Adaptive Weight Estimator for Quantum Error Correction in a Time‐Dependent Environment

Spitz, S.; Tarasinski, Brian; Beenakker, C. W. J.; O’Brien, Thomas E.

doi:10.1002/qute.201800012

Cited by 15 publications

(16 citation statements)

References 24 publications

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“…The occurrence of an error is signaled by d n ½ ¼ 1. To pair defects we use a minimum-weight perfectmatching (MWPM) decoder, whose weights are trained on simulated data without leakage 27,48 and we benchmark its logical performance in the presence of leakage errors. The logical qubit is initialized in 0 j i L and the logical fidelity is calculated at each QEC cycle, from which the logical error rate ε L can be extracted 27 .…”

Section: Effect Of Leakage On the Code Performancementioning

confidence: 99%

“…For the Surface-17 simulations we use the open-source density-matrix simulation package quantumsim 27 , available at "The quantumsim package can be found at https://quantumsim.gitlab.io/". For decoding we use a MWPM decoder 27 , for which the weights of the possible error pairings are extracted from Surface-17 simulations via adaptive estimation 48 without leakage (L 1 = 0) and an otherwise identical error model (described in section "Error model and parameters").…”

Section: Simulation Protocolmentioning

confidence: 99%

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Leakage detection for a transmon-based surface code

et al. 2020

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Leakage outside of the qubit computational subspace, present in many leading experimental platforms, constitutes a threatening error for quantum error correction (QEC) for qubits. We develop a leakage-detection scheme via Hidden Markov models (HMMs) for transmon-based implementations of the surface code. By performing realistic density-matrix simulations of the distance-3 surface code (Surface-17), we observe that leakage is sharply projected and leads to an increase in the surface-code defect probability of neighboring stabilizers. Together with the analog readout of the ancilla qubits, this increase enables the accurate detection of the time and location of leakage. We restore the logical error rate below the memory break-even point by post-selecting out leakage, discarding less than half of the data for the given noise parameters. Leakage detection via HMMs opens the prospect for near-term QEC demonstrations, targeted leakage reduction and leakage-aware decoding and is applicable to other experimental platforms.

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Section: Effect Of Leakage On the Code Performancementioning

confidence: 99%

Section: Simulation Protocolmentioning

confidence: 99%

Leakage detection for a transmon-based surface code

et al. 2020

View full text Add to dashboard Cite

show abstract

“…For the Surface-17 simulations we use the open-source density-matrix simulation package quantumsim [27], available at [47]. For decoding we use a MWPM decoder [27], for which the weights of the possible error pairings are extracted from Surface-17 simulations via adaptive estimation [56] without leakage (L 1 = 0) and an otherwise identical error model (described in Appendix B).…”

Section: Discussionmentioning

confidence: 99%

“…Ancilla-qubit measurements are modeled as projective in the {|0 , |1 , |2 } basis and ancilla qubits are not reset between QEC cycles. As a consequence, given the ancilla-qubit mea- decoder, whose weights are trained on simulated data without leakage [27,49] and we benchmark its logical performance in the presence of leakage errors. The logical qubit is initialized in |0 L and the logical fidelity is calculated at each QEC cycle, from which the logical error rate ε L can be extracted [27].…”

Section: B Effect Of Leakage On the Code Performancementioning

confidence: 99%

Leakage detection for a transmon-based surface code

Varbanov,

Battistel,

Tarasinski

et al. 2020

Preprint

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Leakage outside of the qubit computational subspace, present in many leading experimental platforms, constitutes a threatening error for quantum error correction (QEC) for qubits. We develop a leakage-detection scheme via Hidden Markov models (HMMs) for transmon-based implementations of the surface code. By performing realistic density-matrix simulations of the distance-3 surface code (Surface-17), we observe that leakage is sharply projected and leads to an increase in the surface-code defect probability of neighboring stabilizers. Together with the analog readout of the ancilla qubits, this increase enables the accurate detection of the time and location of leakage. We restore the logical error rate below the memory break-even point by post-selecting out leakage, discarding about 47% of the data. Leakage detection via HMMs opens the prospect for near-term QEC demonstrations, targeted leakage reduction and leakage-aware decoding and is applicable to other experimental platforms.

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“…We determine the weights in an error-model-free approach by inferring the errors per cycle from the measured data using a correlation analysis of the syndromes as described in Appendix L and Refs. [9,50]. The correction of an error, initiated by analysing all cycles in postprocessing, takes the form of changing the sign of the logical qubit operator values z L and x L when indicated by the decoder.…”

Section: Repeated Quantum Error Correctionmentioning

confidence: 99%

Realizing Repeated Quantum Error Correction in a Distance-Three Surface Code

Krinner,

Lacroix,

Remm

et al. 2021

Preprint

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Quantum computers hold the promise of solving computational problems which are intractable using conventional methods [1]. For fault-tolerant operation quantum computers must correct errors occurring due to unavoidable decoherence and limited control accuracy [2]. Here, we demonstrate quantum error correction using the surface code, which is known for its exceptionally high tolerance to errors [3][4][5][6]. Using 17 physical qubits in a superconducting circuit we encode quantum information in a distance-three logical qubit building up on recent distance-two error detection experiments [7][8][9]. In an error correction cycle taking only 1.1 µs, we demonstrate the preservation of four cardinal states of the logical qubit. Repeatedly executing the cycle, we measure and decode both bit-and phase-flip error syndromes using a minimum-weight perfect-matching algorithm in an error-modelfree approach and apply corrections in postprocessing. We find a low error probability of 3 % per cycle when rejecting experimental runs in which leakage is detected. The measured characteristics of our device agree well with a numerical model. Our demonstration of repeated, fast and highperformance quantum error correction cycles, together with recent advances in ion traps [10], support our understanding that fault-tolerant quantum computation will be practically realizable.

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Adaptive Weight Estimator for Quantum Error Correction in a Time‐Dependent Environment

Cited by 15 publications

References 24 publications

Leakage detection for a transmon-based surface code

Leakage detection for a transmon-based surface code

Leakage detection for a transmon-based surface code

Realizing Repeated Quantum Error Correction in a Distance-Three Surface Code

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