通过量子加速器平台虚拟化实现并行量子计算模拟
Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization
AI总结：
提出一种基于虚拟量子处理单元阵列映射到经典HPC节点的并行量子计算模拟模型，在XACC框架内实现后端无关的并行量子电路执行，并在变分量子本征求解器梯度计算和量子电路学习中展示了强扩展性。
摘要：
量子电路执行是量子计算中的核心任务。由于固有的量子力学限制，量子计算工作流通常涉及对大量略有不同的量子电路进行大量独立测量。在此，我们讨论了一种并行模拟此类量子电路执行的简单模型，该模型基于引入一个大型虚拟量子处理单元阵列，并将其映射到经典 HPC 节点，作为并行量子计算平台。该模型在 XACC 框架内实现，能够直接利用其与后端无关的特性，从而在 XACC 支持的任何目标后端上实现并行量子电路执行。我们通过在两个相关领域科学问题中展示强扩展性来说明该方法的性能，即计算多收缩变分量子本征求解器的梯度和数据驱动的量子电路学习，其中我们改变了量子比特数和电路层数。后一种（经典）模拟利用 cuQuantum SDK 库，在 GPU 加速的 HPC 平台上高效运行。
英文摘要：
Quantum circuit execution is the central task in quantum computation. Due to inherent quantum-mechanical constraints, quantum computing workflows often involve a considerable number of independent measurements over a large set of slightly different quantum circuits. Here we discuss a simple model for parallelizing simulation of such quantum circuit executions that is based on introducing a large array of virtual quantum processing units, mapped to classical HPC nodes, as a parallel quantum computing platform. Implemented within the XACC framework, the model can readily take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend supported by XACC. We illustrate the performance of this approach by demonstrating strong scaling in two pertinent domain science problems, namely in computing the gradients for the multi-contracted variational quantum eigensolver and in data-driven quantum circuit learning, where we vary the number of qubits and the number of circuit layers. The latter (classical) simulation leverages the cuQuantum SDK library to run efficiently on GPU-accelerated HPC platforms.
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