AI 中文总结
针对NISQ设备中异构噪声影响变分量子特征求解器并行化效果的问题，提出三种易并行策略并通过CUNQA平台仿真评估其加速比与精度表现。
AI 中文摘要
变分量子特征求解器需要大量电路执行，非常适合分布式并行化，但含噪中等规模量子（NISQ）设备中的异构噪声会扭曲结果并降低效率。使用CUNQA平台仿真虚拟量子处理单元（QPU），我们在加速比、精度等指标上评估了三种易并行策略： shots级、梯度与观测量的电路级，以及基于种群优化器的候选级。
英文摘要
Variational Quantum Eigensolver requires many circuit executions, making it ideal for distributed parallelization. However, heterogeneous noise in NISQ devices can skew results and efficiency. Using the CUNQA platform for emulation of virtual QPUs, we evaluate three embarrassingly parallelization strategies (shot-level, circuit-level for gradients and observables and candidate level for population-based optimizers) across metrics like speedup and accuracy.
