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.gitignore

@@ -0,0 +1,42 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Testing
+.coverage
+htmlcov/
+.tox/
+.pytest_cache/
+
+# Distribution
+dist/
+build/

README.md

@@ -1,3 +1,92 @@
----
-license: cc-by-nc-4.0
----
+---
+library_name: quantumpeer
+license: cc-by-nc-4.0
+language:
+- en
+tags:
+- quantum-llm
+- quantum-computing
+- openpeerllm
+- chern-simons
+- neural-networks  
+- pytorch
+- causal-lm
+- decentralized-learning
+- transformer
+- boinc
+- decent-torch
+- lonscript
+pipeline_tag: text-generation
+datasets:
+- OpenPeerAI/OpenPeerLLM
+model-index:
+  - name: OpenPeerLLM
+    results:
+      - task: 
+          name: Language Modeling
+          type: text-generation
+        dataset:
+          name: Custom Text Dataset
+          type: text
+        metrics:
+          - name: Epoch
+            type: number
+            value: 2
+          - name: Model Size
+            type: text
+            value: "1.82 GB"
+          - name: Run Time
+            type: text
+            value: "2.5 minutes on Intel UHD Graphics 630"
+          - name: Loss
+            type: cross-entropy
+            value: 7.11
+---
+
+# QuantumPeer: Quantum-Enhanced OpenPeerLLM
+
+## Model Description
+
+QuantumPeer implements a novel approach to language model execution by combining OpenPeerLLM with quantum circuit simulation inspired by the Chern-Simons theory. This hybrid approach enables unique quantum-classical interactions in natural language processing.
+
+## Intended Uses
+
+- Research in quantum-enhanced language models
+- Development of hybrid quantum-classical AI systems
+- Educational purposes in quantum computing
+- Natural language processing with quantum inspiration
+
+## Training Procedure
+
+The model utilizes:
+- Base Model: OpenPeerLLM
+- Quantum Circuit: Custom implementation with Chern-Simons topology
+- Integration: Quantum state influence on attention mechanisms
+
+## Technical Specifications
+
+- **Framework:** PyTorch + Custom Quantum Simulator
+- **Parameters:** Based on OpenPeerLLM architecture
+- **Input Format:** Text prompts
+- **Output Format:** Generated text with quantum enhancement
+- **Model Architecture:** Hybrid quantum-classical
+
+## Limitations & Biases
+
+- Simulation-based quantum computing (not real quantum hardware)
+- Performance dependent on classical computing resources
+- Inherits any limitations from base OpenPeerLLM model
+
+## Out-of-Scope Uses
+
+- Production-critical applications
+- Safety-critical systems
+- Applications requiring true quantum hardware
+
+## Additional Information
+
+**License:** CC-BY-NC-4.0/CC-BY-NC-SA - All rights reserved
+**Creators:** 
+- OpenPeerAI
+- Andrew Magdy Kamal Nassief
+- Riemann Computing

circuit_visualizer.py

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+import numpy as np
+from quantum_circuit import QuantumCircuit
+from quantum_topology import ChernSimonsTopology
+
+class CircuitVisualizer:
+    def __init__(self, circuit: QuantumCircuit):
+        self.circuit = circuit
+        
+    def draw_circuit(self) -> str:
+        """Generate ASCII visualization of quantum circuit"""
+        output = []
+        output.append("Quantum Circuit:")
+        output.append("-" * 40)
+        
+        for i, gate in enumerate(self.circuit.gates):
+            output.append(f"Gate {i}: {gate.gate_type}")
+            if gate.gate_type == "CNOT":
+                output.append("  |control⟩ ──●──")
+                output.append("            │")
+                output.append("  |target⟩  ─⊕─")
+            else:
+                output.append(f"  |ψ⟩ ──{gate.gate_type}──")
+            output.append("")
+            
+        return "\n".join(output)
+        
+    def draw_topology(self) -> str:
+        """Generate ASCII visualization of topology"""
+        output = []
+        output.append("Topology Layout:")
+        output.append("-" * 40)
+        
+        for i in range(self.circuit.topology.depth):
+            connections = [j for j in range(self.circuit.topology.depth) 
+                         if (i,j) in self.circuit.topology.connections]
+            line = [f"Q{i}"]
+            for j in range(self.circuit.topology.depth):
+                if j in connections:
+                    line.append("──●──")
+                else:
+                    line.append("─────")
+            output.append("".join(line))
+            
+        return "\n".join(output)
+        
+    def get_state_visualization(self, state: np.ndarray) -> str:
+        """Visualize quantum state"""
+        output = []
+        output.append("Quantum State:")
+        output.append("-" * 40)
+        
+        # Show amplitudes and probabilities
+        for i, amplitude in enumerate(state):
+            prob = np.abs(amplitude) ** 2
+            binary = format(i, f'0{self.circuit.topology.depth}b')
+            output.append(f"|{binary}⟩: {amplitude:.3f} (Prob: {prob:.3f})")
+            
+        return "\n".join(output)

llm_interface.py

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+from typing import Optional
+import numpy as np
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+class OpenPeerLLMInterface:
+    def __init__(
+        self,
+        model_path: str,
+        checkpoint: str,
+        device: str
+    ):
+        self.device = device
+        self.model_path = f"{model_path}/{checkpoint}"
+        self.model = None
+        self.tokenizer = None
+        self._load_model()
+        
+    def _load_model(self):
+        """Load the model and tokenizer"""
+        try:
+            self.model = AutoModelForCausalLM.from_pretrained(
+                self.model_path
+            ).to(self.device)
+            self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
+        except Exception as e:
+            print(f"Error loading model: {e}")
+            raise
+        
+    def generate(
+        self,
+        prompt: str,
+        quantum_state: Optional[np.ndarray] = None,
+        max_length: int = 100
+    ) -> str:
+        if self.model is None or self.tokenizer is None:
+            raise RuntimeError("Model not properly initialized")
+            
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
+        
+        if quantum_state is not None:
+            self._apply_quantum_state(quantum_state)
+            
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_length=max_length,
+                num_return_sequences=1,
+                pad_token_id=self.tokenizer.eos_token_id
+            )
+        
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+    def _apply_quantum_state(self, quantum_state: np.ndarray):
+        if self.model is None:
+            return
+            
+        state_magnitude = np.abs(quantum_state) ** 2
+        attention_modifier = torch.tensor(
+            state_magnitude, 
+            device=self.device
+        ).float()
+        
+        with torch.no_grad():
+            for layer in self.model.transformer.h[:1]:
+                if hasattr(layer, 'attn'):
+                    attention = layer.attn
+                    if hasattr(attention, 'c_attn'):
+                        weights = attention.c_attn.weight
+                        scale = attention_modifier[:weights.size(0)].reshape(-1, 1)
+                        attention.c_attn.weight.data *= scale

quantum_circuit.py

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+import numpy as np
+from typing import Optional, Dict, Any, List
+from quantum_topology import ChernSimonsTopology
+from quantum_gates import QuantumGate
+
+class QuantumCircuit:
+    def __init__(self, topology: ChernSimonsTopology):
+        self.topology = topology
+        self.gates: List[QuantumGate] = []
+        self.initialize_gates()
+        
+    def initialize_gates(self):
+        """Initialize quantum gates based on topology"""
+        self.gates = [
+            QuantumGate("H"),
+            QuantumGate("CNOT"),
+            QuantumGate("Phase"),
+            QuantumGate("X"),
+            QuantumGate("Z")
+        ]
+        
+    def prepare_input(self, data: str) -> np.ndarray:
+        """Convert classical input to quantum state"""
+        state = np.zeros(self.topology.dimension, dtype=np.complex128)
+        state[0] = 1.0  # Initialize to |0...0⟩
+        
+        for i, char in enumerate(data):
+            if i >= self.topology.depth:
+                break
+            if ord(char) % 2:
+                h_gate = QuantumGate("H")
+                state = h_gate.apply(state, self.topology)
+        
+        state /= np.sqrt(np.sum(np.abs(state) ** 2))
+        return state
+        
+    def evolve(
+        self,
+        state: np.ndarray,
+        params: Optional[Dict[str, Any]] = None
+    ) -> np.ndarray:
+        current_state = state.copy()
+        
+        if params and "gates" in params:
+            for gate_name in params["gates"]:
+                gate = QuantumGate(gate_name)
+                current_state = gate.apply(current_state, self.topology)
+        else:
+            for gate in self.gates:
+                current_state = gate.apply(current_state, self.topology)
+                
+        for i in range(self.topology.depth - 1):
+            for j in range(i + 1, self.topology.depth):
+                braiding = self.topology.calculate_braiding(i, j)
+                current_state = np.dot(current_state.reshape(-1, 4), braiding.T).flatten()
+        
+        current_state /= np.sqrt(np.sum(np.abs(current_state) ** 2))
+        return current_state

quantum_gates.py

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+import numpy as np
+from typing import Any
+
+class QuantumGate:
+    def __init__(self, gate_type: str):
+        self.gate_type = gate_type
+        self.matrix = self._initialize_matrix()
+        
+    def _initialize_matrix(self) -> np.ndarray:
+        """Initialize gate unitary matrix"""
+        if self.gate_type == "H":
+            return np.array([[1, 1], [1, -1]]) / np.sqrt(2)
+        elif self.gate_type == "CNOT":
+            return np.array([[1, 0, 0, 0],
+                           [0, 1, 0, 0],
+                           [0, 0, 0, 1],
+                           [0, 0, 1, 0]])
+        elif self.gate_type == "Phase":
+            return np.array([[1, 0], [0, 1j]])
+        elif self.gate_type == "X":
+            return np.array([[0, 1], [1, 0]])
+        elif self.gate_type == "Z":
+            return np.array([[1, 0], [0, -1]])
+        else:
+            raise ValueError(f"Unknown gate type: {self.gate_type}")
+            
+    def apply(self, state: np.ndarray, topology: Any) -> np.ndarray:
+        """Apply gate to quantum state"""
+        if self.gate_type == "H":
+            return self._apply_single_qubit(state)
+        elif self.gate_type == "CNOT":
+            return self._apply_two_qubit(state)
+        else:
+            return self._apply_single_qubit(state)
+
+    def _apply_single_qubit(self, state: np.ndarray) -> np.ndarray:
+        if len(state.shape) == 1:
+            state_2d = state.reshape(-1, 2)
+            result = np.dot(state_2d, self.matrix.T)
+            return result.flatten()
+        return np.dot(state, self.matrix.T)
+
+    def _apply_two_qubit(self, state: np.ndarray) -> np.ndarray:
+        return np.dot(state.reshape(-1, 4), self.matrix.T).flatten()

quantum_peer_model.py

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+import torch
+import numpy as np
+from quantum_circuit import QuantumCircuit
+from quantum_topology import ChernSimonsTopology
+from llm_interface import OpenPeerLLMInterface
+
+class QuantumPeerModel:
+    def __init__(
+        self,
+        model_path: str = "OpenPeerAI/OpenPeerLLM",
+        checkpoint: str = "bestmodel",
+        device: str = None,
+        quantum_depth: int = 3
+    ):
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+            
+        self.device = device
+        self.topology = ChernSimonsTopology(quantum_depth)
+        self.circuit = QuantumCircuit(self.topology)
+        self.llm_interface = OpenPeerLLMInterface(model_path, checkpoint, device)
+        
+    def generate(
+        self,
+        prompt: str,
+        max_length: int = 100,
+        quantum_params: dict = None
+    ) -> str:
+        try:
+            # Process input through quantum circuit
+            quantum_state = self.circuit.prepare_input(prompt)
+            quantum_state = self.circuit.evolve(quantum_state, quantum_params)
+            
+            # Generate response using quantum-modified state
+            response = self.llm_interface.generate(
+                prompt,
+                quantum_state=quantum_state,
+                max_length=max_length
+            )
+            return response
+        except Exception as e:
+            print(f"Error in generation: {e}")
+            return f"Error: Could not generate response. {str(e)}"

quantum_topology.py

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+import numpy as np
+from typing import List, Tuple
+
+class ChernSimonsTopology:
+    def __init__(self, depth: int):
+        self.depth = depth
+        self.dimension = 2 ** depth
+        self.connections = self._initialize_connections()
+        
+    def _initialize_connections(self) -> List[Tuple[int, int]]:
+        """Initialize topological connections based on Chern-Simons theory"""
+        connections = []
+        for i in range(self.depth - 1):
+            for j in range(i + 1, self.depth):
+                connections.append((i, j))
+        return connections
+        
+    def get_allowed_operations(self, qubit: int) -> List[str]:
+        """Get allowed quantum operations for given qubit"""
+        if 0 <= qubit < self.depth:
+            return ["H", "X", "Z", "Phase"]
+        return []
+        
+    def calculate_braiding(self, q1: int, q2: int) -> np.ndarray:
+        """Calculate braiding operation between two qubits"""
+        if (q1, q2) in self.connections or (q2, q1) in self.connections:
+            theta = np.pi / 4  # Topological phase
+            c = np.cos(theta)
+            s = np.sin(theta)
+            return np.array([[c, -s, 0, 0],
+                           [s, c, 0, 0],
+                           [0, 0, c, -s],
+                           [0, 0, s, c]])
+        return np.eye(4)

requirements.txt

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+# Core dependencies
+numpy>=1.20.0
+scipy>=1.7.0
+
+# Testing dependencies
+pytest>=7.0.0
+pytest-cov>=2.12.0

setup.py

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+from setuptools import setup, find_packages
+
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+setup(
+    name="quantumpeer",
+    version="0.1.0",
+    author="[Andrew Magdy Kamal Nassief]",
+    packages=find_packages(where="src"),
+    package_dir={"": "src"},
+    python_requires=">=3.8",
+    install_requires=[
+        "numpy>=1.20.0",
+        "scipy>=1.7.0",
+    ],
+    extras_require={
+        "dev": [
+            "pytest>=7.0.0",
+            "pytest-cov>=2.12.0",
+        ],
+    },
+)

test_quantum_peer.py

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+import pytest
+import torch
+import numpy as np
+from quantum_peer_model import QuantumPeerModel
+from quantum_circuit import QuantumCircuit
+from quantum_topology import ChernSimonsTopology
+from quantum_gates import QuantumGate
+
+@pytest.fixture
+def model():
+    return QuantumPeerModel(device="cpu")
+
+@pytest.fixture
+def quantum_circuit():
+    topology = ChernSimonsTopology(depth=3)
+    return QuantumCircuit(topology)
+
+def test_circuit_initialization(quantum_circuit):
+    """Test quantum circuit initialization"""
+    assert quantum_circuit is not None
+    assert len(quantum_circuit.gates) > 0
+    gate_types = {gate.gate_type for gate in quantum_circuit.gates}
+    required_gates = {"H", "CNOT", "Phase", "X", "Z"}
+    assert required_gates.issubset(gate_types)
+
+def test_quantum_evolution(quantum_circuit):
+    """Test quantum state evolution"""
+    initial_state = np.zeros(quantum_circuit.topology.dimension)
+    initial_state[0] = 1
+    
+    final_state = quantum_circuit.evolve(initial_state)
+    assert isinstance(final_state, np.ndarray)
+    assert np.allclose(np.sum(np.abs(final_state) ** 2), 1)
+
+def test_gate_operations():
+    """Test individual quantum gates"""
+    gates = {
+        "H": QuantumGate("H"),
+        "X": QuantumGate("X"),
+        "Z": QuantumGate("Z"),
+        "Phase": QuantumGate("Phase"),
+        "CNOT": QuantumGate("CNOT")
+    }
+    
+    state = np.array([1, 0])
+    h_state = gates["H"].apply(state, None)
+    expected = np.array([1, 1]) / np.sqrt(2)
+    assert np.allclose(h_state, expected)
+    
+    x_state = gates["X"].apply(state, None)
+    assert np.allclose(x_state, np.array([0, 1]))
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_model_gpu_support():
+    """Test GPU support when available"""
+    model = QuantumPeerModel(device="cuda")
+    assert model.llm_interface.model.device.type == "cuda"
+    response = model.generate("Test prompt")
+    assert isinstance(response, str)
+
+def test_topology_scaling():
+    """Test topology scaling with different depths"""
+    depths = [2, 3, 4]
+    for depth in depths:
+        topology = ChernSimonsTopology(depth)
+        assert topology.dimension == 2 ** depth
+        assert len(topology.connections) == (depth * (depth - 1)) // 2
+
+def test_model_generation(model):
+    """Test text generation with quantum enhancement"""
+    prompt = "Explain quantum computing in"
+    response = model.generate(
+        prompt,
+        max_length=50,
+        quantum_params={"gates": ["H", "CNOT"]}
+    )
+    assert isinstance(response, str)
+    assert len(response) > len(prompt)