TensorQC: Towards Scalable Distributed Quantum Computing via Tensor Networks

2025-02-05

AI Summary

In distributed quantum computing, circuit cutting incurs classical post-processing overhead that grows exponentially with the number of cuts, while quantum no-cloning constraints prevent direct replication of quantum states. To address this, we propose a novel classical tensor network–based framework for circuit cutting post-processing. This work is the first to systematically integrate tensor network contraction algorithms into quantum circuit cutting, enabling hybrid quantum-classical co-execution and GPU-accelerated tensor operations. Our approach supports parallelized execution across multiple quantum processing units (QPUs) and efficient result reconstruction using only a single GPU and existing QPUs. Evaluated on six real-world benchmarks, it reduces required QPU scale and fidelity requirements by over an order of magnitude, overcoming the scalability bottleneck of prior methods. For the first time, it successfully executes quantum computations previously infeasible due to prohibitive classical post-processing costs.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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 $10 imes$ over purely quantum platforms.

Problem

• Scalable distributed quantum computing
• Exponential classical co-processing cost
• Quantum circuit cutting techniques

Innovation

• TensorQC leverages tensor networks
• Reduces QPU size and quality
• Exponential runtime advantage achieved