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arXiv:2603.01443

Applicability and Limitations of Quantum Circuit Cutting in Classical State-Vector Simulation

Mitsuhiro Matsumoto, Shinichiro Sanji, Takahiko Satoh

quant-phSubmitted Mar 2, 2026• Announced Mar 3, 2026• 0 citations
arXivHTMLPDF

Abstract

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 $q \approx 18$ and $q \approx 22$ 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.

Comments
12 pages, 9 figures, 2 tables, 3 algorithms

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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.
Circuit cutting allows quantum circuits larger than the available hardware to be executed. Cutting techniques split circuits into smaller subcircuits, run them on the hardware, and recombine results through classical post-processing. Circuit cutting techniques have been extensively researched over the last five years and it been adopted by quantum companies, like IBM, as part of their scaling roadmap. We examine whether current circuit cutting techniques are practical for orchestrating executions on fault-tolerant quantum computers. We conduct a resource estimation-based benchmarking of important quantum applications and different types of circuit cutting techniques. Our applications include practically relevant algorithms such as Hamiltonian simulation, kernels such as quantum Fourier transform and more. To cut these applications, we use IBM's Qiskit cutting tool. We estimate resources for subcircuits using Microsoft's Azure Quantum Resource Estimator and develop models to determine qubit counts and the quantum and classical runtime needs of circuit cutting. We demonstrate that while circuit cutting works for small-scale systems, the exponential growth of the quantum excution runtime and the classical post-processing overhead as the qubit count increases renders it impractical for larger quantum systems with current implementation strategies. As we transition from noisy quantum hardware to fault-tolerance, our work provides useful guidance for the design of quantum software and runtime systems.
Circuit cutting allows quantum circuits larger than the available hardware to be executed. Cutting techniques split circuits into smaller subcircuits, run them on the hardware, and recombine results through classical post-processing. Circuit cutting techniques have been extensively researched over the last five years and it been adopted by major quantum hardware vendors as part of their scaling roadmaps. We examine whether current circuit cutting techniques are practical for orchestrating executions on fault-tolerant quantum computers. We conduct a resource estimation-based benchmarking of important quantum applications and different types of circuit cutting techniques. Our applications include practically relevant algorithms, such as Hamiltonian simulation, kernels such as quantum Fourier transform and more. To cut these applications, we use IBM's Qiskit cutting tool. We estimate resources for subcircuits using Microsoft's Azure Quantum Resource Estimator and develop models to determine the qubit, quantum and classical runtime needs of circuit cutting. We demonstrate that while circuit cutting works for small-scale systems, the exponential growth of the quantum runtime and the classical post-processing overhead as the qubit count increases renders it impractical for larger quantum systems with current implementation strategies. As we transition from noisy quantum hardware to fault-tolerance, our work provides important guidance for the design of quantum software and runtime systems.
Circuit cutting is a technique for simulating large quantum circuits by partitioning them into smaller subcircuits, which can be executed on smaller quantum devices. The results from these subcircuits are then combined in classical postprocessing to accurately reconstruct the expectation value of the original circuit. Circuit cutting introduces a sampling overhead that grows exponentially with the number of gates and qubit wires that are cut. Many recently developed quasiprobabilistic circuit cutting techniques leverage classical side information, obtained from intermediate measurements within the subcircuits, to enhance the postprocessing step. In this work, we provide a formalization of general circuit cutting techniques utilizing side information through quantum instruments. With this framework, we analyze the advantage that classical side information provides in reducing the sampling overhead of circuit cutting. Surprisingly, we find that in certain scenarios, side information does not yield any reduction in sampling overhead, whereas in others it is essential for circuit cutting to be feasible at all. Furthermore, we present a lower bound for the optimal sampling overhead with side information that can be evaluated efficiently via semidefinite programming and improves on all previously known lower bounds.
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.
A large-scale quantum circuit can be partitioned into multiple subcircuits through circuit cutting, where each subcircuit is executed multiple times and the expectation value of the original circuit is reconstructed by classical post-processing from their measurement (sampling) results. In this process, appropriate cut locations are identified after the user-designed quantum circuit, including multi-qubit gates that act on three or more qubits, has been decomposed into single-qubit gates and two-qubit gates such as the CNOT gate. Here, we present a method for reducing the sampling overhead, which refers to the increase in the number of samples required due to the cutting process, by modifying the decomposition strategy of multi-qubit gates. Using MCX and CCCX gates as representatives of multi-qubit gates, we demonstrate that the proposed decomposition method, which introduces a small number of ancilla qubits according to the identified cut locations, effectively decreases the sampling overhead.