论文标题
ScaleQC：用于量子和经典处理器混合计算的可扩展框架
ScaleQC: A Scalable Framework for Hybrid Computation on Quantum and Classical Processors
论文作者
论文摘要
量子处理单元（QPU）必须满足其Qubits高度要求的数量和质量要求，以在有用量表下为问题产生准确的结果。此外，量子电路的经典模拟通常不会扩展。取而代之的是，量子电路切割技术将大量子电路切成多个较小的子电路，可用于功能较小的QPU。但是，切割产生的经典后处理引入了运行时和内存瓶颈。我们的工具称为scaleqc，通过开发新型算法技术（包括（1）量子状态合并框架，这些算法框架迅速定位大量子电路的溶液状态； （2）一个自动求解器，可切开复合量子电路以适合较小的QPU； （3）基于张量的基于网络的后处理，可最大程度地减少经典开销。我们的实验证明了QPU的需求优势在纯粹的量子平台上，并且在纯粹的经典平台上的运行时优势最高为1000 QUAT。
Quantum processing unit (QPU) has to satisfy highly demanding quantity and quality requirements on its qubits to produce accurate results for problems at useful scales. Furthermore, classical simulations of quantum circuits generally do not scale. Instead, quantum circuit cutting techniques cut and distribute a large quantum circuit into multiple smaller subcircuits feasible for less powerful QPUs. However, the classical post-processing incurred from the cutting introduces runtime and memory bottlenecks. Our tool, called ScaleQC, addresses the bottlenecks by developing novel algorithmic techniques including (1) a quantum states merging framework that quickly locates the solution states of large quantum circuits; (2) an automatic solver that cuts complex quantum circuits to fit on less powerful QPUs; and (3) a tensor network based post-processing that minimizes the classical overhead. Our experiments demonstrate both QPU requirement advantages over the purely quantum platforms, and runtime advantages over the purely classical platforms for benchmarks up to 1000 qubits.
