Characterization and control of open quantum systems beyond quantum noise spectroscopy

Youssry, Akram; Paz-Silva, Gerardo A.; Ferrie, Christopher

doi:10.1038/s41534-020-00332-8

Cited by 33 publications

(68 citation statements)

References 56 publications

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“…Burgeoning techniques for precise quantum control rely on software which simulates the unitary evolution of a quantum system as accurately as possible, with auxiliary channels responsible for noise and other non-unitary processes [34]. The deployment of learnings from this investigation will maximise the overall performance of these techniques.…”

Section: Discussionmentioning

confidence: 99%

Approximations in transmon simulation

Jones,

Steven,

Poncini

et al. 2021

Preprint

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Classical simulations of time-dependent quantum systems are widely used in quantum control research. In particular, these simulations are commonly used to host iterative optimal control algorithms. This is convenient for algorithms which are too onerous to run in the loop with current-day quantum hardware, as well as for researchers without consistent access to said hardware. However, if the model used to represent the system is not selected carefully, an optimised control protocol may be rendered futile when applied to hardware. We present a series of models, ordered in a hierarchy of progressive approximation, which appear in quantum control literature. Significant model deviations are highlighted, with a focus on simulated dynamics under simple single-qubit protocols. The validity of each model is characterised experimentally by designing and benchmarking control protocols for an IBMQ cloud quantum device. This result demonstrates an error amplification exceeding 100%, induced by the application of a first-order perturbative approximation. Finally, an evaluation of simulated control dynamics reveals that despite the substantial variance in numerical predictions across the proposed models, the complexity of discovering local optimal control protocols appears invariant for a simple control scheme. The set of findings presented heavily encourage practitioners of this field to ensure that their system models do not contain assumptions that markedly decrease applicability to hardware in experimentally relevant control parameter regimes.

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Section: Discussionmentioning

confidence: 99%

Approximations in transmon simulation

Jones,

Steven,

Poncini

et al. 2021

Preprint

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Section: D(t) Tmentioning

confidence: 99%

“…the characterization or "learning" of the noise in superconducting qubits either via classical [24][25][26] or, increasingly popular, machine learning techniques [27][28][29][30][31][32]. The latter have been applied to study the behaviour of a qubit by completely circumventing the issue of the exact nature of the environment [31,32]. Even though this approach is successful in predicting the behaviour of the system, it does not directly reveal further information about the impurities causing the decoherence, which can benefit us when trying to remove the noise source or mitigate its effect [33].…”

Section: D(t) Tmentioning

confidence: 99%

Neural Network Based Qubit Environment Characterization

Papič,

de Vega

2021

Preprint

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The exact microscopic structure of the environments that produces 1/f noise in superconducting qubits remains largely unknown, hindering our ability to have robust simulations and harness the noise. In this paper we show how it is possible to infer information about such an environment based on a single measurement of the qubit coherence, circumventing any need for separate spectroscopy experiments. Similarly to other spectroscopic techniques, the qubit is used as a probe which interacts with its environment. The complexity of the relationship between the observed qubit dynamics and the impurities in the environment makes this problem ideal for machine learning methods -more specifically neural networks. With our algorithm we are able to reconstruct the parameters of the most prominent impurities in the environment, as well as differentiate between different environment models, paving the way towards a better understanding of 1/f noise in superconducting circuits.

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“…Recent work has shown promising results in this direction by using "whitebox" quantum features, such as physical laws, symmetries and relevant correlations, in the ML approach. For example, including quantum features has been used to improve the characterization of quantum noise [11] as well as to learn quantum states more efficiently [12,13] and in an interpretable way [14].…”

Section: Introductionmentioning

confidence: 99%

Quantum-tailored machine-learning characterization of a superconducting qubit

Genois,

Gross,

Di Paolo

et al. 2021

Preprint

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Machine learning (ML) is a promising approach for performing challenging quantum-information tasks such as device characterization, calibration and control. ML models can train directly on the data produced by a quantum device while remaining agnostic to the quantum nature of the learning task. However, these generic models lack physical interpretability and usually require large datasets in order to learn accurately. Here we incorporate features of quantum mechanics in the design of our ML approach to characterize the dynamics of a quantum device and learn device parameters. This physics-inspired approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data obtained from continuous weak measurement of a driven superconducting transmon qubit. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task, thus laying the groundwork for more scalable characterization techniques.

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Characterization and control of open quantum systems beyond quantum noise spectroscopy

Cited by 33 publications

References 56 publications

Approximations in transmon simulation

Approximations in transmon simulation

Neural Network Based Qubit Environment Characterization

Quantum-tailored machine-learning characterization of a superconducting qubit

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