From 2996a568befede59d0f4a7e012267a1332299455 Mon Sep 17 00:00:00 2001 From: Senzhan9 Date: Wed, 18 Feb 2026 08:23:20 -0500 Subject: [PATCH] ADD Experiment --- Experiment_hdfm.py | 370 ++++++++++++++++++ .../__pycache__/__init__.cpython-313.pyc | Bin 339 -> 339 bytes .../__pycache__/backend.cpython-313.pyc | Bin 6052 -> 6052 bytes .../__pycache__/dqc_simulator.cpython-313.pyc | Bin 66226 -> 66563 bytes src/dqc_simulator/dqc_simulator.py | 13 +- 5 files changed, 382 insertions(+), 1 deletion(-) create mode 100644 Experiment_hdfm.py diff --git a/Experiment_hdfm.py b/Experiment_hdfm.py new file mode 100644 index 0000000..d3fb196 --- /dev/null +++ b/Experiment_hdfm.py @@ -0,0 +1,370 @@ +""" +量子哈密顿量模拟 - 1D横场伊辛模型(TFIM) +使用Qiskit实现，支持分布式量子计算(DQC) +""" +# ========================= +# Python path setup +# ========================= +import sys +from pathlib import Path +sys.path.insert(0, str(Path(__file__).parent / "src")) + +import numpy as np +import time +import collections +from typing import List, Tuple +import matplotlib.pyplot as plt + +from qiskit import QuantumCircuit, ClassicalRegister, transpile +from qiskit.circuit import Instruction, CircuitInstruction +from qiskit.converters import circuit_to_dag +from qiskit.quantum_info import Statevector +from qiskit_aer import AerSimulator +from qiskit.visualization import plot_histogram +from qiskit_aer.noise import NoiseModel + +# ========================= +# Fake backends (heterogeneous QPUs) +# ========================= +from qiskit_ibm_runtime.fake_provider import ( + FakeVigoV2, + FakeLagosV2, + FakeCasablancaV2, + FakeYorktownV2, + FakeManilaV2, + FakeNairobiV2, + FakeMumbaiV2, + FakeKolkataV2, + FakeGuadalupeV2, + FakeAlmadenV2, + FakeAthensV2, + FakeCambridgeV2 +) + +# ========================= +# Distributed Quantum Computing Simulator +# ========================= +from dqc_simulator import DQCCircuit, DQCQPU, QPUManager +from dqc_simulator.backend import IonQ + +class HamiltonianSimulationQiskit: + """ + 1D横场伊辛模型(TFIM)哈密顿量模拟的Qiskit实现 + + Parameters: + ----------- + num_qubits : int + 量子比特数量 + time_step : int + 时间步长 + total_time : int + 总演化时间 + """ + + def __init__(self, num_qubits: int, time_step: int, total_time: int) -> None: + self.num_qubits = num_qubits + self.time_step = time_step + self.total_time = total_time + + def circuit(self) -> QuantumCircuit: + """ + 生成量子电路 + + Returns: + -------- + QuantumCircuit : 构建的量子电路 + """ + # 物理常数 + hbar = 0.658212 # eV*fs (约化普朗克常数) + jz = hbar * np.pi / 4 # eV, 耦合系数 + freq = 0.0048 # 1/fs, MoSe2声子频率 + + # 声子参数 + w_ph = 2 * np.pi * freq + e_ph = 3 * np.pi * hbar / (8 * np.cos(np.pi * freq)) + + # 初始化量子电路 + qc = QuantumCircuit(self.num_qubits, self.num_qubits) + + # 构建时间演化电路 + num_steps = int(self.total_time / self.time_step) + for step in range(num_steps): + t = (step + 0.5) * self.time_step + + # 单量子比特项 (横场项) + psi = -2.0 * e_ph * np.cos(w_ph * t) * self.time_step / hbar + for q in range(self.num_qubits): + qc.h(q) + qc.rz(psi, q) + qc.h(q) + + # 双量子比特耦合项 (伊辛相互作用) + psi2 = -2.0 * jz * self.time_step / hbar + for i in range(self.num_qubits - 1): + qc.cx(i, i + 1) + qc.rz(psi2, i + 1) + qc.cx(i, i + 1) + + # 添加测量 + qc.measure(range(self.num_qubits), range(self.num_qubits)) + return qc + + def _get_ideal_counts(self, circuit: QuantumCircuit) -> collections.Counter: + """ + 计算理想(无噪声)概率分布 + + Parameters: + ----------- + circuit : QuantumCircuit + 量子电路 + + Returns: + -------- + Counter : 理想概率分布 + """ + qc_no_measure = circuit.remove_final_measurements(inplace=False) + sv = Statevector.from_instruction(qc_no_measure) + probs = sv.probabilities_dict() + return collections.Counter(probs) + + def _average_magnetization(self, result: dict, shots: int) -> float: + """ + 计算平均磁化强度 + + Parameters: + ----------- + result : dict + 测量结果计数 + shots : int + 总测量次数 + + Returns: + -------- + float : 平均磁化强度 + """ + mag = 0 + for spin_str, count in result.items(): + bitstring = spin_str[::-1] + # 将比特串转换为自旋: 0→+1, 1→-1 + spin_int = [1 - 2 * int(s) for s in bitstring] + mag += (sum(spin_int) / len(spin_int)) * count + average_mag = mag / shots + return average_mag + + def score(self, counts: collections.Counter) -> float: + """ + 基于磁化强度计算保真度评分 + + Parameters: + ----------- + counts : Counter + 实验测量结果 + + Returns: + -------- + float : [0,1]范围的评分，1表示完美匹配 + """ + ideal_counts = self._get_ideal_counts(self.circuit()) + total_shots = sum(counts.values()) + + mag_ideal = self._average_magnetization(ideal_counts, 1) + mag_experimental = self._average_magnetization(counts, total_shots) + + return 1 - abs(mag_ideal - mag_experimental) / 2 + + def score2(self, counts: collections.Counter) -> float: + """ + 使用Hellinger保真度评估模拟质量 + + Parameters: + ----------- + counts : Counter + 实验测量结果 + + Returns: + -------- + float : Hellinger保真度 [0,1] + """ + # 获取电路并移除测量 + qc = self.circuit() + qc_no_measure = qc.copy() + qc_no_measure.data = [ + inst for inst in qc_no_measure.data + if inst[0].name != 'measure' + ] + + # 生成理想状态向量 + sv = Statevector.from_instruction(qc_no_measure) + ideal_probs = sv.probabilities_dict() + + # 归一化实验概率 + total_shots = sum(counts.values()) + exp_probs = {k: v / total_shots for k, v in counts.items()} + + # 计算Hellinger保真度 + fidelity = sum( + np.sqrt(ideal_probs.get(k, 0)) * np.sqrt(p) + for k, p in exp_probs.items() + ) + + return fidelity + + +def count_two_qubit_gates(circuit: QuantumCircuit) -> int: + """ + 统计电路中两比特门的数量 + + Parameters: + ----------- + circuit : QuantumCircuit + 量子电路 + + Returns: + -------- + int : 两比特门数量 + """ + dag = circuit_to_dag(circuit) + dag.remove_all_ops_named("barrier") + return len(list(dag.two_qubit_ops())) + + +def setup_qpugroup(qpu_configs: List[Tuple[int, str]], + connections: List[Tuple[int, int]], + dist: float = 0.5) -> QPUManager: + """ + 设置QPU组 + + Parameters: + ----------- + qpu_configs : List[Tuple[int, str]] + QPU配置列表，每个元素为(qpu_id, backend_name) + connections : List[Tuple[int, int]] + QPU连接列表，每个元素为(qpu1_id, qpu2_id) + dist : float + QPU间距离，默认0.5 + + Returns: + -------- + QPUManager : 配置好的QPU管理器 + """ + qpugroup = QPUManager() + + # 添加QPU + for qpu_id, backend_name in qpu_configs: + qpugroup.add_qpu(DQCQPU(qpu_id, backend_name)) + + # 添加连接 + for qpu1, qpu2 in connections: + qpugroup.add_coonnection(qpu1, qpu2, distance=dist) + + return qpugroup + + +def run_hamiltonian_dqc(ham: HamiltonianSimulationQiskit, + partition: List[int], + qpugroup: QPUManager, + numbits: int = 12) -> Tuple[float, int, int]: + """ + 运行哈密顿量DQC模拟并测量性能指标 + + Parameters: + ----------- + ham : HamiltonianSimulationQiskit + 哈密顿量模拟对象 + partition : List[int] + 量子比特分区方案 + qpugroup : QPUManager + QPU管理器 + numbits : int + 保留的比特数，默认12 + + Returns: + -------- + Tuple[float, int, int] : (保真度, 远程门数量, 两比特门总数) + """ + qc = ham.circuit() + + print(f" 电路信息: {qc.num_qubits} qubits, {qc.num_clbits} classical bits") + + try: + # 执行DQC + qc_dqc = DQCCircuit(qc) + result_qc = qc_dqc.Execution(partition, qpugroup, comm_noise=True, measure_time = True) + + # 获取噪声模型并运行仿真 + noise_model = qc_dqc.get_noise_model() + sim = AerSimulator(noise_model=noise_model) + compiled = transpile(result_qc, sim) + start_time = time.time() + job = sim.run(compiled, shots=1000) + result = job.result() + end_time = time.time() + elapsed_time = end_time - start_time + print(f"Running time: {elapsed_time:.4f} seconds") + + # 处理测量结果 + counts = result.get_counts() + + # 保留前numbits位 + new_counts = {} + for bitstring, cnt in counts.items(): + bits = bitstring[:numbits] + new_counts[bits] = new_counts.get(bits, 0) + cnt + + # 计算保真度 (Hellinger fidelity) + fidelity = ham.score2(new_counts) + + # 获取性能指标 + num_remote_gates = qc_dqc.Num_RemoteGate + num_two_qubit_gates = count_two_qubit_gates(result_qc) + + return fidelity, num_remote_gates, num_two_qubit_gates + + except Exception as e: + print(f" Error with partition {partition}: {e}") + import traceback + traceback.print_exc() + return None, None, None + + +def main(): + """ + 主函数 - 演示如何使用 + """ + # 创建12量子比特的TFIM电路 + ham = HamiltonianSimulationQiskit(num_qubits=20, time_step=1, total_time=3) + qc = ham.circuit() + qc0 = qc.copy() + + # 设置分布式量子计算 + qc_dqc = DQCCircuit(qc0) + partition = [4, 4, 4, 4, 4] # 4个QPU，每个3个量子比特 + + # 配置QPU组 + qpugroup = QPUManager() + for i in range(5): + qpugroup.add_qpu(DQCQPU(i, "FakeVigoV2")) + + # 添加QPU连接（线性拓扑） + distance = 5 + qpugroup.add_coonnection(0, 1, distance=distance) + # 可以添加更多连接以形成不同的拓扑结构 + # qpugroup.add_coonnection(0, 2, distance=distance) + qpugroup.add_coonnection(1, 2, distance=distance) + # qpugroup.add_coonnection(1, 3, distance=distance) + qpugroup.add_coonnection(2, 3, distance=distance) + qpugroup.add_coonnection(3, 4, distance=distance) + + # 运行DQC模拟 + fidelity, num_remote, num_two_qubit = run_hamiltonian_dqc( + ham, partition, qpugroup, numbits=20 + ) + + print(f"\n结果:") + print(f" Hellinger保真度: {fidelity:.4f}") + # print(f" 远程门数量: {num_remote}") + # print(f" 两比特门总数: {num_two_qubit}") + + +if __name__ == "__main__": + main() \ No newline at end of file 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+34,7 @@ import numpy as np import copy +import time import matplotlib.pyplot as plt class RemoteGate(Instruction): @@ -336,7 +337,11 @@ def __init__(self, *args, **kwargs): self.Num_RemoteGate = 0 # 执行 - def Execution(self, config, qpugroup, comm_noise = False): + def Execution(self, config, qpugroup, comm_noise=False, measure_time=False): + # 开始计时 + if measure_time: + start_time = time.time() + self.qpugroup = qpugroup qpus = self.qpugroup.qpus @@ -359,6 +364,12 @@ def Execution(self, config, qpugroup, comm_noise = False): result_qc = self.merge_trans_circuits(comm_noise) + # 结束计时并输出 + if measure_time: + end_time = time.time() + elapsed_time = end_time - start_time + print(f"SimDisQ Execution time: {elapsed_time:.4f} seconds") + return result_qc # Get the index of a qubit in the circuit