Quantum certification and benchmarking

Eisert, Jens; Hangleiter, Dominik; Walk, Nathan; Roth, Ingo; Markham, Damian; Parekh, Rhea; Chabaud, Ulysse; Kashefi, Elham

doi:10.1038/s42254-020-0186-4

Cited by 285 publications

(215 citation statements)

References 104 publications

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“…Indirect methods could be used to assess entanglement but would require specific assumptions about the initial state and noise statistics [14] or performing homodyne measurements [35]. In the broad context of quantum networks, this result also emphasizes the topical importance of developing efficient benchmarking tools [36].…”

Section: Entanglement Transfermentioning

confidence: 99%

Efficient reversible entanglement transfer between light and quantum memories

Cao¹,

Hoffet²,

Qiu³

et al. 2020

Optica

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Reversible entanglement transfer between light and matter is a crucial requisite for the ongoing developments of quantum information technologies. Quantum networks and their envisioned applications, e.g., secure communications beyond direct transmission, distributed quantum computing, or enhanced sensing, rely on entanglement distribution between nodes. Although entanglement transfer has been demonstrated, a current roadblock is the limited efficiency of this process that can compromise the scalability of multi-step architectures. Here we demonstrate the efficient transfer of heralded single-photon entanglement into and out of two quantum memories based on large ensembles of cold cesium atoms. We achieve an overall storage-and-retrieval efficiency of 85% together with a preserved suppression of the twophoton component of about 10% of the value for a coherent state. Our work constitutes an important capability that is needed toward large scale networks and increased functionality.

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Section: Entanglement Transfermentioning

confidence: 99%

Efficient reversible entanglement transfer between light and quantum memories

Cao¹,

Hoffet²,

Qiu³

et al. 2020

Optica

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“…The primary goal in this work is to mitigate errors that cannot be described solely as a gate-independent depolarizing noise and without relying on any noise characterization methods, such as randomized benchmarking and process tomography [27]- [39]. This section is dedicated to explaining machine learning methods to achieve such a goal.…”

Section: B Error Mitigation Via Machine Learningmentioning

confidence: 99%

Quantum Error Mitigation With Artificial Neural Network

2020

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A quantum error mitigation technique based on machine learning is proposed, which learns how to adjust the probabilities estimated by measurement in the computational basis. Neural networks in two different designs are trained with random quantum circuits consisting of a set of one-and two-qubit unitary gates whose measurement statistics in the ideal (noiseless) and real (noisy) cases are known. Once the neural networks are trained, they infer the amount of probability adjustment to be made on the measurement obtained from executing an unseen quantum circuit to reduce the error. The proposed schemes are tested with two-, three-, five-, and seven-qubit quantum circuits of depth up to 20 by computer simulations with realistic error models and experiments using the IBM quantum cloud platform. In all test cases, the proposed mitigation technique reduces the error effectively. Our method can be used to improve the accuracy of noisy intermediate-scale quantum (NISQ) algorithms without relying on extensive error characterization or quantum error correction.

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“…In the following, we will demonstrate that the qualitative and quantitative features of the DSF for both the short- and long-range TFIM can be recovered from a quantum simulation even in the presence of experimental imperfections. Here, we discuss the role of imperfections and their impact as such; the certification of the actual correctness of the quantum simulation ( 59 ) is a separate task.…”

Section: Effect Of Experimental Imperfections On the Dsf Of The Shortmentioning

confidence: 99%

Dynamical structure factors of dynamical quantum simulators

Baez

Goihl²,

Haferkamp³

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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The dynamical structure factor is one of the experimental quantities crucial in scrutinizing the validity of the microscopic description of strongly correlated systems. However, despite its long-standing importance, it is exceedingly difficult in generic cases to numerically calculate it, ensuring that the necessary approximations involved yield a correct result. Acknowledging this practical difficulty, we discuss in what way results on the hardness of classically tracking time evolution under local Hamiltonians are precisely inherited by dynamical structure factors and, hence, offer in the same way the potential computational capabilities that dynamical quantum simulators do: We argue that practically accessible variants of the dynamical structure factors are bounded-error quantum polynomial time (BQP)-hard for general local Hamiltonians. Complementing these conceptual insights, we improve upon a novel, readily available measurement setup allowing for the determination of the dynamical structure factor in different architectures, including arrays of ultra-cold atoms, trapped ions, Rydberg atoms, and superconducting qubits. Our results suggest that quantum simulations employing near-term noisy intermediate-scale quantum devices should allow for the observation of features of dynamical structure factors of correlated quantum matter in the presence of experimental imperfections, for larger system sizes than what is achievable by classical simulation.

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Quantum certification and benchmarking

Cited by 285 publications

References 104 publications

Efficient reversible entanglement transfer between light and quantum memories

Efficient reversible entanglement transfer between light and quantum memories

Quantum Error Mitigation With Artificial Neural Network

Dynamical structure factors of dynamical quantum simulators

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