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 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.

arXiv:2411.17756Nov 25, 20240 citations

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

Mitsuhiro Matsumoto, Shinichiro Sanji, Takahiko Satoh

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.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.

quant-pharXiv:2603.01443Mar 3, 20260 citations

Scalable Circuit Cutting and Scheduling in a Resource-Constrained and Distributed Quantum System

Shuwen Kan, Zefan Du, Miguel Palma +6 more

Despite rapid developments in quantum computing, current systems remain limited in practical applications due to their constrained qubit counts and quality. Technologies such as superconducting, trapped ions, and neutral atom quantum computing are progressing towards fault tolerance. However, they face challenges in scalability and control. Recent efforts have concentrated on multi-node quantum systems that connect smaller quantum devices to execute larger circuits. Future demonstrations aim to utilize quantum channels for system coupling, but current methods often resort to classical communication with circuit cutting techniques. This involves dividing large circuits into smaller subcircuits and reconstructing them after execution. Existing cutting methods face challenges such as lengthy search times with increasing numbers of qubits and gates. Moreover, they often struggle to efficiently use resources across various worker configurations in a multi-node system. To address these challenges, we propose FitCut, a novel approach that transforms quantum circuits into weighted graphs. FitCut employs a community-based, bottom-up approach to cut circuits based on resource constraints such as qubit counts on each worker. Additionally, it includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms existing tools such as Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 388% on the worker side, leading to a system-wide improvement of 286% in accumulated circuit depth.Despite rapid developments in quantum computing, current systems remain limited in practical applications due to their constrained qubit counts and quality. Technologies such as superconducting, trapped ions, and neutral atom quantum computing are progressing towards fault tolerance. However, they face challenges in scalability and control. Recent efforts have concentrated on multi-node quantum systems that connect smaller quantum devices to execute larger circuits. Future demonstrations aim to utilize quantum channels for system coupling, but current methods often resort to classical communication with circuit cutting techniques. This involves dividing large circuits into smaller subcircuits and reconstructing them after execution. Existing cutting methods face challenges such as lengthy search times with increasing numbers of qubits and gates. Moreover, they often struggle to efficiently use resources across various worker configurations in a multi-node system. To address these challenges, we propose FitCut, a novel approach that transforms quantum circuits into weighted graphs. FitCut employs a community-based, bottom-up approach to cut circuits based on resource constraints such as qubit counts on each worker. Additionally, it includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms existing tools such as Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 388% on the worker side, leading to a system-wide improvement of 286% in accumulated circuit depth.

arXiv:2405.04514IEEE International Conference on Quantum Computing and Engineering (QCE)Sep 15, 202416 citations

Evaluating Quantum Wire Cutting for QAOA: Performance Benchmarks in Ideal and Noisy Environments

Michel Meulen, Niels M. P. Neumann, Jasper Verbree

Current quantum computers suffer from a limited number of qubits and high error rates, limiting practical applicability. Different techniques exist to mitigate these effects and run larger algorithms. In this work, we analyze one of these techniques called quantum circuit cutting. With circuit cutting, a quantum circuit is decomposed into smaller sub-circuits, each of which can be run on smaller quantum hardware. We compare the performance of quantum circuit cutting with different cutting strategies, and then apply circuit cutting to a QAOA algorithm. Using simulations, we first show that Randomized Clifford measurements outperform both Pauli and random unitary measurements. Second, we show that circuit cutting has trouble providing correct answers in noisy settings, especially as the number of circuits increases.Current quantum computers suffer from a limited number of qubits and high error rates, limiting practical applicability. Different techniques exist to mitigate these effects and run larger algorithms. In this work, we analyze one of these techniques called quantum circuit cutting. With circuit cutting, a quantum circuit is decomposed into smaller sub-circuits, each of which can be run on smaller quantum hardware. We compare the performance of quantum circuit cutting with different cutting strategies, and then apply circuit cutting to a QAOA algorithm. Using simulations, we first show that Randomized Clifford measurements outperform both Pauli and random unitary measurements. Second, we show that circuit cutting has trouble providing correct answers in noisy settings, especially as the number of circuits increases.

quant-pharXiv:2602.03482Feb 4, 20260 citations

CutQC: using small Quantum computers for large Quantum circuit evaluations

Wei Tang, Teague Tomesh, Martin Suchara +2 more

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.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.

arXiv:2012.02333International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)Apr 11, 2021174 citations

Reducing Maximum Subcircuits Depth in Quantum Circuit Cutting

Milad Eslaminia, Sébastien Le Beux

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.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.

IEEE Transactions on Quantum EngineeringJan 1, 20260 citations

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.