Quantum sensing with superconducting circuits

Danilin, S.; Weides, M.

doi:10.48550/arxiv.2103.11022

Cited by 8 publications

(7 citation statements)

References 73 publications

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“…Our approach can be applied to the encoding of structured data, such as images [41,65] and videos [56], into quantum circuits. Besides, our method can be utilized to prepare quantum states for sensing to maximise the detection efficiency [66,67]. A more detailed explanation of these applications is given in appendix C.…”

Section: Discussionmentioning

confidence: 99%

Quantum state preparation using tensor networks

Melnikov

Alena

Dolgov

et al. 2023

Quantum Sci. Technol.

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Quantum state preparation is a vital routine in many quantum algorithms, including solution of linear systems of equations, Monte Carlo simulations, quantum sampling, and machine learning. However, to date, there is no established framework of encoding classical data into gate-based quantum devices. In this work, we propose a method for the encoding of vectors obtained by sampling analytical functions into quantum circuits that features polynomial runtime with respect to the number of qubits and provides >99.9% accuracy, which is better than a state-of-the-art two-qubit gate fidelity. We employ hardware-efficient variational quantum circuits, which are simulated using tensor networks, and matrix product state representation of vectors. In order to tune variational gates, we utilize Riemannian optimization incorporating auto-gradient calculation. Besides, we propose a ”cut once, measure twice” method, which allows us to avoid barren plateaus during gates’ update, benchmarking it up to 100-qubit circuits. Remarkably, any vectors that feature low-rank structure – not limited by analytical functions – can be encoded using the presented approach. Our method can be easily implemented on modern quantum hardware, and facilitates the use of the hybrid-quantum computing architectures.

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Section: Discussionmentioning

confidence: 99%

Quantum state preparation using tensor networks

Melnikov

Alena

Dolgov

et al. 2023

Quantum Sci. Technol.

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“…An automated procedure to analyze superconducting circuits is a beneficial tool for the study of superconducting circuits within the context of quantum information. Apart from the application to computing, superconducting circuits can be also used as a platform in other areas of application like sensing [43] or studying thermodynamics [44].…”

Section: Discussionmentioning

confidence: 99%

CircuitQ: an open-source toolbox for superconducting circuits

et al. 2022

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We introduce CircuitQ, an open-source toolbox for the analysis of superconducting circuits implemented in Python. It features the automated construction of a symbolic Hamiltonian of the input circuit and a dynamic numerical representation of the Hamiltonian with a variable basis choice. The software implementation is capable of choosing the basis in a fully automated fashion based on the potential energy landscape. Additional features include the estimation of the T1 lifetimes of the circuit states under various noise mechanisms. We review previously established circuit quantization methods and formulate them in a way that facilitates the software implementation. The toolbox is then showcased by applying it to practically relevant qubit circuits and comparing it to specialized circuit solvers. Our circuit quantization is applicable to circuit inputs from a large design space, and the software is open-sourced. We thereby add an important resource for the design of new quantum circuits for quantum information processing applications.

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“…In other words, one would ask for sensing protocols able to infer quantum system-environment interactions. In this context, one of the most promising applications is quantum thermometry, that may play a crucial role to improve accuracy in carrying out communication and computing in the quantum regime [15,[185][186][187]. It is generally known, indeed, that the unavoidable influence of the environment can lead both to a temperature increase and energy fluctuations in terms of heat losses [188,189], which is expected to prevent the correct functionality of the physical mechanism one is investigating.…”

Section: Inference Of Quantum System-environment Interactionsmentioning

confidence: 99%

Propagating quantum microwaves: towards applications in communication and sensing

Casariego

Cruzeiro

Gherardini

et al. 2023

Quantum Sci. Technol.

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The field of propagating quantum microwaves is a relatively new area of research that is receiving increased attention due to its promising technological applications, both in communication and sensing. While formally similar to quantum optics, some key elements required by the aim of having a controllable quantum microwave interface are still on an early stage of development. Here, we argue where and why a fully operative toolbox for propagating quantum microwaves will be needed, pointing to novel directions of research along the way: from microwave quantum key distribution to quantum radar, bath-system learning, or direct dark matter detection. The article therefore functions both as a review of the state-of-the-art, and as an illustration of the wide reach of applications the future of quantum microwaves will open.

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Quantum sensing with superconducting circuits

Cited by 8 publications

References 73 publications

Quantum state preparation using tensor networks

Quantum state preparation using tensor networks

CircuitQ: an open-source toolbox for superconducting circuits

Propagating quantum microwaves: towards applications in communication and sensing

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