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Dynamical Decoupling and Zero-Noise Extrapolation Under Colored Noise

Alexandru-Florin Boboc

Published Aug 26, 2026• 0 citations
Publisher

Abstract

Zero-noise extrapolation (ZNE) is an established quantum error mitigation technique that infers noise-free expectation values by scaling circuit noise and extrapolating to the zero-noise limit. Standard ZNE formulations implicitly assume memoryless Markovian gate noise, where error rates scale linearly with circuit duration. In physical solid-state quantum processors dominated by colored $1/f^\alpha$ environmental dephasing, this fundamental assumption fails: non-Markovian noise induces superlinear coherence decay $\chi(t) \propto t^{1+\alpha}$, causing standard polynomial extrapolation to diverge. In this work, we demonstrate how inserting dynamical decoupling (DD) sequences into idle circuit intervals spectrally filters low-frequency noise components, linearizing the effective decay profile into a Markovian regime $\chi_{\text{eff}}(t) \propto t$. We benchmark this combined DD–ZNE framework by reconstructing the order parameter across the quantum phase transition of a 1D Transverse-Field Ising Model (TFIM). Our results demonstrate that while standalone ZNE fails under non-Markovian dephasing, the combined DD–ZNE approach eliminates extrapolation divergence and reliably recovers ideal quantum observables. Key Highlights: Theoretical Breakdown: Analytical derivation showing why polynomial ZNE diverges under $1/f^\alpha$ dephasing environments due to superlinear temporal scaling. Filter Function Engineering: Demonstration of spectator-qubit XY-4 pulse insertion as a high-pass spectral filter that suppresses low-frequency noise and restores linear decay dynamics. Phase Transition Benchmark: Validation on a 4-qubit 1D TFIM order parameter ($\langle \sigma_z^{(0)} \sigma_z^{(1)} \rangle$) across $g \in [0.1, 2.0]$, demonstrating a $>69\%$ error reduction over unmitigated circuits and preventing non-physical polynomial divergence.

DOI
10.5281/zenodo.22113057

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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.
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.
Zero-noise extrapolation (ZNE) is an increasingly popular technique for mitigating errors in noisy quantum computations without using additional quantum resources. We review the fundamentals of ZNE and propose several improvements to noise scaling and extrapolation, the two key components in the technique. We introduce unitary folding and parameterized noise scaling. These are digital noise scaling frameworks, i.e. one can apply them using only gate-level access common to most quantum instruction sets. We also study different extrapolation methods, including a new adaptive protocol that uses a statistical inference framework. Benchmarks of our techniques show error reductions of 18X to 24X over non-mitigated circuits and demonstrate ZNE's effectiveness at larger qubit numbers than have been tested previously. In addition to presenting new results, this work is a self-contained introduction to the practical use of ZNE by quantum programmers.
Zero-noise extrapolation (ZNE) mitigates errors in near-term quantum devices by extrapolating measurements obtained at amplified noise levels to estimate noise-free expectation values. In practice, commonly used extrapolation models are fitted without enforcing physical constraints, which can yield predictions outside the valid range of quantum observables. In this work, we introduce physically bounded variants of polynomial, exponential, and polynomial--exponential extrapolation models by explicitly parameterizing the zero-noise estimate and constraining it during optimization. We evaluate the approach using a large synthetic benchmark comprising 180,000 circuits and approximately 3.6 million ZNE experiments generated under realistic device noise models derived from IBM quantum backends. We also perform preliminary validation on real quantum hardware using GHZ and W-state circuits. Across the synthetic benchmark, bounded extrapolation substantially reduces unphysical predictions and improves the stability of exponential- and polynomial--exponential-family models, whereas polynomial models show little difference between bounded and unbounded variants. Hardware experiments show similar qualitative behaviour: bounded models generally avoid pathological extrapolations and often provide a more reliable balance between accuracy and usable coverage. At the same time, the results highlight practical limitations of current devices, including stronger-than-expected noise effects and variability not fully captured by simulation models. These results suggest that enforcing physical constraints during extrapolation improves the reliability of ZNE and that this approach can be incorporated into existing workflows with minimal modification.
Quantum computers in the NISQ era are prone to noise. A range of quantum error mitigation techniques has been proposed to address this issue. Zero-noise extrapolation (ZNE) stands out as a promising one. ZNE involves increasing the noise levels in a circuit and then using extrapolation to infer the zero noise case from the noisy results obtained. This paper presents a novel ZNE approach that does not require circuit folding or noise scaling to mitigate depolarizing and/or decoherence noise. To mitigate depolarizing noise, we propose leveraging the extreme/infinite noisy case, which allows us to avoid circuit folding. Specifically, the circuit output with extreme noise becomes the maximally mixed state. We show that using circuit-reliability metrics, simple linear extrapolation can effectively mitigate depolarizing noise. With decoherence noise, different states decay into the all-zero state at a rate that depends on the number of excited states and time. Therefore, we propose a state- and latency-aware exponential extrapolation that does not involve folding or scaling. When dealing with a quantum system affected by both decoherence and depolarizing noise, we propose to use our two mitigation techniques in sequence: first applying decoherence error mitigation, followed by depolarizing error mitigation. A common limitation of ZNE schemes is that if the circuit of interest suffers from high noise, scaling-up noise levels could not provide useful data for extrapolation. We propose using circuit-cut techniques to break a large quantum circuit into smaller sub-circuits to overcome this limitation. This way, the noise levels of the sub-circuits are lower than the original circuit, and ZNE can become more effective in mitigating their noises.
Standard Zero-Noise Extrapolation (ZNE) in Variational Quantum Eigensolvers (VQE) assumes a single global noise scaling parameter, failing to capture heterogeneous and non-linear noise propagation across layered quantum circuits. We simulate VQE parameter optimisation at a challenging L=6 hardware-efficient depth, extracting the exact expectation values at each iteration. When the optimal parameters theta_star are found, they are fixed, and the multi-parameter noise surface is mapped. We propose a multi-parameter noise framework, modeling expectation values as a function of layer-wise noise scaling factors E(lambda_1, lambda_2, ..., lambda_L). Using structured unitary folding U_l = U_l (U_l^dagger U_l)^((lambda_l - 1) / 2), we independently scale the noise contribution of each ansatz layer and empirically map the resulting multi-dimensional noise surface. We analyze the separability of inter-layer noise interactions, identify layers with disproportionate error contributions via the gradient dE/dlambda_l, and compare scalar ZNE with multivariate polynomial regression and Gaussian Process (GP) extrapolation across H_2, LiH, and BeH_2 under depolarizing, amplitude damping, and coherent over-rotation noise. Our results demonstrate that noise is non-uniform and partially coupled across layers, and that layer-resolved ZNE with higher-order extrapolation can improve accuracy and variance under realistic NISQ noise models. These findings provide geometric insight into noise propagation in variational circuits and motivate targeted, layer-specific error mitigation strategies.