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