Reliable benchmarking of Quantum Error Mitigation (QEM) requires distinguishing genuine improvements from artefacts of the post-processing arithmetic. In this paper, we expose a failure mode in Richardson Zero-Noise Extrapolation (ZNE), a widely used technique routinely (and often implicitly) relied upon in benchmarks and experiments. When noise amplification operates beyond usable signals - a regime that is quickly reached on current hardware for non-trivial circuits - we show that the extrapolation no longer reflects the underlying physics, but collapses into a fixed rescaling of a single noisy measurement, producing a bogus apparent improvement that is independent of noise amplification. This poses a rarely considered threat to the validity of many empirical evaluations in quantum computing.
Measurements on real hardware (IQM Euro-Q-Exa) confirm this collapse with ordinary folding alone: as circuit depth erodes the signal, the reported estimate decouples from the truth and overshoots the ideal by up to 21%. We further introduce a matched-cost "garbage-folding" negative control that carries no usable signal yet reports a larger apparent improvement than genuine folding - showing that the magnitude of an improvement is not evidence of its correctness - alongside a zero-cost check flagging the artefact from data a benchmark already holds. We distil both into a short reporting checklist for ZNE benchmarks.
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4 pages including references, 2 Figures, Accepted for publication at IEEE Quantum Week (QCE) 2026
Reliable benchmarking of Quantum Error Mitigation (QEM) requires distinguishing genuine improvements from artefacts of the post-processing arithmetic. In this paper, we expose a failure mode in Richardson Zero-Noise Extrapolation (ZNE), a widely used technique routinely (and often implicitly) relied upon in benchmarks and experiments. When noise amplification operates beyond usable signals - a regime that is quickly reached on current hardware for non-trivial circuits - we show that the extrapolation no longer reflects the underlying physics, but collapses into a fixed rescaling of a single noisy measurement, producing a bogus apparent improvement that is independent of noise amplification. This poses a rarely considered threat to the validity of many empirical evaluations in quantum computing.
Measurements on real hardware (IQM Euro-Q-Exa) confirm this collapse with ordinary folding alone: as circuit depth erodes the signal, the reported estimate decouples from the truth and overshoots the ideal by up to 21%. We further introduce a matched-cost "garbage-folding" negative control that carries no usable signal yet reports a larger apparent improvement than genuine folding - showing that the magnitude of an improvement is not evidence of its correctness - alongside a zero-cost check flagging the artefact from data a benchmark already holds. We distil both into a short reporting checklist for ZNE benchmarks.
Near term quantum processors operate in a noise dominated regime, motivating error mitigation techniques that recover accurate expectation values without full fault tolerance. Zero Noise Extrapolation (ZNE) is a widely used but biased error mitigation method that lacks rigorous error bounds. Its effective application requires nontrivial technical choices, most notably the selection of noise scaling factors and extrapolation models, making ZNE sensitive to user expertise and often necessitating costly trial and error procedures. Here, we introduce Folding Free Zero Noise Extrapolation (FF-ZNE), a method that removes the need for noise factor selection by achieving effective noise amplification without circuit folding. FF-ZNE exploits isomorphic hardware layouts with distinct native noise profiles, such that executing a fixed circuit across these layouts induces controllable variations in the effective noise strength. Under a depolarizing noise model, we analytically show that the resulting extrapolation admits a fixed linear form, eliminating extrapolator choice and enabling a seamless, user independent mitigation procedure. We further propose two algorithms that identify sets of isomorphic hardware layouts on which a given circuit yields sufficiently distinct expectation values to enable reliable zero noise extrapolation. Experiments on a 133 qubit IBM Quantum device demonstrate that FF-ZNE yields mitigated expectation values with average deviations of ~6% and 4.5% for up to 50 qubit EfficientSU2 (sparse) and Hamiltonian simulation (dense) circuits, respectively. The method is thus scalable and applicable to a broad range of circuits. By eliminating noise factor and extrapolator selection, FF-ZNE transforms zero noise extrapolation from a technique requiring expert tuning into a practical, scalable, and broadly accessible error mitigation method for current quantum hardware.
QEM is widely regarded as a plausible bridge from NISQ devices to FTQC. Yet the empirical studies used to assess the effectiveness of QEM techniques on concrete problems have received comparatively little scrutiny with respect to the validity of their conclusions. We systematically review 81 recent QEM papers using an eight-criterion framework covering statistical rigour, reproducibility, and reporting quality. Among the applicable papers, only 15 (25%) use inferential methods, while 25 (42%) report uncertainty only descriptively, without testing whether the claimed effects are statistically supported. To demonstrate the consequences of these omissions, we use ZNE as a representative and widely used case study and identify two compounding sources of artefacts in current QEM benchmarks. First, we observe parameter sensitivity: in a 132-configuration sweep, implicitly assumed choices such as scale factors, extrapolation method, and hardware calibration are not merely incidental but active, with variations changing conclusions from statistically significant improvement to statistically significant degradation. Second, we identify a drift-induced effectiveness illusion: in a 72-hour longitudinal study on real hardware, temporal drift alone can make the same ZNE configuration exhibit an effect size more than three times as large, depending solely on when it is executed, and also drastically reduces the effective number of independent observations. These findings do not imply that QEM methods are intrinsically unsound; rather, they show that current evaluation practice can make mitigation performance appear more robust than the evidence warrants. We therefore propose minimum reporting standards for QEM evaluations, including explicit parameter documentation, robustness checks, longitudinal drift assessment, and inferential statistical testing with effect-size reporting.
We present a simple, malleable and low-overhead approach for improving generic biased quantum error mitigation (QEM) methods, achieving up to 15% fidelity improvements over standard QEM on 100-qubit circuits with up to 2000 entangling gates. We do so by constructing verifiable benchmark circuits which mirror the application circuit's native-gate structure and thus noise profile. These circuits can be used to benchmark and mitigate the bias of the underlying error mitigation method, requiring only the application circuit and hardware native gate set. We present two methods for generating benchmark circuits; one is agnostic to the target hardware at the expense of a small overhead of single-qubit gates, while the other is specific to the IBM superconducting hardware and has no gate overhead. As a corollary, we introduce benchmarked-noise zero-noise extrapolation (bnZNE) as a simple adaptation of zero-noise extrapolation (ZNE), one of the most popular error mitigation methods. We consider as an example the bias-mitigated ZNE and bnZNE of Trotterized Hamiltonian simulations, observing that our approaches outperform standard ZNE using both small-scale classical simulations and 100-qubit utility-scale experiments on the IBM superconducting hardware. We consider the measurement of both single-site observables as well as two-site correlations along a one-dimensional qubit chain. We also provide a software package for implementing the error mitigation techniques used in this research.
Recent hundred-qubit processors represent a significant hardware advancement, but current limitations prevent effective quantum error correction (QEC), necessitating reliance on quantum error mitigation (QEM) to enhance result fidelity from quantum computers. Our paper introduces a noise-aware folding technique that enhances Zero-Noise Extrapolation (ZNE) by leveraging the noise characteristics of target quantum hardware to fold circuits more efficiently. Unlike traditional ZNE approaches assuming uniform error distribution, our method redistributes noise using calibration data based on hardware noise models. By employing a noise-adaptive compilation method combined with our proposed folding mechanism, we enhance the ZNE accuracy of quantum gate-based computing using superconducting quantum computers. This paper highlights the uniqueness of our method, summarizes noise accumulation, presents the scaling algorithm, and compares the reliability of our method with those of existing models using linear extrapolation model. Experimental results show that compared to existing folding methods, our approach achieved up to a 23.6% improvement on quantum computer simulators and up to a 44.8% improvement on real quantum computers, with statistical confidence intervals indicating stable performance gains across circuit scales.
Abstract Quantum error mitigation is a key enabling technique for extracting reliable computational results from noisy intermediate-scale quantum devices. Among existing approaches, zero-noise extrapolation (ZNE) is widely employed, but its effectiveness deteriorates in high-noise regimes or deep circuits, where measurement outcomes converge to the random-guessing limit. To address this issue, we propose a lightweight, extrapolation-based error mitigation framework that leverages the intrinsic repetitive structure of quantum algorithms. We classify these algorithms into homogeneous and heterogeneous types and efficiently estimate block fidelities using shallow identity sequences of the core operational block, thereby avoiding global circuit folding. Experiments on IBM’s advanced superconducting platform and its corresponding simulator demonstrate that the proposed framework remains effective even in high-noise regimes where folding-based ZNE(f-ZNE) fails due to noise saturation. For a four-qubit Grover search, our approach restores the success probability to near its theoretical value, whereas f-ZNE yields negligible improvement. We further validate the proposed method on a 17-qubit quantum approximate optimization algorithm with layer-dependent parameters, demonstrating robustness beyond fixed-structure circuits. These results indicate that leveraging algorithmic structure provides a practical pathway to extending the performance of extrapolation-based error mitigation on current quantum processors.
Coping with noise in quantum computation poses significant challenges due to its unpredictable nature and the complexities of accurate modeling. This paper presents noise-adaptive folding, a technique that enhances zero-noise extrapolation (ZNE) through the use of adaptive scaling factors based on circuit error measurements. Furthermore, we introduce two filtering methods: one relies on measuring error strength, while the other utilizes statistical filtering to improve the extrapolation process. Comparing our approach with standard ZNE reveals that adaptive scaling factors can be optimized using either a noise model or direct error strength measurements from inverted circuits. The integration of adaptive scaling with filtering techniques leads to notable improvements in expectation-value extrapolation over standard ZNE. Our findings demonstrate that these adaptive methods effectively strengthen error mitigation against noise fluctuations, thereby enhancing the precision and reliability of quantum computations.