Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Communications in computer and information science

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

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

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-13852-1_29

Related papers

QAOA Performance Benchmarks using Max-Cut

Jonathan Ward, Johannes Otterbach, Gavin E. Crooks +2 more

Bulletin of the American Physical Society•Mar 8, 2018•5 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.

quant-pharXiv:2602.03482•Feb 4, 2026•0 citations

Benchmarking Metaheuristic-Integrated QAOA Against Quantum Annealing

Arul Rhik Mazumder, Anuvab Sen, Udayon Sen

arXiv:2309.16796•Lecture notes in networks and systems•Jan 1, 2024•6 citations

Patterns for Quantum Circuit Cutting

Conference on Pattern Languages of Programs•Jun 24, 2024•0 citations

Hybrid Quantum-HPC Solutions for Max-Cut: Bridging Classical and Quantum Algorithms

Ishan Patwardhan, Akhil Akkapelli

This research explores the integration of the Quantum Approximate Optimization Algorithm (QAOA) into Hybrid Quantum-HPC systems for solving the Max-Cut problem, comparing its performance with classical algorithms like brute-force search and greedy heuristics. We develop a theoretical model to analyze the time complexity, scalability, and communication overhead in hybrid systems. Using simulations, we evaluate QAOA's performance on small-scale Max-Cut instances, benchmarking its runtime, solution accuracy, and resource utilization. The study also investigates the scalability of QAOA with increasing problem size, offering insights into its potential advantages over classical methods for large-scale combinatorial optimization problems, with implications for future Quantum computing applications in HPC environments.

arXiv:2410.15626•Oct 21, 2024•0 citations

Quantifying Performance of Wire-Based Quantum Circuit Cutting with Entanglements

Shiplu Sarker, Wenyang Qian, Soham Pal +5 more

Circuit cutting is a promising technique for mapping a quantum circuit requiring many qubits to a series of circuits requiring fewer qubits. At the cost of a longer runtime, such a strategy can reduce qubit requirements and mitigate noise in currently available Noisy Intermediate-Scale Quantum (NISQ) devices where noise scales poorly with greater levels of entanglement. In this work, we investigate the performance of wire-based circuit cutting in cases of different levels of entanglement and forms of noise. As a broadly applicable starting point, we focus on cutting Hamiltonian-inspired quantum circuits with various entanglement strategies and circuit depths. We provide model calculations of these circuits before and after cutting, both in the ideal case and in the presence of noise, using quantum simulators with quantum noise models. We present a numerical analysis of the performance of circuit cutting using various metrics for comparing ideal and noisy model calculations. Our results indicate that the efficacy of circuit cutting is sensitive to the level of entanglements and is affected differently by incoherent quantum noises.

IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum•May 27, 2024•1 citation
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.
This research explores the integration of the Quantum Approximate Optimization Algorithm (QAOA) into Hybrid Quantum-HPC systems for solving the Max-Cut problem, comparing its performance with classical algorithms like brute-force search and greedy heuristics. We develop a theoretical model to analyze the time complexity, scalability, and communication overhead in hybrid systems. Using simulations, we evaluate QAOA's performance on small-scale Max-Cut instances, benchmarking its runtime, solution accuracy, and resource utilization. The study also investigates the scalability of QAOA with increasing problem size, offering insights into its potential advantages over classical methods for large-scale combinatorial optimization problems, with implications for future Quantum computing applications in HPC environments.
Circuit cutting is a promising technique for mapping a quantum circuit requiring many qubits to a series of circuits requiring fewer qubits. At the cost of a longer runtime, such a strategy can reduce qubit requirements and mitigate noise in currently available Noisy Intermediate-Scale Quantum (NISQ) devices where noise scales poorly with greater levels of entanglement. In this work, we investigate the performance of wire-based circuit cutting in cases of different levels of entanglement and forms of noise. As a broadly applicable starting point, we focus on cutting Hamiltonian-inspired quantum circuits with various entanglement strategies and circuit depths. We provide model calculations of these circuits before and after cutting, both in the ideal case and in the presence of noise, using quantum simulators with quantum noise models. We present a numerical analysis of the performance of circuit cutting using various metrics for comparing ideal and noisy model calculations. Our results indicate that the efficacy of circuit cutting is sensitive to the level of entanglements and is affected differently by incoherent quantum noises.