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

arXiv:2606.20968 · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

Profiling the Effective Limits of Error Mitigation via Circuit Replication

Jeremie Pope, Swaroop Ghosh

quant-phSubmitted Jun 18, 2026• Announced Jun 22, 2026• 0 citations
arXivHTMLPDF

Abstract

Current era quantum computers continue to grow in both capability and capacity. Despite these advancements, errors induced by environmental noise severely limit practical applicability. Current research into error mitigation and correction to bridge the gap between current-era quantum computers and the execution of noise-sensitive workloads. These methods have significant performance and resource overheads, thereby greatly limiting the real-world benefits of their use. Circuit replication, as a naive form of error mitigation, is not new and has largely been ignored given the resource constraints of current quantum hardware. However, its simplicity is attractive as a means to supplement modern methods, reducing the overall performance overhead while still preserving error-mitigation capabilities. In this paper, we profile the effects of simple circuit replication under real-world noise profiles to better establish replication's limits as a supplemental mitigation strategy. Quantum Approximate Optimization Algorithm (QAOA) for the Maxcut problem is explored for the analysis. For small graphs, we found that the average inference strength decreases by approximately 21.8% while the average standard deviation decreases by 108.8% compared to 6 replicates. For larger graphs, inference strength decreases by 35.4% while the average standard deviation decreased only 20.5%. Fewer replications did not affect smaller graphs, but degraded inference strength, with comparable benefits to standard deviation in larger graphs. These results show that replication has potential uses as a supplemental mitigation strategy for large-depth, highly variable workloads.

Related papers

Error Mitigation–Driven Enhancement of QAOA Performance on NISQ Devices

Kondreddy Sathwika, Maka Sri Varshith, Marati Nanda Kumar +1 more

The Quantum Approximate Optimization Algorithm (QAOA) is widely regarded as a promising near-term quantum algorithm for addressing hard combinatorial optimization problems on Noisy Intermediate-Scale Quantum (NISQ) devices; however, its real-world effectiveness is severely constrained by hardware noise, including gate infidelities, decoherence, and measurement errors. This work explores an error mitigation–driven approach to enhance the performance and robustness of QAOA without altering its algorithmic structure or requiring fault-tolerant quantum resources. Measurement error mitigation and zero-noise extrapolation are incorporated into the execution and post-processing stages of QAOA circuits and evaluated using the Minimum Vertex Cover problem as a representative benchmark. Performance is assessed through approximation ratios, solution stability, and noise sensitivity across multiple circuit depths. The results indicate that error mitigation not only improves approximation accuracy but also stabilizes output distributions and delays performance degradation at increased circuit depths. Additionally, the mitigated executions demonstrate better consistency across repeated runs, highlighting improved resilience to stochastic noise. These findings emphasize that lightweight and hardware-efficient error mitigation techniques can significantly extend the practical usability of QAOA on current NISQ hardware, providing a scalable and realistic pathway for executing quantum optimization algorithms more reliably in near-term quantum computing environments.

International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC)•Jan 19, 2026•0 citations

The Impact of Noise on Quantum Adder Circuits: An IBM Quantum Case Study

Jefferson Rice, David H. K. Hoe

Quantum computers are able to efficiently solve certain problems that are very computationally demanding or even intractable on classical computers. With recent advances in quantum computing hardware, we are entering an exciting era where it is becoming possible to execute ‘useful’ quantum algorithms. However, quantum computers will continue to be noisy for the foreseeable future. In this paper, quantum adder circuits are used to evaluate three simple error mitigation methods: (1) error correction through the use of replicated circuits and a voter circuit, (2) error detection by encoding, which involves expanding the qubit space and partitioning it into orthogonal subspaces consisting of correct and incorrect code words, and (3) error mitigation by reducing circuit depth at the expense of increasing the number of qubits. These three mitigation methods are what we dub replication, encoding, and simplification, respectively. We find simplification to be most effective for implementing the simple quantum adders on the IBM Quantum Experience, while replication increases the gate count and the error count on current quantum devices. The encoding methods used in this study proved to be not effective for even the simple adder circuits that were implemented, suggesting the need to study and evaluate more sophisticated error correcting schemes.

IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)•Aug 11, 2024•1 citation

A Fidelity-Aware Framework for Joint Circuit Knitting and Error Mitigation

Matteo Mendula, Deborah Volpe, Fabrizio Riente +2 more

Executing quantum circuits on current quantum Best Cut Identification devices is fundamentally limited by noise, decoherence, and hardware constraints. Circuit Knitting (CK) techniques enable the execution of larger circuits by decomposing them into smaller sub-circuits, at the cost of reconstruction overhead. At the same time, Quantum Error Mitigation (QEM) methods improve fidelity but introduce additional complexity. In this work, we propose a fidelity-aware framework that jointly optimizes circuit partitioning and error mitigation to maximize end-to-end execution fidelity. We introduce a unified model that captures the trade-off between sub-circuit operational fidelity, reconstruction penalties induced by cuts, and the impact of error mitigation. Based on this model, we formulate the cut placement problem as an optimization task and solve it to identify fidelity-optimal partitioning strategies accounting for error mitigation and under realistic hardware constraints. Notably, our framework integrates calibration-driven noise models and supports practical fidelity estimation using backend-specific parameters. We validate the proposed method on both noisy simulators (IBM Fake Backends) and real quantum hardware (IQM Lagrange), proving consistent improvements in execution fidelity with gains of up to 40% over standard circuit execution.

•Jan 1, 2026•0 citations

Error-Mitigated Hamiltonian Simulation: Complexity Analysis and Optimization for Near-Term and Early-Fault-Tolerant Quantum Computers

Keisuke Murota, Synge Todo, Suguru Endo

Simulating real-time dynamics under a Hamiltonian is a central goal of quantum information science. While numerous Hamiltonian-simulation quantum algorithms have been proposed, the effects of physical noise have rarely been incorporated into their performance analysis, despite the non-negligible noise levels of quantum devices. We present an end-to-end complexity analysis of noisy Hamiltonian simulation combined with quantum error mitigation (QEM) to answer how many circuit runs are required to reach a given target accuracy. Because the QEM sampling overhead grows exponentially with the circuit depth while the algorithmic error decreases with it, the circuit depth becomes an optimization variable, and we derive an analytic depth-selection rule for two algorithm families. For the order-$k$ Suzuki--Trotter formula, the optimized cost exhibits a critical error $ε_c$, below which the required number of circuit runs grows exponentially. QEM improves the noise dependence of $ε_c$ from sublinear to $k$th-power scaling, an exponent improvement by a factor of $k+1$. For randomized-LCU-based simulation, optimizing the repetition number yields a square-root improvement in the simulation-time dependence of the sampling-overhead exponent over the standard parameter choice. We further quantify the noise-characterization cost required for error mitigation via gate set tomography and the recently proposed space-time noise inversion method, showing that the latter can significantly reduce this cost.

quant-pharXiv:2603.11527•Mar 13, 2026•0 citations

Quantum error mitigations for quantum approximate optimization algorithms on IBM quantum processors

Sixian Liu, Hyunkyung Lim, Jia Choi +3 more

In recent years, there have been significant advancements in various aspects of quantum computing. However, despite this substantial progress, the availability of fault-tolerant quantum computers is still out of reach and may remain so for decades. Therefore, a key challenge is to leverage current NISQ devices to achieve a quantum advantage effectively. In this context, the Quantum Approximate Optimization Algorithm (QAOA) was proposed to potentially demonstrate computational advantages in combinatorial optimization problems using NISQ computers. Meanwhile, quantum error mitigation (QEM) techniques have been developed to address errors, with their effectiveness validated in practical problems involving more than 100 qubits. Therefore, in this paper, we optimize QAOA circuits and apply various error mitigation methods, such as dynamic decoupling and Pauli-twirling, to scale problem sizes on IBM quantum processors. Additionally, we discuss optimal implementation strategies for scalable QAOA. We test our implementations on Max-Cut problems and compare our results with previous works.

Quantum Communications and Quantum Imaging•Oct 4, 2024•0 citations

Error Mitigation in Dynamic Circuits for Hamiltonian Simulation

Sumeet Shirgure, Siyuan Niu

Dynamic quantum circuits integrate mid-circuit measurements and feed-forward operations to enable real-time classical processing and conditional quantum logic. These capabilities are central to key quantum protocols such as quantum error correction, and have recently demonstrated significant potential for reducing quantum resources, including circuit depth and gate count, across a range of applications. However, executing dynamic circuits on real quantum hardware introduces a critical trade-off: while resource requirements decrease, circuit fidelity degrades due to high error rates of mid-circuit measurements, as well as the decoherence errors accumulated during the extended idle periods introduced by both mid-circuit measurements and feed-forward operations. In this paper, we systematically investigate the impact of standard error mitigation techniques on dynamic circuit applications pertaining to Hamiltonian simulation and ground state estimation of physically relevant systems like the Heisenberg model. We explore dynamical decoupling (DD) as a strategy to suppress decoherence and crosstalk errors during idle windows introduced by mid-circuit measurements and feed-forward delays, and also examine error mitigation via zero-noise extrapolation (ZNE). Through experiments conducted on IBM quantum hardware, we benchmark effective combinations of these strategies that maximize the practical benefits of dynamic quantum circuits in these applications. We demonstrate that a combination of DD and ZNE is effective in mitigating the errors introduced during mid-circuit measurements and feed-forward operations, as well as the errors arising from faulty measurements. This approach yields a energy gap improvement of at least 60% in ground state estimation and reduces observed error of time-evolved states by up to 99% for the Ising model and up to 20% for the Heisenberg model.

quant-pharXiv:2605.05256•ACM Great Lakes Symposium on VLSI (GLSVLSI)•May 7, 2026•0 citations
Current era quantum computers continue to grow in both capability and capacity. Despite these advancements, errors induced by environmental noise severely limit practical applicability. Current research into error mitigation and correction to bridge the gap between current-era quantum computers and the execution of noise-sensitive workloads. These methods have significant performance and resource overheads, thereby greatly limiting the real-world benefits of their use. Circuit replication, as a naive form of error mitigation, is not new and has largely been ignored given the resource constraints of current quantum hardware. However, its simplicity is attractive as a means to supplement modern methods, reducing the overall performance overhead while still preserving error-mitigation capabilities. In this paper, we profile the effects of simple circuit replication under real-world noise profiles to better establish replication's limits as a supplemental mitigation strategy.
Quantum Approximate Optimization Algorithm (QAOA) for the Maxcut problem is explored for the analysis. For small graphs, we found that the average inference strength decreases by approximately 21.8% while the average standard deviation decreases by 108.8% compared to 6 replicates. For larger graphs, inference strength decreases by 35.4% while the average standard deviation decreased only 20.5%. Fewer replications did not affect smaller graphs, but degraded inference strength, with comparable benefits to standard deviation in larger graphs. These results show that replication has potential uses as a supplemental mitigation strategy for large-depth, highly variable workloads.
The Quantum Approximate Optimization Algorithm (QAOA) is widely regarded as a promising near-term quantum algorithm for addressing hard combinatorial optimization problems on Noisy Intermediate-Scale Quantum (NISQ) devices; however, its real-world effectiveness is severely constrained by hardware noise, including gate infidelities, decoherence, and measurement errors. This work explores an error mitigation–driven approach to enhance the performance and robustness of QAOA without altering its algorithmic structure or requiring fault-tolerant quantum resources. Measurement error mitigation and zero-noise extrapolation are incorporated into the execution and post-processing stages of QAOA circuits and evaluated using the Minimum Vertex Cover problem as a representative benchmark. Performance is assessed through approximation ratios, solution stability, and noise sensitivity across multiple circuit depths. The results indicate that error mitigation not only improves approximation accuracy but also stabilizes output distributions and delays performance degradation at increased circuit depths. Additionally, the mitigated executions demonstrate better consistency across repeated runs, highlighting improved resilience to stochastic noise. These findings emphasize that lightweight and hardware-efficient error mitigation techniques can significantly extend the practical usability of QAOA on current NISQ hardware, providing a scalable and realistic pathway for executing quantum optimization algorithms more reliably in near-term quantum computing environments.
Quantum computers are able to efficiently solve certain problems that are very computationally demanding or even intractable on classical computers. With recent advances in quantum computing hardware, we are entering an exciting era where it is becoming possible to execute ‘useful’ quantum algorithms. However, quantum computers will continue to be noisy for the foreseeable future. In this paper, quantum adder circuits are used to evaluate three simple error mitigation methods: (1) error correction through the use of replicated circuits and a voter circuit, (2) error detection by encoding, which involves expanding the qubit space and partitioning it into orthogonal subspaces consisting of correct and incorrect code words, and (3) error mitigation by reducing circuit depth at the expense of increasing the number of qubits. These three mitigation methods are what we dub replication, encoding, and simplification, respectively. We find simplification to be most effective for implementing the simple quantum adders on the IBM Quantum Experience, while replication increases the gate count and the error count on current quantum devices. The encoding methods used in this study proved to be not effective for even the simple adder circuits that were implemented, suggesting the need to study and evaluate more sophisticated error correcting schemes.
Executing quantum circuits on current quantum Best Cut Identification devices is fundamentally limited by noise, decoherence, and hardware constraints. Circuit Knitting (CK) techniques enable the execution of larger circuits by decomposing them into smaller sub-circuits, at the cost of reconstruction overhead. At the same time, Quantum Error Mitigation (QEM) methods improve fidelity but introduce additional complexity. In this work, we propose a fidelity-aware framework that jointly optimizes circuit partitioning and error mitigation to maximize end-to-end execution fidelity. We introduce a unified model that captures the trade-off between sub-circuit operational fidelity, reconstruction penalties induced by cuts, and the impact of error mitigation. Based on this model, we formulate the cut placement problem as an optimization task and solve it to identify fidelity-optimal partitioning strategies accounting for error mitigation and under realistic hardware constraints. Notably, our framework integrates calibration-driven noise models and supports practical fidelity estimation using backend-specific parameters. We validate the proposed method on both noisy simulators (IBM Fake Backends) and real quantum hardware (IQM Lagrange), proving consistent improvements in execution fidelity with gains of up to 40% over standard circuit execution.
Simulating real-time dynamics under a Hamiltonian is a central goal of quantum information science. While numerous Hamiltonian-simulation quantum algorithms have been proposed, the effects of physical noise have rarely been incorporated into their performance analysis, despite the non-negligible noise levels of quantum devices. We present an end-to-end complexity analysis of noisy Hamiltonian simulation combined with quantum error mitigation (QEM) to answer how many circuit runs are required to reach a given target accuracy. Because the QEM sampling overhead grows exponentially with the circuit depth while the algorithmic error decreases with it, the circuit depth becomes an optimization variable, and we derive an analytic depth-selection rule for two algorithm families. For the order-kkk Suzuki--Trotter formula, the optimized cost exhibits a critical error εcε_cεc​, below which the required number of circuit runs grows exponentially. QEM improves the noise dependence of εcε_cεc​ from sublinear to kkkth-power scaling, an exponent improvement by a factor of k+1k+1k+1. For randomized-LCU-based simulation, optimizing the repetition number yields a square-root improvement in the simulation-time dependence of the sampling-overhead exponent over the standard parameter choice. We further quantify the noise-characterization cost required for error mitigation via gate set tomography and the recently proposed space-time noise inversion method, showing that the latter can significantly reduce this cost.
In recent years, there have been significant advancements in various aspects of quantum computing. However, despite this substantial progress, the availability of fault-tolerant quantum computers is still out of reach and may remain so for decades. Therefore, a key challenge is to leverage current NISQ devices to achieve a quantum advantage effectively. In this context, the Quantum Approximate Optimization Algorithm (QAOA) was proposed to potentially demonstrate computational advantages in combinatorial optimization problems using NISQ computers. Meanwhile, quantum error mitigation (QEM) techniques have been developed to address errors, with their effectiveness validated in practical problems involving more than 100 qubits. Therefore, in this paper, we optimize QAOA circuits and apply various error mitigation methods, such as dynamic decoupling and Pauli-twirling, to scale problem sizes on IBM quantum processors. Additionally, we discuss optimal implementation strategies for scalable QAOA. We test our implementations on Max-Cut problems and compare our results with previous works.
Dynamic quantum circuits integrate mid-circuit measurements and feed-forward operations to enable real-time classical processing and conditional quantum logic. These capabilities are central to key quantum protocols such as quantum error correction, and have recently demonstrated significant potential for reducing quantum resources, including circuit depth and gate count, across a range of applications. However, executing dynamic circuits on real quantum hardware introduces a critical trade-off: while resource requirements decrease, circuit fidelity degrades due to high error rates of mid-circuit measurements, as well as the decoherence errors accumulated during the extended idle periods introduced by both mid-circuit measurements and feed-forward operations. In this paper, we systematically investigate the impact of standard error mitigation techniques on dynamic circuit applications pertaining to Hamiltonian simulation and ground state estimation of physically relevant systems like the Heisenberg model. We explore dynamical decoupling (DD) as a strategy to suppress decoherence and crosstalk errors during idle windows introduced by mid-circuit measurements and feed-forward delays, and also examine error mitigation via zero-noise extrapolation (ZNE). Through experiments conducted on IBM quantum hardware, we benchmark effective combinations of these strategies that maximize the practical benefits of dynamic quantum circuits in these applications. We demonstrate that a combination of DD and ZNE is effective in mitigating the errors introduced during mid-circuit measurements and feed-forward operations, as well as the errors arising from faulty measurements. This approach yields a energy gap improvement of at least 60% in ground state estimation and reduces observed error of time-evolved states by up to 99% for the Ising model and up to 20% for the Heisenberg model.