace(' ', '_')
+        value = float(match.group(2))
+        if 0 <= value <= 1:
+            scores[f'{label}_score'] = value
+        elif 0 <= value <= 10:
+            scores[f'{label}_score'] = value / 10
+
+    # Pattern: "Rating: X/10"
+    rating_pattern = r'rating\s*[:=]\s*([\d.]+)\s*/\s*10'
+    for match in re.finditer(rating_pattern, text, re.IGNORECASE):
+        value = float(match.group(1))
+        scores['rating'] = value / 10
+
+    return scores
+
+
+class HallucinationChecker:
+    """
+    Check agent outputs for potential hallucinations.
+
+    Compares claims against known facts about quantum computing
+    and finance applications.
+    """
+
+    # Known facts for validation
+    KNOWN_FACTS = {
+        # Hardware facts (2024-2026)
+        'ibm_qubit_count': (433, 1121),  # Range: Osprey to Condor
+        'ionq_qubit_count': (32, 36),
+        'google_qubit_count': (72, 100),
+
+        # Algorithm facts
+        'grover_speedup': 'quadratic',  # sqrt(N)
+        'shor_speedup': 'exponential',
+        'qaoa_proven_advantage': False,
+        'vqe_proven_advantage': False,
+
+        # Finance facts
+        'hft_latency_requirement_us': 1,  # microseconds
+        'typical_portfolio_size': (10, 10000),
+    }
+
+    HALLUCINATION_PATTERNS = [
+        # Claims of proven quantum advantage for NISQ
+        r'proven\s+quantum\s+advantage\s+(?:for|in|on)\s+(?:NISQ|near-term)',
+        r'demonstrat(?:ed|es)\s+quantum\s+supremacy\s+(?:for|in)\s+finance',
+
+        # Unrealistic hardware claims
+        r'(?:current|available)\s+(?:quantum\s+)?computers?\s+(?:with|have)\s+(?:\d{4,}|\d+,\d{3,})\s+qubits',
+
+        # Impossible speedup claims
+        r'exponential\s+speedup\s+(?:for|in|using)\s+(?:QAOA|VQE)',
+    ]
+
+    def check_output(self, text: str) -> dict:
+        """
+        Check text for potential hallucinations.
+
+        Args:
+            text: Agent output text to check
+
+        Returns:
+            Dictionary with hallucination analysis
+        """
+        findings = []
+
+        # Check against hallucination patterns
+        for pattern in self.HALLUCINATION_PATTERNS:
+            matches = re.findall(pattern, text, re.IGNORECASE)
+            if matches:
+                findings.append({
+                    'type': 'suspicious_claim',
+                    'pattern': pattern,
+                    'matches': matches
+                })
+
+        # Check qubit count claims
+        qubit_claims = re.findall(r'(\d+)\s*qubits?', text, re.IGNORECASE)
+        for claim in qubit_claims:
+            count = int(claim)
+            if count > 2000:  # No current hardware has this many
+                findings.append({
+                    'type': 'unrealistic_qubit_count',
+                    'claimed': count,
+                    'realistic_max': 1121
+                })
+
+        return {
+            'potential_hallucinations': len(findings),
+            'findings': findings,
+            'text_length': len(text)
+        }
+
+
+if __name__ == "__main__":
+    # Demo metrics collection
+    collector = MetricsCollector()
+
+    # Simulate an evaluation
+    metrics = collector.start_evaluation(
+        "Quantum portfolio optimization using QAOA"
+    )
+    metrics.total_agent_rounds = 5
+    metrics.quantum_feasibility_score = 0.6
+    metrics.hardware_readiness_score = 0.4
+    metrics.classical_competitiveness_score = 0.8
+    metrics.business_viability_score = 0.5
+    metrics.overall_score = 0.3
+    metrics.expert_alignment_score = 0.85
+
+    import time
+    time.sleep(0.1)  # Simulate some work
+    metrics.complete()
+
+    print("Evaluation Metrics Demo")
+    print("=" * 50)
+    print(metrics.to_dict())
+
+    print("\nSummary Statistics:")
+    print(collector.get_summary_statistics())