Colloquium: Advances in automation of quantum dot devices control

Zwolak, Justyna P.

doi:10.48550/arxiv.2112.09362

Cited by 2 publications

(2 citation statements)

References 60 publications

(96 reference statements)

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“…Similarly, a four-qubit in the Ge/ SiGe hole spin system also made its debut, where a fourqubit GHZ state was demonstrated [539]. Meanwhile, methods for multiple quantum dots tuning using virtual gates [585] and automated machine learning were developed [586,587] and multiplexed quantum dots readout was realized [568,588]. Toward QEC, a three-qubit phase error correction algorithm was carried out by two groups [25,26].…”

Section: T > 15mentioning

confidence: 99%

Noisy intermediate-scale quantum computers

Cheng

Deng

et al. 2023

Front. Phys.

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Quantum computers have made extraordinary progress over the past decade, and significant milestones have been achieved along the path of pursuing universal fault-tolerant quantum computers. Quantum advantage, the tipping point heralding the quantum era, has been accomplished along with several waves of breakthroughs. Quantum hardware has become more integrated and architectural compared to its toddler days. The controlling precision of various physical systems is pushed beyond the fault-tolerant threshold. Meanwhile, quantum computation research has established a new norm by embracing industrialization and commercialization. The joint power of governments, private investors, and tech companies has significantly shaped a new vibrant environment that accelerates the development of this field, now at the beginning of the noisy intermediate-scale quantum era. Here, we first discuss the progress achieved in the field of quantum computation by reviewing the most important algorithms and advances in the most promising technical routes, and then summarizing the next-stage challenges. Furthermore, we illustrate our confidence that solid foundations have been built for the fault-tolerant quantum computer and our optimism that the emergence of quantum killer applications essential for human society shall happen in the future.

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Section: T > 15mentioning

confidence: 99%

Noisy intermediate-scale quantum computers

Cheng

Deng

et al. 2023

Front. Phys.

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“…Machine learning techniques have been applied extensively to calibrating quantum dots [10][11][12][13][14][15][16][17][18]. For superconducting quantum processors, machine learning approaches have been used, for example, for automated * These authors contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays

Barrett¹,

Karamlou²,

Muschinske³

et al. 2023

Preprint

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Superconducting quantum processors comprising flux-tunable data and coupler qubits are a promising platform for quantum computation. However, magnetic flux crosstalk between the fluxcontrol lines and the constituent qubits impedes precision control of qubit frequencies, presenting a challenge to scaling this platform. In order to implement high-fidelity digital and analog quantum operations, one must characterize the flux crosstalk and compensate for it. In this work, we introduce a learning-based calibration protocol and demonstrate its experimental performance by calibrating an array of 16 flux-tunable transmon qubits. To demonstrate the extensibility of our protocol, we simulate the crosstalk matrix learning procedure for larger arrays of transmon qubits. We observe an empirically linear scaling with system size, while maintaining a median qubit frequency error below 300 kHz.

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Colloquium: Advances in automation of quantum dot devices control

Cited by 2 publications

References 60 publications

Noisy intermediate-scale quantum computers

Noisy intermediate-scale quantum computers

Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays

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