src/tools/quantum_tools.py

"""
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}")