TensorQC: Towards Scalable Distributed Quantum Computing via Tensor Networks

TL;DR

TensorQC introduces a tensor network-based approach to enable scalable distributed quantum computing, significantly reducing hardware requirements and computational costs compared to existing methods.

Contribution

This work presents TensorQC, a novel method leveraging tensor networks to improve the efficiency and scalability of distributed quantum circuit processing.

Findings

Achieves exponential runtime advantage over prior techniques

Enables running large benchmarks on current QPUs with a single GPU

Reduces quantum hardware requirements by over 10 times

Abstract

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.