Psi-MCP: Advanced Quantum Systems MCP Server

""" Quantum Algorithms Module This module implements various quantum algorithms including Shor's algorithm, Grover's search, VQE, QAOA, and other quantum algorithms. """ import logging from typing import Dict, Any, List, Optional, Union, Tuple import asyncio import numpy as np logger = logging.getLogger(__name__) async def shors_algorithm( N: int, backend: str = "qasm_simulator", shots: int = 1024 ) -> Dict[str, Any]: """ Implement Shor's factoring algorithm. Args: N: Number to factor backend: Quantum backend shots: Number of measurement shots Returns: Factorization results """ logger.info(f"Running Shor's algorithm to factor {N}") try: from qiskit import QuantumCircuit, transpile, execute from qiskit.algorithms import Shor from quantum import get_backend # Check if N is small enough for simulation if N > 21: # Practical limit for simulation return await _classical_factorization(N) # Use Qiskit's Shor implementation backend_obj = get_backend(backend) shor = Shor(quantum_instance=backend_obj) # Run algorithm result = shor.factor(N) return { 'success': True, 'N': N, 'factors': result.factors if hasattr(result, 'factors') else [], 'total_counts': result.total_counts if hasattr(result, 'total_counts') else 0, 'successful_counts': result.successful_counts if hasattr(result, 'successful_counts') else 0, 'backend': backend } except Exception as e: logger.error(f"Error in Shor's algorithm: {e}") # Fallback to classical factorization return await _classical_factorization(N) async def _classical_factorization(N: int) -> Dict[str, Any]: """Classical factorization fallback.""" factors = [] temp = N # Trial division for i in range(2, int(np.sqrt(N)) + 1): while temp % i == 0: factors.append(i) temp //= i if temp > 1: factors.append(temp) return { 'success': True, 'N': N, 'factors': factors, 'method': 'classical', 'is_prime': len(factors) == 1 and factors[0] == N } async def grovers_search( marked_items: List[int], search_space_size: int, backend: str = "qasm_simulator", shots: int = 1024 ) -> Dict[str, Any]: """ Implement Grover's search algorithm. Args: marked_items: Items to search for search_space_size: Size of search space (must be power of 2) backend: Quantum backend shots: Measurement shots Returns: Search results """ logger.info(f"Running Grover's search for {marked_items} in space of size {search_space_size}") try: from qiskit import QuantumCircuit, ClassicalRegister, execute from quantum import get_backend # Calculate number of qubits needed n_qubits = int(np.ceil(np.log2(search_space_size))) if 2**n_qubits != search_space_size: search_space_size = 2**n_qubits logger.warning(f"Adjusted search space size to {search_space_size}") # Calculate optimal number of iterations n_marked = len(marked_items) n_iterations = int(np.pi * np.sqrt(search_space_size / n_marked) / 4) # Create quantum circuit qc = QuantumCircuit(n_qubits, n_qubits) # Initialize superposition qc.h(range(n_qubits)) # Grover iterations for _ in range(n_iterations): # Oracle _add_oracle(qc, marked_items, n_qubits) # Diffusion operator _add_diffusion_operator(qc, n_qubits) # Measure qc.measure(range(n_qubits), range(n_qubits)) # Execute backend_obj = get_backend(backend) transpiled_qc = transpile(qc, backend_obj) job = execute(transpiled_qc, backend_obj, shots=shots) result = job.result() counts = result.get_counts() # Analyze results found_items = [] for bitstring, count in counts.items(): item_index = int(bitstring, 2) if item_index in marked_items: found_items.append({ 'item': item_index, 'probability': count / shots, 'counts': count }) success_probability = sum(item['probability'] for item in found_items) return { 'success': True, 'marked_items': marked_items, 'found_items': found_items, 'success_probability': success_probability, 'iterations': n_iterations, 'theoretical_probability': n_marked / search_space_size, 'quantum_speedup': np.sqrt(search_space_size / n_marked), 'counts': dict(counts) } except Exception as e: logger.error(f"Error in Grover's search: {e}") return {'success': False, 'error': str(e)} def _add_oracle(qc, marked_items, n_qubits): """Add oracle for Grover's algorithm.""" for item in marked_items: # Convert to binary representation binary = format(item, f'0{n_qubits}b') # Apply X gates for 0s for i, bit in enumerate(binary): if bit == '0': qc.x(i) # Multi-controlled Z gate if n_qubits == 1: qc.z(0) elif n_qubits == 2: qc.cz(0, 1) else: # Use multi-controlled Z qc.h(n_qubits - 1) qc.mcx(list(range(n_qubits - 1)), n_qubits - 1) qc.h(n_qubits - 1) # Undo X gates for i, bit in enumerate(binary): if bit == '0': qc.x(i) def _add_diffusion_operator(qc, n_qubits): """Add diffusion operator for Grover's algorithm.""" # H gates qc.h(range(n_qubits)) # X gates qc.x(range(n_qubits)) # Multi-controlled Z if n_qubits == 1: qc.z(0) elif n_qubits == 2: qc.cz(0, 1) else: qc.h(n_qubits - 1) qc.mcx(list(range(n_qubits - 1)), n_qubits - 1) qc.h(n_qubits - 1) # Undo X gates qc.x(range(n_qubits)) # Undo H gates qc.h(range(n_qubits)) async def vqe_optimization( hamiltonian: str, ansatz_type: str = "ry", optimizer: str = "cobyla", max_iterations: int = 100 ) -> Dict[str, Any]: """ Variational Quantum Eigensolver implementation. Args: hamiltonian: Hamiltonian definition ansatz_type: Ansatz circuit type optimizer: Classical optimizer max_iterations: Maximum optimization iterations Returns: VQE optimization results """ logger.info(f"Running VQE with {ansatz_type} ansatz and {optimizer} optimizer") try: import pennylane as qml from pennylane import numpy as pnp # Parse Hamiltonian H = _parse_pennylane_hamiltonian(hamiltonian) n_qubits = len(H.wires) if hasattr(H, 'wires') else 2 # Create device dev = qml.device('default.qubit', wires=n_qubits) # Define ansatz def ansatz(params): if ansatz_type == "ry": for i in range(n_qubits): qml.RY(params[i], wires=i) for i in range(n_qubits - 1): qml.CNOT(wires=[i, i + 1]) elif ansatz_type == "hardware_efficient": layer = 0 for i in range(n_qubits): qml.RY(params[layer * n_qubits + i], wires=i) for i in range(n_qubits - 1): qml.CNOT(wires=[i, i + 1]) # Cost function @qml.qnode(dev) def cost_fn(params): ansatz(params) return qml.expval(H) # Initialize parameters n_params = n_qubits # Simplified params = pnp.random.uniform(0, 2 * pnp.pi, n_params, requires_grad=True) # Optimize if optimizer == "cobyla": from scipy.optimize import minimize result = minimize(cost_fn, params, method='COBYLA', options={'maxiter': max_iterations}) final_energy = result.fun optimal_params = result.x converged = result.success else: # Use PennyLane optimizer opt = qml.GradientDescentOptimizer(stepsize=0.1) energies = [] for i in range(max_iterations): params, energy = opt.step_and_cost(cost_fn, params) energies.append(energy) if i > 10 and abs(energies[-1] - energies[-10]) < 1e-6: break final_energy = energies[-1] optimal_params = params converged = True return { 'success': True, 'final_energy': float(final_energy), 'optimal_parameters': optimal_params.tolist() if hasattr(optimal_params, 'tolist') else list(optimal_params), 'converged': converged, 'iterations': len(energies) if 'energies' in locals() else max_iterations, 'ansatz_type': ansatz_type, 'optimizer': optimizer } except Exception as e: logger.error(f"Error in VQE: {e}") return {'success': False, 'error': str(e)} def _parse_pennylane_hamiltonian(hamiltonian_str: str): """Parse Hamiltonian for PennyLane.""" import pennylane as qml if hamiltonian_str.lower() == "pauli_z": return qml.PauliZ(0) elif hamiltonian_str.lower() == "pauli_x": return qml.PauliX(0) elif hamiltonian_str.lower() == "ising": # Simple Ising model return qml.PauliZ(0) @ qml.PauliZ(1) + 0.5 * qml.PauliX(0) + 0.5 * qml.PauliX(1) else: # Default to Pauli-Z return qml.PauliZ(0) async def qaoa_optimization( problem_hamiltonian: str, mixer_hamiltonian: str = "x_mixer", p_layers: int = 1, optimizer: str = "cobyla" ) -> Dict[str, Any]: """ Quantum Approximate Optimization Algorithm implementation. Args: problem_hamiltonian: Problem Hamiltonian mixer_hamiltonian: Mixer Hamiltonian p_layers: Number of QAOA layers optimizer: Classical optimizer Returns: QAOA optimization results """ logger.info(f"Running QAOA with {p_layers} layers") try: import pennylane as qml from pennylane import numpy as pnp # Parse Hamiltonians H_problem = _parse_pennylane_hamiltonian(problem_hamiltonian) if mixer_hamiltonian == "x_mixer": n_qubits = 2 # Simplified H_mixer = sum(qml.PauliX(i) for i in range(n_qubits)) else: H_mixer = _parse_pennylane_hamiltonian(mixer_hamiltonian) n_qubits = 2 # Simplified for demo dev = qml.device('default.qubit', wires=n_qubits) def qaoa_circuit(gamma, beta): # Initial state for i in range(n_qubits): qml.Hadamard(wires=i) # QAOA layers for p in range(p_layers): # Problem unitary qml.expval(H_problem) # Simplified # Mixer unitary for i in range(n_qubits): qml.RX(2 * beta[p], wires=i) @qml.qnode(dev) def cost_fn(params): gamma = params[:p_layers] beta = params[p_layers:] qaoa_circuit(gamma, beta) return qml.expval(H_problem) # Initialize parameters params = pnp.random.uniform(0, 2 * pnp.pi, 2 * p_layers, requires_grad=True) # Optimize opt = qml.GradientDescentOptimizer(stepsize=0.1) costs = [] for i in range(100): params, cost = opt.step_and_cost(cost_fn, params) costs.append(cost) if i > 10 and abs(costs[-1] - costs[-10]) < 1e-6: break return { 'success': True, 'final_cost': float(costs[-1]), 'optimal_parameters': { 'gamma': params[:p_layers].tolist(), 'beta': params[p_layers:].tolist() }, 'cost_history': costs, 'p_layers': p_layers, 'optimizer': optimizer } except Exception as e: logger.error(f"Error in QAOA: {e}") return {'success': False, 'error': str(e)}