TensorQC：通过张量网络实现可扩展的分布式量子计算
TensorQC: Towards Scalable Distributed Quantum Computing via Tensor Networks
AI总结：
TensorQC利用经典张量网络处理量子电路切割后的后处理，以指数级加速降低分布式混合量子计算成本，并在六个现实基准上使QPU规模与质量需求降低超过10倍。
摘要：
量子处理单元（QPU）必须包含大量高质量量子比特，才能在有用规模的问题上产生准确结果。相比之下，大多数科学和工业经典计算工作负载在分布式系统上并行发生，这些系统依赖于跨多个核心复制数据。不幸的是，由于量子不可克隆理论，复制量子数据在理论上是被禁止的。相反，量子电路切割技术将大型量子电路切割成多个较小的子电路，将子电路分布在并行的QPU上，并用经典计算重建结果。此类技术使分布式混合量子计算（DHQC）成为可能，但也引入了随切割数量呈指数增长的经典协同处理成本，并且很容易变得难以处理。本文提出了TensorQC，它利用经典张量网络，相较于最先进的并行化后处理技术带来了指数级的运行时间优势。因此，本文展示了在独立QPU和先前的电路切割技术下原本难以处理的基准测试运行。具体而言，本文使用当前可用的QPU和单个GPU运行了六个现实基准测试，并将QPU规模和量子比特质量要求相较于纯量子平台降低了超过10倍。
英文摘要：
A quantum processing unit (QPU) must contain a large number of high quality qubits to produce accurate results for problems at useful scales. In contrast, most scientific and industry classical computation workloads happen in parallel on distributed systems, which rely on copying data across multiple cores. Unfortunately, copying quantum data is theoretically prohibited due to the quantum non-cloning theory. Instead, quantum circuit cutting techniques cut a large quantum circuit into multiple smaller subcircuits, distribute the subcircuits on parallel QPUs and reconstruct the results with classical computing. Such techniques make distributed hybrid quantum computing (DHQC) a possibility but also introduce an exponential classical co-processing cost in the number of cuts and easily become intractable. This paper presents TensorQC, which leverages classical tensor networks to bring an exponential runtime advantage over state-of-the-art parallelization post-processing techniques. As a result, this paper demonstrates running benchmarks that are otherwise intractable for a standalone QPU and prior circuit cutting techniques. Specifically, this paper runs six realistic benchmarks using QPUs available nowadays and a single GPU, and reduces the QPU size and quality requirements by more than $10\times$ over purely quantum platforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
