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ACM Transactions on Design Automation of Electronic Systems

QCutSim: Accelerating Quantum Circuit Cutting Simulation on Consumer-Grade Classical Systems

Po-Hsuan Huang, Chun-Yen Tai, Chia-Heng Tu, Shih‐Hao Hung

Published Aug 8, 2026• 0 citations
Publisher

Abstract

Quantum computing has the potential to accelerate various fields by solving specific problems significantly faster than classical computers. Solving more complex problems generally requires a larger number of qubits. However, current quantum devices are constrained by limited qubit counts and environmental noise. Quantum circuit cutting bridges the gap between the theoretical requirements of large quantum circuits and the practical limitations of current quantum hardware by decomposing large circuits into smaller subcircuits. Tang et al. introduced CutQC, a framework that reduces the number of generated subcircuits and reconstructs the complete quantum state with limited memory consumption. Despite these advances, CutQC faces performance bottlenecks in classical postprocessing, leading to long execution times. To address this limitation, we propose QCutSim, an efficient simulation-based framework guided by insights from the postprocessing stage. It improves performance through optimized simulation and reconstruction strategies, along with computational optimizations such as vectorization and parallelization. QCutSim demonstrates that even consumer-grade systems can efficiently simulate 100-qubit circuits and achieves a speedup of up to 5.1x compared to prior work on the same hardware. In the worst-case scenario of reconstructing dense solution circuits, QCutSim achieves a speedup of 6 to 8 orders of magnitude.

DOI
10.1145/3833423

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Quantum computing has the potential to accelerate various fields by solving specific problems significantly faster than classical computers. Solving more complex problems generally requires a larger number of qubits. However, current quantum devices are constrained by limited qubit counts and environmental noise. Quantum circuit cutting bridges the gap between the theoretical requirements of large quantum circuits and the practical limitations of current quantum hardware by decomposing large circuits into smaller subcircuits. Tang et al. introduced CutQC, a framework that reduces the number of generated subcircuits and reconstructs the complete quantum state with limited memory consumption. Despite these advances, CutQC faces performance bottlenecks in classical postprocessing, leading to long execution times. To address this limitation, we propose QCutSim, an efficient simulation-based framework guided by insights from the postprocessing stage. It improves performance through optimized simulation and reconstruction strategies, along with computational optimizations such as vectorization and parallelization. QCutSim demonstrates that even consumer-grade systems can efficiently simulate 100-qubit circuits and achieves a speedup of up to 5.1x compared to prior work on the same hardware. In the worst-case scenario of reconstructing dense solution circuits, QCutSim achieves a speedup of 6 to 8 orders of magnitude.
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.
Quantum computing (QC) is a new paradigm offering the potential of exponential speedups over classical computing for certain computational problems. Each additional qubit doubles the size of the computational state space available to a QC algorithm. This exponential scaling underlies QC’s power, but today’s Noisy Intermediate-Scale Quantum (NISQ) devices face significant engineering challenges in scalability. The set of quantum circuits that can be reliably run on NISQ devices is limited by their noisy operations and low qubit counts. This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers and quantum computers to enable evaluation of quantum circuits that cannot be run on classical or quantum computers alone. CutQC cuts large quantum circuits into smaller subcircuits, allowing them to be executed on smaller quantum devices. Classical postprocessing can then reconstruct the output of the original circuit. This approach offers significant runtime speedup compared with the only viable current alternative—purely classical simulations—and demonstrates evaluation of quantum circuits that are larger than the limit of QC or classical simulation. Furthermore, in real-system runs, CutQC achieves much higher quantum circuit evaluation fidelity using small prototype quantum computers than the state-of-the-art large NISQ devices achieve. Overall, this hybrid approach allows users to leverage classical and quantum computing resources to evaluate quantum programs far beyond the reach of either one alone.
Circuit cutting partitions a large quantum circuit into smaller subcircuits that can be executed independently and recombined by classical post-processing. In classical state-vector simulation with full-state reconstruction, the runtime is governed by a trade-off between reduced subcircuit size and the overheads of exponentially many subcircuits and full-state reconstruction. For equal partitioning, we derive threshold conditions on the number of cuts below which cutting reduces the wall-clock time. State-vector experiments validate the predicted speedup boundary up to 24 qubits, and a runtime breakdown up to 30 qubits identifies crossovers at q≈18q \approx 18q≈18 and q≈22q \approx 22q≈22 where merging overtakes first preprocessing and then subcircuit simulation. As a practical guideline, we show that under a 10-minute wall-clock budget, two-way cutting extends the maximum feasible qubit count by 4 to 6 qubits relative to simulation without cutting.
Quantum computing has shown great potential in addressing traditional computational challenges, but due to its high error rates and noise issues, classical simulation has become an essential tool for verifying its performance. However, the superposition and entanglement properties of quantum systems pose significant challenges for simulation, especially when memory is limited. Although circuit cutting methods can decompose large-scale quantum circuits into smaller computational tasks to reduce computational load, previous research primarily focused on their application to quantum computers, without fully considering their effectiveness in quantum circuit simulation. This study fills that gap by proposing an optimization scheme based on a heuristic cutting algorithm and subcircuit state vector reuse to address memory limitations in simulations. By incorporating global computational cost considerations and an integer programming model, the heuristic method proposed in this paper not only optimizes the cutting process but also combines subcircuit state vector reuse to reduce redundant calculations and memory usage. Experimental results show that compared to current popular circuit cutting methods, the proposed approach significantly improves simulation speed while reducing memory requirements, effectively addressing the challenges in quantum circuit simulation. The overall average speedup achieved 46%.
Noisy intermediate-scale quantum (NISQ) devices with limited qubit count and connectivity limit the scale of quantum circuits that can be executed. Circuit cutting methods simulate larger quantum computers by decomposing large circuits into small subcircuits that can be executed on NISQ hardware. Existing circuit cutting methods only consider the qubit count of the target hardware for finding the locations of cuts on the circuits. This can result in deeper subcircuits after mapping on the hardware. Fidelity decays exponentially with circuit depth and diminishes the quality of execution. We propose a circuit-cutting framework that reduces variance in the depth of subcircuits after mapping to the hardware. Our method formulates the initial circuit cutting as a multi-objective optimization problem based on an evolutionary search method with the number of cuts and the maximum depth of individual routed subcircuits as fitness objectives. This method is implemented as an extension to the existing Qiskit Circuit Cutting Addon, which allows for integration in existing flows. Our approach achieves reductions in routed subcircuit depth of up to 9.9% across benchmark circuits compared with baseline methods.
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.