Estimating the coherence of noise

Wallman, Joel J.; Granade, Christopher; Harper, Robin; Flammia, Steven T.

doi:10.1088/1367-2630/17/11/113020

Cited by 181 publications

(270 citation statements)

References 24 publications

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“…For the implemented gates, we evaluate the average gate fidelity [9], the diamond distance [10], and the unitarity [11]. The obtained gate fidelities agree with those reported in state-of-the-art experiments [12][13][14][15].…”

Section: Introductionsupporting

confidence: 74%

“…We have found that gate metrics such as the average gate fidelity [9], the diamond distance [10,33], and the unitarity [11] each provide insights into the errors of the implemented gate pulses. Specifically, while the time evolution of the total system is inherently unitary and the errors are systematic, they appear as incoherent non-Pauli errors on the computational subspace.…”

Section: Discussionmentioning

confidence: 99%

“…where Note that by construction, the errors we observe are systematic, unitary, and coherent (in the sense of [11]) on the total Hilbert space H. Hence this quantity characterizes how incoherent these errors appear on the computational subspace.…”

Section: Unitaritymentioning

confidence: 91%

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Gate-error analysis in simulations of quantum computers with transmon qubits

et al. 2017

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In the model of gate-based quantum computation, the qubits are controlled by a sequence of quantum gates. In superconducting qubit systems, these gates can be implemented by voltage pulses. The success of implementing a particular gate can be expressed by various metrics such as the average gate fidelity, the diamond distance, and the unitarity. We analyze these metrics of gate pulses for a system of two superconducting transmon qubits coupled by a resonator, a system inspired by the architecture of the IBM Quantum Experience. The metrics are obtained by numerical solution of the time-dependent Schrödinger equation of the transmon system. We find that the metrics reflect systematic errors that are most pronounced for echoed cross-resonance gates, but that none of the studied metrics can reliably predict the performance of a gate when used repeatedly in a quantum algorithm.

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Section: Introductionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

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Gate-error analysis in simulations of quantum computers with transmon qubits

et al. 2017

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“…15 Understanding that such an approach is untenable when attempting to rigorously compare QCVV results to metrics relevant to quantum error correction has recently led to an expansion of theoretical activity in this space. 3,[16][17][18][19][20] In this work our objectives are to experimentally characterise and explain the impact of temporally correlated noise processes on the outputs of QCVV protocols, and to identify potential modifications enabling users to improve the utility of the information returned. We perform QCVV experiments using a single trapped 171 Yb + ion as a long-lived, high-stability qubit.…”

Section: Introductionmentioning

confidence: 99%

Experimental quantum verification in the presence of temporally correlated noise

et al. 2018

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Growth in the capabilities of quantum information hardware mandates access to techniques for performance verification that function under realistic laboratory conditions. Here we experimentally characterise the impact of common temporally correlated noise processes on both randomised benchmarking (RB) and gate-set tomography (GST). Our analysis highlights the role of sequence structure in enhancing or suppressing the sensitivity of quantum verification protocols to either slowly or rapidly varying noise, which we treat in the limiting cases of quasi-DC miscalibration and white noise power spectra. We perform experiments with a single trapped 171 Yb + ion-qubit and inject engineered noise /σ z ð Þto probe protocol performance. Experiments on RB validate predictions that measured fidelities over sequences are described by a gamma distribution varying between approximately Gaussian, and a broad, highly skewed distribution for rapidly and slowly varying noise, respectively. Similarly we find a strong gate set dependence of default experimental GST procedures in the presence of correlated errors, leading to significant deviations between estimated and calculated diamond distances in the presence of correlatedσ z errors. Numerical simulations demonstrate that expansion of the gate set to include negative rotations can suppress these discrepancies and increase reported diamond distances by orders of magnitude for the same error processes. Similar effects do not occur for correlatedσ x orσ y errors or depolarising noise processes, highlighting the impact of the critical interplay of selected gate set and the gauge optimisation process on the meaning of the reported diamond norm in correlated noise environments.

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“…Thus, fast and reliable routines that determine the presence of specific error types are required. Others have proposed to use RB for measuring the unital part of a quantum map [13], correlated errors on a multi-qubit space [14], and recently Ref. [15] has proposed an alternative method for measuring unitary errors.…”

mentioning

confidence: 99%

Characterizing errors on qubit operations via iterative randomized benchmarking

Sheldon¹,

Bishop²,

Magesan³

et al. 2016

Phys. Rev. A

150

119

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With improved gate calibrations reducing unitary errors, we achieve a benchmarked single-qubit gate fidelity of 99.95% with superconducting qubits in a circuit quantum electrodynamics system. We present a method for distinguishing between unitary and non-unitary errors in quantum gates by interleaving repetitions of a target gate within a randomized benchmarking sequence. The benchmarking fidelity decays quadratically with the number of interleaved gates for unitary errors but linearly for non-unitary, allowing us to separate systematic coherent errors from decoherent effects. With this protocol we show that the fidelity of the gates is not limited by unitary errors, but by another drive-activated source of decoherence such as amplitude fluctuations.Accurate characterization of control gates is an essential task for developing any quantum computing device. Quantum process tomography (QPT) [1][2][3] has been the standard method for characterizing quantum gates because, ideally, it produces a full reconstruction of the quantum process. In practice however, QPT suffers from many drawbacks, the most inimical being its exponential scaling in the number of quantum bits (qubits) comprising the system and that it is limited by state preparation and measurement (SPAM) errors. Various methods such as randomized benchmarking (RB) [4][5][6][7] and gate set tomography (GST) [8,9] have recently been developed to help overcome these limitations. RB is both insensitive to SPAM errors and efficient [10]. However, it only extracts a single piece of information, the average gate fidelity. GST on the other hand helps to overcome limitations from SPAM errors by reconstructing an entire library of gates in a self-consistent manner. The price paid for this self-consistent reconstruction is an even worse scaling than QPT.As control calibration techniques continue to improve and quantum gates approach the fidelity required for fault tolerant quantum computation, it becomes both important and difficult to verify the presence of increasingly small errors. Error verification constitutes a critical first step in a debugging routine since different physical mechanisms can lead to different error types. QPT and GST are often poor choices for error verification since they are time consuming and contain so much information that backing out the presence of specific error types on small scales can be a challenge in itself. In addition, SPAM errors in QPT sets a lower limit on the detectable error strengths [8]. At the other end of the spectrum, while standard RB is efficient the information it contains about the gate is typically not enough to perform any sort of useful error verification. An extension of standard RB, interleaved randomized benchmarking, consists of interleaving a target gate in a benchmarking sequence and provides bounds on the error for the gate of interest [11,12]. Interleaved benchmarking can identify gates that are poorly calibrated, but does not reveal if the errors are due to decoherence, over-/underrotations, or offresona...

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Estimating the coherence of noise

Cited by 181 publications

References 24 publications

Gate-error analysis in simulations of quantum computers with transmon qubits

Gate-error analysis in simulations of quantum computers with transmon qubits

Experimental quantum verification in the presence of temporally correlated noise

Characterizing errors on qubit operations via iterative randomized benchmarking

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