Always-On Quantum Error Tracking with Continuous Parity Measurements

Mohseninia, Razieh; Yang, Jing; Siddiqi, Irfan; Jordan, Andrew N.; Dressel, Justin

doi:10.22331/q-2020-11-04-358

Cited by 17 publications

(26 citation statements)

References 60 publications

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“…The picture changes qualitatively if entanglement is instead generated using native two-body measurements -a context for which this code is optimized [1]. Direct two-body measurements have been proposed and demonstrated experimentally in circuit QED [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31], and are expected to be the native operation in Majorana-based architectures [1,14,32]. In this setting, we observe a honeycomb code threshold between 1.5% and 2.0% -by comparison, previous work reported a surface code threshold of 0.237% in the same error model [14] (but using a less accurate union-find decoder).…”

Section: Summary Of Resultsmentioning

confidence: 99%

A Fault-Tolerant Honeycomb Memory

Gidney,

Newman,

Fowler

et al. 2021

Preprint

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Recently, Hastings & Haah introduced a quantum memory defined on the honeycomb lattice [1]. Remarkably, this honeycomb code assembles weight-six parity checks using only two-local measurements. The sparse connectivity and two-local measurements are desirable features for certain hardware, while the weight-six parity checks enable robust performance in the circuit model.In this work, we quantify the robustness of logical qubits preserved by the honeycomb code using a correlated minimum-weight perfect-matching decoder. Using Monte Carlo sampling, we estimate the honeycomb code's threshold in different error models, and project how efficiently it can reach the "teraquop regime" where trillions of quantum logical operations can be executed reliably. We perform the same estimates for the rotated surface code, and find a threshold of 0.2% − 0.3% for the honeycomb code compared to a threshold of 0.5% − 0.7% for the surface code in a controlled-not circuit model. In a circuit model with native two-body measurements, the honeycomb code achieves a threshold of 1.5% < p < 2.0%, where p is the collective error rate of the two-body measurement gate -including both measurement and correlated data depolarization error processes. With such gates at a physical error rate of 10 −3 , we project that the honeycomb code can reach the teraquop regime with only 600 physical qubits.

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Section: Summary Of Resultsmentioning

confidence: 99%

A Fault-Tolerant Honeycomb Memory

Gidney,

Newman,

Fowler

et al. 2021

Preprint

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“…However, in contrast to this theoretical idealization of instantaneous projections of the quantum state, experimental implementation of such measurements inherently involves performing weak measurements over finite time intervals [6], with the dispersive readouts in superconducting qubit architectures constituting the prime example of this in today's quantum technologies [7][8][9][10]. This has motivated the development of continuous quantum error correction (CQEC) [11][12][13][14], where the error syndrome operators are measured weakly in strength and continuously in time.…”

Section: Introductionmentioning

confidence: 99%

“…Previous theoretical work on CQEC has focused primarily on measurement signals that behave in an idealized manner [11][12][13], such that each sample is assumed to be i.i.d. Gaussian with a mean given by one of the syndrome eigenvalues.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits

Convy,

Liao,

Zhang

et al. 2021

Preprint

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We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identity bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these realworld imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

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“…Weak measurements allow the observer to reconstruct the dynamics of a quantum system, and to track the evolution of a wavefunction before its collapse to an eigenstate. For superconducting quantum circuits in particular, reconstructing individual quantum trajectories [1][2][3][4][5][6][7][8][9][10] has served as a tool to monitor quantum jumps [11][12][13][14], track diffusion statistics [15][16][17][18], generate entanglement via measurement [19,20], coherently control quantum evolution using feedback [21][22][23][24][25], and implement continuous quantum error correction [26][27][28][29]. In weak measurements with superconducting qubits coupled to a readout resonator, the dynamics of the latter are typically much faster than the former.…”

Section: Introductionmentioning

confidence: 99%

Monitoring fast superconducting qubit dynamics using a neural network

Koolstra¹,

Stevenson²,

Barzili³

et al. 2021

Preprint

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Weak measurements of a superconducting qubit produce noisy voltage signals that are weakly correlated with the qubit state. To recover individual quantum trajectories from these noisy signals, traditional methods require slow qubit dynamics and substantial prior information in the form of calibration experiments. Monitoring rapid qubit dynamics, e.g. during quantum gates, requires more complicated methods with increased demand for prior information. Here, we experimentally demonstrate an alternative method for accurately tracking rapidly driven superconducting qubit trajectories that uses a Long-Short Term Memory (LSTM) artificial neural network with minimal prior information. Despite few training assumptions, the LSTM produces trajectories that include qubit-readout resonator correlations due to a finite detection bandwidth. In addition to revealing rotated measurement eigenstates and a reduced measurement rate in agreement with theory for a fixed drive, the trained LSTM also correctly reconstructs evolution for an unknown drive with rapid modulation. Our work enables new applications of weak measurements with faster or initially unknown qubit dynamics, such as the diagnosis of coherent errors in quantum gates.

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Always-On Quantum Error Tracking with Continuous Parity Measurements

Cited by 17 publications

References 60 publications

A Fault-Tolerant Honeycomb Memory

A Fault-Tolerant Honeycomb Memory

Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits

Monitoring fast superconducting qubit dynamics using a neural network

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