Výsledky vyhledávání - "Roman V. Krems"

Akademický článek

Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines

Publikováno v: Nature Communications, Vol 14, Iss 1, Pp 1-7 (2023)

Rigorous results about the real computational advantages of quantum machine learning are few. Here, the authors prove that a PROMISEBQP-complete problem can be expressed by variational quantum classifiers and quantum support vector machines, meaning

Externí odkaz: https://doaj.org/article/abd17952d2744c99b9cbd73374845ee4

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Akademický článek

Benchmarking of quantum fidelity kernels for Gaussian process regression

Autor: Xuyang Guo, Jun Dai, Roman V Krems

Publikováno v: Machine Learning: Science and Technology, Vol 5, Iss 3, p 035081 (2024)

Quantum computing algorithms have been shown to produce performant quantum kernels for machine-learning classification problems. Here, we examine the performance of quantum kernels for regression problems of practical interest. For an unbiased benchm

Externí odkaz: https://doaj.org/article/e48ccf428aee4c598aacb148ed477dfe

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Akademický článek

Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors

Autor: Yun-Wen Mao, Roman V Krems

Publikováno v: Machine Learning: Science and Technology, Vol 5, Iss 1, p 015059 (2024)

We demonstrate accurate data-starved models of molecular properties for interpolation in chemical compound spaces with low-dimensional descriptors. Our starting point is based on three-dimensional, universal, physical descriptors derived from the pro

Externí odkaz: https://doaj.org/article/c2cd9ad1b3dc445e9d9a70adba4eb794

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Akademický článek

Compositional optimization of quantum circuits for quantum kernels of support vector machines

Autor: Elham Torabian, Roman V. Krems

Publikováno v: Physical Review Research, Vol 5, Iss 1, p 013211 (2023)

While quantum machine learning (ML) has been proposed to be one of the most promising applications of quantum computing, how to build quantum ML models that outperform classical ML remains a major open question. Here, we demonstrate a Bayesian algori

Externí odkaz: https://doaj.org/article/4cd99142313141f48ee0ff9032d4adf7

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Akademický článek

Cross-calibration of atomic pressure sensors and deviation from quantum diffractive collision universality for light particles

Autor: Pinrui Shen, Erik Frieling, Katherine R Herperger, Denis Uhland, Riley A Stewart, Avinash Deshmukh, Roman V Krems, James L Booth, Kirk W Madison

Publikováno v: New Journal of Physics, Vol 25, Iss 5, p 053018 (2023)

The room-temperature, velocity-averaged, total cross section for atom–atom and atom–molecule collisions can be approximated using a universal function depending only on the magnitude of the leading order dispersion coefficient, C _6 . This featur

Externí odkaz: https://doaj.org/article/f714f39dbd1c4245852a34fba04ec79a

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Akademický článek

A general method for controlling and resolving rotational orientation of molecules in molecule-surface collisions

Autor: Oded Godsi, Gefen Corem, Yosef Alkoby, Joshua T. Cantin, Roman V. Krems, Mark F. Somers, Jörg Meyer, Geert-Jan Kroes, Tsofar Maniv, Gil Alexandrowicz

Publikováno v: Nature Communications, Vol 8, Iss 1, Pp 1-7 (2017)

The rotational orientation of a molecule plays a fundamental role in molecule-surface collisions, yet is difficult to study. Here, the authors present a general approach for controlling and resolving molecular rotational orientation and apply it to s

Externí odkaz: https://doaj.org/article/0c32593378994fc1b7fe8fb629dc58c7

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Akademický článek

Universality of quantum diffractive collisions and the quantum pressure standard

Autor: James L Booth, Pinrui Shen, Roman V Krems, Kirk W Madison

Publikováno v: New Journal of Physics, Vol 21, Iss 10, p 102001 (2019)

This work demonstrates that quantum diffractive collisions are governed by a universal law characterized by a single parameter that can be determined experimentally. Specifically, we report a quantitative form of the universal, cumulative energy dist

Externí odkaz: https://doaj.org/article/aa29839d8ad24c6391e0f17c992e2119

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Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines

Autor: Jonas Jäger, Roman V. Krems

Machine learning is considered to be one of the most promising applications of quantum computing. Therefore, the search for quantum advantage of the quantum analogues of machine learning models is a key research goal. Here, we show that variational q

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::237b23ce3c33fb2b1c123f2963d426aa

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Gradient-based reconstruction of molecular Hamiltonians and density matrices from time-dependent quantum observables

Autor: Wucheng Zhang, Ilia Tutunnikov, Ilya Sh. Averbukh, Roman V. Krems

We consider a quantum system with a time-independent Hamiltonian parametrized by a set of unknown parameters $\alpha$. The system is prepared in a general quantum state by an evolution operator that depends on a set of unknown parameters $P$. After t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::370f3e4846d3cc6cb3f62048807a12f9

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