""" CUDA Quantum Python Bridge Provides a unified interface for Node.js to interact with CUDA Quantum functionality """ import json import sys import traceback import numpy as np from typing import Dict, List, Any, Optional, Union, Tuple import logging # Setup logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) try: import cudaq from cudaq import spin CUDAQ_AVAILABLE = True logger.info("CUDA Quantum successfully imported") except ImportError as e: CUDAQ_AVAILABLE = False logger.error(f"CUDA Quantum not available: {e}") logger.error("Please install CUDA Quantum: pip install cudaq") class QuantumKernelManager: """Manages quantum kernels and their execution""" def __init__(self): self.kernels: Dict[str, Any] = {} self.kernel_metadata: Dict[str, Dict] = {} def create_kernel(self, name: str, num_qubits: int, parameters: Optional[List[Dict]] = None) -> Dict: """Create a new quantum kernel""" if not CUDAQ_AVAILABLE: return {"error": "CUDA Quantum not available"} try: # Create kernel dynamically kernel_code = self._generate_kernel_code(name, num_qubits, parameters or []) exec(kernel_code, globals()) self.kernels[name] = globals()[name] self.kernel_metadata[name] = { "num_qubits": num_qubits, "parameters": parameters or [], "operations": [] } return {"success": True, "kernel_name": name} except Exception as e: logger.error(f"Error creating kernel {name}: {e}") return {"error": str(e), "traceback": traceback.format_exc()} def _generate_kernel_code(self, name: str, num_qubits: int, parameters: List[Dict]) -> str: """Generate CUDA Quantum kernel code dynamically""" param_str = "" if parameters: param_types = { "int": "int", "float": "float", "complex": "complex", "list[int]": "list[int]", "list[float]": "list[float]", "list[complex]": "list[complex]" } param_list = [] for p in parameters: param_list.append(f"{p['name']}: {param_types.get(p['type'], 'float')}") param_str = ", " + ", ".join(param_list) code = f''' @cudaq.kernel def {name}({param_str.lstrip(', ')}): """Dynamically generated quantum kernel""" qubits = cudaq.qvector({num_qubits}) # Placeholder - operations will be added dynamically pass ''' return code def apply_gate(self, kernel_name: str, gate_name: str, qubits: List[int], parameters: Optional[List[float]] = None, controls: Optional[List[int]] = None, adjoint: bool = False) -> Dict: """Apply a quantum gate to a kernel""" if not CUDAQ_AVAILABLE: return {"error": "CUDA Quantum not available"} if kernel_name not in self.kernel_metadata: return {"error": f"Kernel {kernel_name} not found"} try: operation = { "gate": gate_name, "qubits": qubits, "parameters": parameters, "controls": controls, "adjoint": adjoint } self.kernel_metadata[kernel_name]["operations"].append(operation) # Rebuild kernel with new operations self._rebuild_kernel(kernel_name) return {"success": True} except Exception as e: logger.error(f"Error applying gate {gate_name} to {kernel_name}: {e}") return {"error": str(e), "traceback": traceback.format_exc()} def _rebuild_kernel(self, kernel_name: str): """Rebuild kernel with accumulated operations""" metadata = self.kernel_metadata[kernel_name] num_qubits = metadata["num_qubits"] parameters = metadata["parameters"] operations = metadata["operations"] # Generate parameter string param_str = "" if parameters: param_types = { "int": "int", "float": "float", "complex": "complex", "list[int]": "list[int]", "list[float]": "list[float]", "list[complex]": "list[complex]" } param_list = [] for p in parameters: param_list.append(f"{p['name']}: {param_types.get(p['type'], 'float')}") param_str = ", " + ", ".join(param_list) # Generate operations code ops_code = [] for op in operations: gate_code = self._generate_gate_code(op) if gate_code: ops_code.append(f" {gate_code}") code = f''' @cudaq.kernel def {kernel_name}({param_str.lstrip(', ')}): """Dynamically generated quantum kernel with operations""" qubits = cudaq.qvector({num_qubits}) {chr(10).join(ops_code) if ops_code else " pass"} ''' # Execute and register new kernel exec(code, globals()) self.kernels[kernel_name] = globals()[kernel_name] def _generate_gate_code(self, operation: Dict) -> str: """Generate code for a single gate operation""" gate = operation["gate"] qubits = operation["qubits"] parameters = operation.get("parameters", []) controls = operation.get("controls", []) adjoint = operation.get("adjoint", False) if len(qubits) == 1: target = f"qubits[{qubits[0]}]" else: target = f"[{', '.join([f'qubits[{q}]' for q in qubits])}]" # Handle parameterized gates if parameters: if len(parameters) == 1: gate_call = f"{gate}({parameters[0]}, {target})" else: params_str = ", ".join(map(str, parameters)) gate_call = f"{gate}({params_str}, {target})" else: gate_call = f"{gate}({target})" # Handle controlled gates if controls: if len(controls) == 1: gate_call = f"{gate}.ctrl(qubits[{controls[0]}], {target})" else: ctrl_str = "[" + ", ".join([f"qubits[{c}]" for c in controls]) + "]" gate_call = f"{gate}.ctrl({ctrl_str}, {target})" # Handle adjoint if adjoint: gate_call = gate_call.replace(gate, f"{gate}.adj") return gate_call class QuantumExecutor: """Handles execution of quantum kernels""" def __init__(self, kernel_manager: QuantumKernelManager): self.kernel_manager = kernel_manager def set_target(self, target: str, configuration: Optional[Dict] = None) -> Dict: """Set quantum execution target""" if not CUDAQ_AVAILABLE: return {"error": "CUDA Quantum not available"} try: if configuration: cudaq.set_target(target, **configuration) else: cudaq.set_target(target) return {"success": True, "target": target} except Exception as e: logger.error(f"Error setting target {target}: {e}") return {"error": str(e), "traceback": traceback.format_exc()} def sample(self, kernel_name: str, shots: int = 1000, parameters: Optional[Dict] = None) -> Dict: """Sample measurement results from quantum kernel""" if not CUDAQ_AVAILABLE: return {"error": "CUDA Quantum not available"} if kernel_name not in self.kernel_manager.kernels: return {"error": f"Kernel {kernel_name} not found"} try: kernel = self.kernel_manager.kernels[kernel_name] if parameters: # Call with parameters args = [parameters[p["name"]] for p in self.kernel_manager.kernel_metadata[kernel_name]["parameters"]] result = cudaq.sample(kernel, *args, shots_count=shots) else: result = cudaq.sample(kernel, shots_count=shots) # Convert result to serializable format counts = {} for bits, count in result.items(): counts[str(bits)] = count return { "success": True, "counts": counts, "shots": shots, "total_counts": sum(counts.values()) } except Exception as e: logger.error(f"Error sampling kernel {kernel_name}: {e}") return {"error": str(e), "traceback": traceback.format_exc()} def observe(self, kernel_name: str, hamiltonian_terms: List[Dict], shots: int = 1000, parameters: Optional[Dict] = None) -> Dict: """Compute expectation value of Hamiltonian""" if not CUDAQ_AVAILABLE: return {"error": "CUDA Quantum not available"} if kernel_name not in self.kernel_manager.kernels: return {"error": f"Kernel {kernel_name} not found"} try: kernel = self.kernel_manager.kernels[kernel_name] # Build Hamiltonian from terms hamiltonian = self._build_hamiltonian(hamiltonian_terms) if parameters: args = [parameters[p["name"]] for p in self.kernel_manager.kernel_metadata[kernel_name]["parameters"]] result = cudaq.observe(kernel, hamiltonian, *args, shots_count=shots) else: result = cudaq.observe(kernel, hamiltonian, shots_count=shots) return { "success": True, "expectation": result.expectation(), "variance": result.variance() if hasattr(result, 'variance') else None, "shots": shots } except Exception as e: logger.error(f"Error observing kernel {kernel_name}: {e}") return {"error": str(e), "traceback": traceback.format_exc()} def get_state(self, kernel_name: str, parameters: Optional[Dict] = None) -> Dict: """Get quantum state vector""" if not CUDAQ_AVAILABLE: return {"error": "CUDA Quantum not available"} if kernel_name not in self.kernel_manager.kernels: return {"error": f"Kernel {kernel_name} not found"} try: kernel = self.kernel_manager.kernels[kernel_name] if parameters: args = [parameters[p["name"]] for p in self.kernel_manager.kernel_metadata[kernel_name]["parameters"]] state = cudaq.get_state(kernel, *args) else: state = cudaq.get_state(kernel) # Convert state to serializable format state_array = np.array(state) state_list = [] for amplitude in state_array: state_list.append({ "real": float(amplitude.real), "imag": float(amplitude.imag) }) return { "success": True, "state": state_list, "dimension": len(state_list) } except Exception as e: logger.error(f"Error getting state for kernel {kernel_name}: {e}") return {"error": str(e), "traceback": traceback.format_exc()} def _build_hamiltonian(self, terms: List[Dict]): """Build CUDA Quantum Hamiltonian from term list""" h = None for term in terms: paulis = term["paulis"] qubits = term["qubits"] coeff_real = term["coefficient"]["real"] coeff_imag = term["coefficient"]["imag"] # Build Pauli string pauli_term = None for i, (pauli, qubit) in enumerate(zip(paulis, qubits)): if pauli == 'I': continue elif pauli == 'X': p = spin.x(qubit) elif pauli == 'Y': p = spin.y(qubit) elif pauli == 'Z': p = spin.z(qubit) else: raise ValueError(f"Invalid Pauli operator: {pauli}") if pauli_term is None: pauli_term = p else: pauli_term = pauli_term * p if pauli_term is not None: # Apply coefficient if coeff_imag != 0: coeff = complex(coeff_real, coeff_imag) else: coeff = coeff_real term_contribution = coeff * pauli_term if h is None: h = term_contribution else: h = h + term_contribution return h if h is not None else 0.0 * spin.i(0) # Global instances kernel_manager = QuantumKernelManager() executor = QuantumExecutor(kernel_manager) def dispatch_command(command: str, **kwargs) -> Dict: """Main dispatch function for Python bridge commands""" try: if command == "create_kernel": return kernel_manager.create_kernel( kwargs["name"], kwargs["num_qubits"], kwargs.get("parameters") ) elif command == "apply_gate": return kernel_manager.apply_gate( kwargs["kernel_name"], kwargs["gate_name"], kwargs["qubits"], kwargs.get("parameters"), kwargs.get("controls"), kwargs.get("adjoint", False) ) elif command == "set_target": return executor.set_target( kwargs["target"], kwargs.get("configuration") ) elif command == "sample": return executor.sample( kwargs["kernel_name"], kwargs.get("shots", 1000), kwargs.get("parameters") ) elif command == "observe": return executor.observe( kwargs["kernel_name"], kwargs["hamiltonian_terms"], kwargs.get("shots", 1000), kwargs.get("parameters") ) elif command == "get_state": return executor.get_state( kwargs["kernel_name"], kwargs.get("parameters") ) elif command == "list_kernels": return { "success": True, "kernels": list(kernel_manager.kernels.keys()), "metadata": kernel_manager.kernel_metadata } elif command == "get_platform_info": if not CUDAQ_AVAILABLE: return {"error": "CUDA Quantum not available"} try: platform = cudaq.get_platform() return { "success": True, "platform_name": platform.name(), "num_qpus": platform.num_qpus(), "is_simulator": platform.is_simulator(), "is_remote": platform.is_remote() } except: return {"success": True, "platform_name": "default"} else: return {"error": f"Unknown command: {command}"} except Exception as e: logger.error(f"Error executing command {command}: {e}") return {"error": str(e), "traceback": traceback.format_exc()} def main(): """Main entry point for standalone execution""" if len(sys.argv) > 1: # Command line mode command_json = sys.argv[1] try: command_data = json.loads(command_json) result = dispatch_command(**command_data) print(json.dumps(result)) except Exception as e: print(json.dumps({"error": str(e), "traceback": traceback.format_exc()})) else: # Interactive mode print("CUDA Quantum Python Bridge - Interactive Mode") print("Available commands: create_kernel, apply_gate, set_target, sample, observe, get_state") while True: try: command_line = input("> ") if command_line.lower() in ["quit", "exit"]: break command_data = json.loads(command_line) result = dispatch_command(**command_data) print(json.dumps(result, indent=2)) except KeyboardInterrupt: break except Exception as e: print(json.dumps({"error": str(e)})) if __name__ == "__main__": main()