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arXiv:2607.26756

Recovering Coherent Errors from Randomized-Compiling Labels

Kushagra Vyas

quant-phSubmitted Jul 29, 2026• Announced Jul 30, 2026• 0 citations• Revised Aug 19, 2026 (v2)
v1 · Jul 29, 2026v2 · Aug 19, 2026
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Abstract

Randomized compiling converts a coherent gate error into a stochastic one by dressing each gate with a randomly chosen Pauli operator, and the standard view is that the coherent information is lost once outcomes are averaged over these random choices. This is incorrect: an exact Fisher-information identity shows that the averaged data and the correlation between the outcome and the random choice, the twirl label, together conserve the full information, and only the label is normally discarded. Retaining it turns an unmodified randomized-compiling run into an unbiased estimator for the coherent error, at no added circuit cost, valid under realistic decoherence and at any circuit depth. The same construction extends to coupling between qubits through two further mechanisms: a neighboring qubit prepared in a known, randomized state probes static coupling, and randomizing the pulse count used to prepare that state isolates a second, physically distinct leakage effect found unexpectedly on real hardware during this work. All three results are derived exactly, checked against closed-form oracles, and tested on a 127-qubit IBM processor. The single-qubit case is confirmed cleanly: an injected phase is recovered to 0.006~rad while the same data without labels shows no signal. The two-qubit extensions are validated at the level of the recovery circuit, with clean positive controls on hardware, though the coupling strengths themselves remain below the resolution of the shot budgets used here.

Comments
8 pages, 3 figures, 3 tables

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Randomized compiling converts a coherent gate error into a stochastic one by dressing each gate with a randomly chosen Pauli operator, and the standard view is that the coherent information is lost once outcomes are averaged over these random choices. This is incorrect: an exact Fisher-information identity shows that the averaged data and the correlation between the outcome and the random choice, the twirl label, together conserve the full information, and only the label is normally discarded. Retaining it turns an unmodified randomized-compiling run into an unbiased estimator for the coherent error, at no added circuit cost, valid under realistic decoherence and at any circuit depth. The same construction extends to coupling between qubits through two further mechanisms: a neighboring qubit prepared in a known, randomized state probes static coupling, and randomizing the pulse count used to prepare that state isolates a second, physically distinct leakage effect found unexpectedly on real hardware during this work. All three results are derived exactly, checked against closed-form oracles, and tested on a 127-qubit IBM processor. The single-qubit case is confirmed cleanly: an injected phase is recovered to 0.006~rad while the same data without labels shows no signal. The two-qubit extensions are validated at the level of the recovery circuit, with clean positive controls on hardware, though the coupling strengths themselves remain below the resolution of the shot budgets used here.
Randomized compiling reduces the effects of errors on quantum computers by tailoring arbitrary Markovian errors into stochastic Pauli noise. Here we prove that randomized compiling also tailors non-Markovian errors into local stochastic Pauli noise and investigate the technique's limitations. We show through analysis and numerical results that randomized compiling alters errors in three distinct helpful ways. First, it prevents the coherent accumulation of errors (including hard to remove crosstalk effects) across gate cycles by destroying intercycle coherent correlations. Second, it converts individual gate cycle errors into Pauli noise. Finally, randomized compiling reduces the variability inherent to noisy devices. We confirm these theoretical predictions with the IBM Quantum Experience platform and describe experimental data that illustrates a drastic performance improvement across public devices. These results cement the importance of randomized compiling in near- and long-term quantum information processing.
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Quantum noise is a central challenge in quantum computing across many applications. Extensive work has examined how qubits couple to their environment, leading to decoherence and irreversible relaxation. This work studies a continuous coherent noise model for quantum circuits and compares it with a discrete Pauli model. The focus is on small, coherent gate errors that accumulate across circuit depth. These errors are modeled as random rotations on the Bloch sphere using a von Mises-Fisher distribution. In the small-angle limit, the model reduces to an isotropic Gaussian distribution. We test the model on quantum error-correction circuits based on the [[5,1,3]] and [[7,1,3]] codes. A variant of Grover's search circuit with different qubit counts is also examined. To enable fair comparison, we introduce a model-independent matching scheme. Pauli and continuous noise channels are aligned using the binary entropy at readout. This isolates the effect of noise structure at fixed uncertainty. An approximate analytical method for the propagation of coherent errors is also developed. The method tracks error distributions both on Clifford and non-Clifford circuits without full Monte Carlo sampling. It reduces simulation cost while preserving accuracy for circuit-level error estimates. The approximation is validated against brute-force simulations, identifying its regime of validity with Clifford and non-Clifford circuits under error correction. Our results show that continuous coherent noise and Pauli noise lead to comparable logical-error trends in the stabilizer-code circuits studied here. This behavior is consistent with syndrome extraction projecting a general single-qubit error onto Pauli-error subspaces before recovery. They also show where simplified propagation models work well and where their accuracy is reduced.
Present-day quantum processors are open systems in which dissipation and decoherence degrade the information carried by a quantum state as it propagates through a circuit. We study this degradation in dissipative random quantum circuits, modeling each two-qubit gate as a diluted unitary that interpolates between the intended unitary operation and a random dissipative quantum channel. Using the fidelity between the ideal and noisy output states as a diagnostic, we show that sufficiently random unitary gates or dissipative Kraus operators lead to a universal decay of the average fidelity. This decay is determined only by the dissipation strength, system size, and circuit depth, while microscopic details of the gates and noise, including the Kraus rank, appear only in subleading corrections to higher moments. We derive a closed-form expression for the average fidelity in terms of three physically meaningful error parameters, dissipation strength, coherent two-qubit gate-error strength, and connectivity-error probability. Finally, we identify the cases when dissipative effects can be reproduced by an unitary-noise model and when dissipation-induced decoherence remains distinguishable from coherent noise at the level of the average fidelity.
The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) computing, systematic miscalibrations, drift, and crosstalk in the control of qubits can lead to a coherent form of error that has no classical analog. Coherent errors severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable quantum computations. Moreover, the average error rates measured by randomized benchmarking and related protocols are not sensitive to the full impact of coherent errors and therefore do not reliably predict the global performance of quantum algorithms, leaving us unprepared to validate the accuracy of future large-scale quantum computations. Randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error rates measured via cycle benchmarking. In this work, we demonstrate significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. Additionally, we accurately predict algorithm performance using experimentally measured error rates. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing.
Quantum circuit ensembles that have the properties of unitary k-designs represent applications where there is no obvious bias toward any particular Pauli support, as is the case in simulating systems exhibiting ''quantum chaos,'' which range from quantum dynamics near black holes to gapless spin fluid analysis. However, noisy hardware makes quantum circuits prone to a myriad of error sources, of which depolarizing and coherent error can be particularly destructive. To combat depolarizing error, popular techniques typically involve circuit or gate folding, which can be time-intensive procedures due to increased circuit depth and shot overhead. Other tensor-network-based mitigation techniques suffer from intractability in high-entanglement regimes. In this work, we leverage the structure of unitary k-design Pauli support distributions by introducing a technique we name ''circuit balancing,'' along with gate benchmarking data, in order to estimate circuit-wide depolarization. We describe how to invert the diagnosed circuit depolarization even in the presence of coherent error, via Pauli twirling. We provide asymptotics to estimate the number of twirls needed to maintain a desired output fidelity. We test our method numerically in a variety of simulation settings and find that it can significantly reduce average random circuit infidelity. Further, we employ our methods to find significant infidelity reductions when running a random circuit ensemble on a contemporary superconducting quantum computer, IBM Fez. Overall, we show that the method effectively reduces gate-based error for unitary k-designs without incurring any two-qubit gate overhead.