Optimized emulation of quantum magnetometry via superconducting qubits

Gusarov, N. N.; Perelshtein, M. R.; Hakonen, P. J.; Paraoanu, G. S.

doi:10.1103/physreva.107.052609

Cited by 4 publications

(1 citation statement)

References 71 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.

Self Cite

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Electronic noise—From advanced materials to quantum technologies

Balandin,

Paladino,

Hakonen

2024

Applied Physics Letters

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Coherent interaction-free detection of noise

McCord,

Dogra,

Paraoanu

2024

Phys. Rev. A

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The measurement and characterization of noise is a flourishing area of research in mesoscopic physics. In this work, we propose interaction-free measurements as a noise-detection technique, exploring two conceptually different schemes: the coherent and the projective realizations. These detectors consist of a qutrit whose second transition is resonantly coupled to an oscillatory field that may have noise in amplitude or phase. For comparison, we consider a more standard detector previously discussed in this context: a qubit coupled in a similar way to the noise source. We find that the qutrit scheme offers clear advantages, allowing precise detection and characterization of the noise, while the qubit does not. Finally, we study the signature of noise correlations in the detector's signal. Published by the American Physical Society 2024

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Optimized emulation of quantum magnetometry via superconducting qubits

Cited by 4 publications

References 71 publications

Quantum state preparation using tensor networks

Quantum state preparation using tensor networks

Electronic noise—From advanced materials to quantum technologies

Coherent interaction-free detection of noise

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