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arXiv:2610.20810

Syndrome-Resolved Decoding of Qubit Readout Errors

Han Zheng, Vladimir Pešić, Nikolay Gusarov, Andrea Lizzit, Taketo Imaizumi, Chulwoo Ahn, Marco Nicolas Mueller, Raymond Mencia, Edoardo Charbon, Vladimir E. Manucharyan

quant-phSubmitted Oct 14, 2026• Announced Oct 16, 2026• 0 citations
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Abstract

Readout errors in superconducting qubits include incorrect state assignment, bit flips within the computational subspace, and leakage to non-computational states. These errors require different mitigation strategies, yet conventional measurement fidelity and repeatability metrics do not resolve their individual contributions. Previous repeated-readout studies have shown that temporal correlations reveal additional information about readout errors. Here we introduce the Readout Error Decoder (RED), which converts repeated thresholded binary records into a channel-resolved error budget. Controlled error injections validate the channel-specific response of the decoder. We demonstrate RED on both fluxonium and transmon qubits and across distinct leakage-lifetime regimes. RED identifies the error channels limiting readout performance without requiring dedicated discrimination of non-computational states, providing a basis for targeted readout optimization across different qubit modalities.

Comments
13 pages, 4 main figures, 4 supplementary figures

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Readout errors in superconducting qubits include incorrect state assignment, bit flips within the computational subspace, and leakage to non-computational states. These errors require different mitigation strategies, yet conventional measurement fidelity and repeatability metrics do not resolve their individual contributions. Previous repeated-readout studies have shown that temporal correlations reveal additional information about readout errors. Here we introduce the Readout Error Decoder (RED), which converts repeated thresholded binary records into a channel-resolved error budget. Controlled error injections validate the channel-specific response of the decoder. We demonstrate RED on both fluxonium and transmon qubits and across distinct leakage-lifetime regimes. RED identifies the error channels limiting readout performance without requiring dedicated discrimination of non-computational states, providing a basis for targeted readout optimization across different qubit modalities.
Readout of superconducting qubits faces a trade-off between measurement speed and unwanted backaction on the qubit caused by the readout drive, such as T_{1} degradation and leakage out of the computational subspace. The readout is typically benchmarked by integrating the readout signal and choosing a binary threshold to extract the "readout fidelity." We show that readout fidelity may significantly overlook readout-induced leakage errors. Such errors are detrimental for applications that rely on continuously repeated measurements, e.g., quantum error correction. We introduce a method to measure the readout-induced leakage rate by repeatedly executing a composite operation-a readout preceded by a randomized qubit flip. We apply this technique to characterize the readout of a superconducting qubit, optimized for fidelity across four different readout durations. Our technique highlights the importance of an independent leakage characterization by showing that the leakage rates vary from 0.12% to 7.76% across these readouts even though the fidelity exceeds 99.5% in all four cases.
Quantum error correction enables the preservation of logical qubits with a lower logical error rate than the physical error rate, with performance depending on the decoding method. Traditional decoding approaches rely on the binarization (“hardening”) of readout data, thereby ignoring valuable information embedded in the analog (“soft”) readout signal. We present experimental results showcasing the advantages of incorporating soft information into the decoding process of a distance-3 ( d = 3 ) bit-flip surface code with flux-tunable transmons. We encode each of the 16 computational states that make up the logical state | 0 L ⟩ , and protect them against bit-flip errors by performing repeated Z -basis stabilizer measurements. To infer the logical fidelity for the | 0 L ⟩ state, we average across the 16 computational states and employ two decoding strategies: minimum-weight perfect matching and a recurrent neural network. Our results show a reduction of up to 6.8% in the extracted logical error rate with the use of soft information. Decoding with soft information is widely applicable, independent of the physical qubit platform, and could allow for shorter readout durations, further minimizing logical error rates. Published by the American Physical Society 2024
State transitions during qubit measurements are extremely detrimental to quantum tasks that rely on repeated measurements, such as quantum error correction. These state transitions can occur when excessive measurement power leads to qubit excitations outside its computational space. Alternatively, the qubit state can decay rapidly when the measurement protocol inadvertently couples the qubit to lossy modes such as two-level systems (TLSs). We experimentally verify the impact of these TLSs in qubit readout by measuring the transition errors at different qubit flux bias. Because such state transitions during measurements are often localized in frequency space, we demonstrate the ability to avoid them during a fluxonium readout by exploiting the qubit's flux-tunability. By synchronizing the flux bias with the readout photon dynamics, we obtain an optimal readout fidelity of 99 % (98.4 %) in 1 us (0.5 us) integration time. Our work advances the understanding of state transitions in superconducting circuit measurements and demonstrates the potential of fluxonium qubits to achieve fast high-fidelity readout.
Readout errors are one of the dominant sources of noise in current quantum processors, limiting both expectation-value estimation and sampling-based applications. Since they affect only the classical measurement outcomes, they can be addressed using classical coding techniques: immediately before measurement, each data qubit is redundantly encoded with ancilla qubits, and the resulting bit string is decoded either by post-selection or by majority voting. Unlike conventional readout error mitigation, which corrects only aggregate quantities such as expectation values, this approach operates on individual measurement shots and can therefore produce approximately corrected samples. We present a systematic cross-platform and cross-generation experimental evaluation of repetition-code readout error detection and correction. We benchmark the same protocol on IBM Heron r1-r3 superconducting processors and Quantinuum H1 and H2 trapped-ion processors while independently varying the code distance, hardware generation, and encoding layout. We find that both error detection and correction improve readout fidelity on every device and generation tested, even as the unencoded baseline improves substantially across successive hardware releases. At the same time, the value of additional redundancy depends strongly on the underlying hardware. On superconducting processors, the extra gate errors introduced by the encoding rapidly offset its benefits, whereas on trapped-ion processors the much lower gate error rates allow larger code distances to remain advantageous.
Quantum error correction allows for quantum information to be preserved using logical qubits, which are subject to lower error rates than their constituent physical qubits. The degree of error suppression depends on the choice of error correcting code and distance, the underlying physical error rate, and the accuracy of the decoder. While traditional decoders utilise a binary (hard) syndrome, recent work shows that additional (soft) information captured during qubit readout can be effectively utilised to improve decoding accuracy. In this work, we present experimental results from a distance-three surface code implemented on transmon qubits, where we perform Z-stabiliser measurements to protect the state of the logical qubit against bit-flip errors. We initialise the logical qubit in one of 16 possible computational states representing the logical zero state, and perform repeated stabiliser checks over a variable number of rounds to preserve the state over time. We compare the decoding performance for a hard minimum-weight perfect matching decoder against a soft variant where rich measurement information is incorporated, and demonstrate an improved logical fidelity. Additionally, we employ a recurrent neural network decoder with both soft and hard variants and observe improved performance when soft information is used. The general nature of soft information makes it widely applicable to different physical qubit platforms, where it can be leveraged to shorten measurement times and improve the logical fidelity in quantum error correction experiments. Pre-print available at arXiv:2403.00706.
Quantum computers will require quantum error correction to reach the low error rates necessary for solving problems that surpass the capabilities of conventional computers. One of the dominant errors limiting the performance of quantum error correction codes across multiple technology platforms is leakage out of the computational subspace arising from the multilevel structure of qubit implementations. Here, we present a resource-efficient universal leakage reduction unit for superconducting qubits using parametric flux modulation. This operation removes leakage down to our measurement inaccuracy of 7×10^{-4} in approximately 50 ns with a low error of 2.5(1)×10^{-3} on the computational subspace, thereby reaching durations and fidelities comparable to those of single-qubit gates. We demonstrate that using the leakage reduction unit in repeated weight-two stabilizer measurements reduces the total number of detected errors in a scalable fashion to close to what can be achieved using leakage-rejection methods that do not scale. Our approach does not require additional control electronics or on-chip components and is applicable to both auxiliary and data qubits. These benefits make our method particularly attractive for mitigating leakage in large-scale quantum error correction circuits, a crucial requirement for the practical implementation of fault-tolerant quantum computation.