标题： 变分量子本征求解器的分布式实现以解决QUBO问题 显示英文标题

标题： Distributed Implementation of Variational Quantum Eigensolver to Solve QUBO Problems

摘要： 我们提出了一种变分量子本征求解器（VQE）的分布式算法和实现，称为分布式VQE（DVQE）。 DVQE作为一个开源的Python包提供，能够在分布式方式下在多个逻辑量子处理单元（QPUs）上执行参数化量子电路。 这种方法解决了近期量子设备的关键硬件限制，包括有限的量子比特数量和有限的电路深度。 构建了分布式ansatz电路，以保持其单片对应物的量子态保真度，从而在分散计算负载的同时保持一致的能量估计。 为了提高DVQE ansatz电路变分参数优化循环的收敛性和鲁棒性，我们结合使用ADAM优化器和元启发式初始化策略，这些策略在各种测试案例中均优于随机初始化。 完整的DVQE流程被实现为一个模块化的Python包，接受QUBO问题作为输入，并支持单片和分布式执行模式。 该框架利用 Qiskit来构建和模拟分布式电路，并包含一个内部的贪心算法，用于在多个QPUs之间自动分配量子比特。 在QUBO基准上的仿真结果证实了该方法的正确性，为真实QPU的部署以及分布式量子优化的进一步探索铺平了道路。 \textbf{模拟器在\href{https://github.com/LSU-RAISE-LAB/DVQE.git}{GitHub}上公开可用，包名为 raiselab，包含一系列教程示例。} 显示英文摘要

摘要： We present a distributed algorithm and implementation of the variational quantum eigensolver (VQE), termed distributed VQE (DVQE). DVQE, provided as an open-source Python package, enables the execution of parameterized quantum circuits across multiple logical quantum processing units (QPUs) in a distributed fashion. This approach addresses key hardware limitations of near-term quantum devices, including restricted qubit counts and limited circuit depth. Distributed ansatz circuits are constructed to preserve the quantum state fidelity of their monolithic counterparts, allowing consistent energy estimation while distributing the computational load. To improve the convergence and robustness of the optimization loop for identifying the variational parameters of the DVQE ansatz circuit, we use the ADAM optimizer in combination with metaheuristic initialization strategies, which outperform random initialization across various test cases. The complete DVQE pipeline is implemented in a modular Python package that accepts QUBO problems as input and supports monolithic and distributed execution modes. The framework leverages Qiskit to construct and simulate distributed circuits, and includes an internal greedy algorithm for automatic qubit allocation across multiple QPUs. Simulation results on QUBO benchmarks confirm the correctness of the approach, paving the way for real QPU deployment and further exploration of distributed quantum optimization. \textbf{The simulator is publicly available on \href{https://github.com/LSU-RAISE-LAB/DVQE.git}{GitHub} under a package named raiselab, with a collection of tutorial examples.}