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arXiv:2502.03445

TensorQC: Towards Scalable Distributed Quantum Computing via Tensor Networks

Wei Tang, Margaret Martonosi

Published Feb 5, 2025• 0 citations
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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.

DOI
10.48550/arxiv.2502.03445

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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 10×10\times10× over purely quantum platforms.
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.
Quantum computing (QC) offers a new computing paradigm that has the potential to provide significant speedups over classical computing. Each additional qubit doubles the size of the computational state space available to a quantum algorithm. Such exponentially expanding reach underlies QC's power, but at the same time puts demanding requirements on the quantum processing units (QPU) hardware. On the other hand, purely classical simulations of quantum circuits on either central processing unit (CPU) or graphics processing unit (GPU) scale poorly as they quickly become bottlenecked by runtime and memory. This paper introduces CutQC, a scalable hybrid computing approach that distributes a large quantum circuit onto quantum (QPU) and classical platforms (CPU or GPU) for co-processing. CutQC demonstrates evaluation of quantum circuits that are larger than the limit of QPU or classical simulation, and achieves much higher quantum circuit evaluation fidelity than the large NISQ devices achieve in real-system runs.
Most quantum computers today are constrained by hardware limitations, particularly the number of available qubits, causing significant challenges for executing large-scale quantum algorithms. Circuit cutting has emerged as a key technique to overcome these limitations by decomposing large quantum circuits into smaller subcircuits that can be executed independently and later reconstructed. In this work, we introduce Qdislib, a distributed and flexible library for quantum circuit cutting, designed to seamlessly integrate with hybrid quantum-classical high-performance computing (HPC) systems. Qdislib employs a graph-based representation of quantum circuits to enable efficient partitioning, manipulation and execution, supporting both wire cutting and gate cutting techniques. The library is compatible with multiple quantum computing libraries, including Qiskit and Qibo, and leverages distributed computing frameworks to execute subcircuits across CPUs, GPUs, and quantum processing units (QPUs) in a fully parallelized manner. We present a proof of concept demonstrating how Qdislib enables the distributed execution of quantum circuits across heterogeneous computing resources, showcasing its potential for scalable quantum-classical workflows.
Today's Quantum Computers (QCs) face significant engineering challenges that limit their size and fidelity. To execute realistic applications, researchers must develop ways to move past these constraints. CutQC enables small QCs to run larger quantum circuits by cutting circuits into smaller subcircuits and then utilizing classical resources and Kronecker products to reproduce the desired output. In this work, we enhance the reconstruction process. In particular, we develop a distributed PyTorch implementation of CutQC's classical post-processing step, which can run across multiple nodes on either GPU or CPU devices. Our results show that our PyTorch implementation executes circuits of up to 35 qubits on 10- and 15-qubit QCs with efficient reconstruction. We use single-node computation as a baseline and then compare the results to the runtimes of our PyTorch workflows. By utilizing parallelism and specified devices, the reconstruction step of hybrid quantum-classical circuit execution can be improved. The original CutQC paper demonstrated a 60X to 8600X runtime speedup over classical simulation, and our work increases this speedup. These results show that large quantum circuits can be run on smaller QCs by leveraging circuit cutting with data parallelism techniques to maintain a reasonable runtime. This allows researchers to maintain the high fidelity of smaller machines while executing larger circuits.
Distributing quantum workloads over many Quantum Processing Units (QPUs) is a crucial step in scaling up quantum computers toward practical quantum advantage due to the limitations in size of a single QPU. In the absence of high-fidelity quantum interconnects, circuit knitting could provide a path to computing certain properties of large quantum systems on many QPUs of limited size in a distributed fashion using only classical communication. Circuit knitting partitions large quantum circuits into manageable sub-circuits, however, reconstructing observables in a straightforward manner comes at an exponential cost in sampling and classical post-processing. To mitigate the overhead this technique incurs, we introduce an Adaptive Circuit Knitting (ACK) method that finds efficient partitions of quantum circuits by discovering regions of minimal entanglement between subsystems. We simulate 1D and 2D disordered mixed-field Ising models up to 60 qubits and show that the ACK approach can reduce circuit knitting sampling overheads by up to four orders of magnitude for observables of interest. We highlight our parallel GPU-accelerated implementation and discuss the need for efficient classical simulators to enable distributed quantum algorithm development. Our techniques could enable efficient distribution of quantum simulation for both near-term and fault-tolerant architectures.
Most quantum computers today are constrained by hardware limitations, particularly the number of available qubits, causing significant challenges for executing large-scale quantum algorithms. Circuit cutting has emerged as a key technique to overcome these limitations by decomposing large quantum circuits into smaller subcircuits that can be executed independently and later reconstructed. In this work, we introduce Qdislib, a distributed and flexible library for quantum circuit cutting, designed to seamlessly integrate with hybrid quantum-classical high-performance computing (HPC) systems. Qdislib employs a graph-based representation of quantum circuits to enable efficient partitioning, manipulation and execution, supporting both wire cutting and gate cutting techniques. The library is compatible with multiple quantum computing libraries, including Qiskit and Qibo, and leverages distributed computing frameworks to execute subcircuits across CPUs, GPUs, and quantum processing units (QPUs) in a fully parallelized manner. We present a proof of concept demonstrating how Qdislib enables the distributed execution of quantum circuits across heterogeneous computing resources, showcasing its potential for scalable quantum-classical workflows.