Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Angelatos AS"'
Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum
Externí odkaz:
http://arxiv.org/abs/2412.07910
Although linear quantum amplification has proven essential to the processing of weak quantum signals, extracting higher-order quantum features such as correlations in principle demands nonlinear operations. However, nonlinear processing of quantum si
Externí odkaz:
http://arxiv.org/abs/2409.03748
Autor:
Hu, Fangjun, Khan, Saeed A., Bronn, Nicholas T., Angelatos, Gerasimos, Rowlands, Graham E., Ribeill, Guilhem J., Türeci, Hakan E.
Publikováno v:
Nature Communications 15, 7491 (2024)
Practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that
Externí odkaz:
http://arxiv.org/abs/2312.16165
Autor:
Hu, Fangjun, Angelatos, Gerasimos, Khan, Saeed A., Vives, Marti, Türeci, Esin, Bello, Leon, Rowlands, Graham E., Ribeill, Guilhem J., Türeci, Hakan E.
Publikováno v:
Phys. Rev. X 13, 041020 (2023)
The expressive capacity of physical systems employed for learning is limited by the unavoidable presence of noise in their extracted outputs. Though present in physical systems across both the classical and quantum regimes, the precise impact of nois
Externí odkaz:
http://arxiv.org/abs/2307.16083
Autor:
Fangjun Hu, Saeed A. Khan, Nicholas T. Bronn, Gerasimos Angelatos, Graham E. Rowlands, Guilhem J. Ribeill, Hakan E. Türeci
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract The practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum
Externí odkaz:
https://doaj.org/article/a3038c2f947a48bb89a0a06f2048753a
Autor:
Hu, Fangjun, Angelatos, Gerasimos, Khan, Saeed A., Vives, Marti, Türeci, Esin, Bello, Leon, Rowlands, Graham E., Ribeill, Guilhem J., Türeci, Hakan E.
Publikováno v:
Phys. Rev. X 13, 041020 (2023)
The expressive capacity of quantum systems for machine learning is limited by quantum sampling noise incurred during measurement. Although it is generally believed that noise limits the resolvable capacity of quantum systems, the precise impact of no
Externí odkaz:
http://arxiv.org/abs/2301.00042
The paradigm of reservoir computing exploits the nonlinear dynamics of a physical reservoir to perform complex time-series processing tasks such as speech recognition and forecasting. Unlike other machine-learning approaches, reservoir computing rela
Externí odkaz:
http://arxiv.org/abs/2110.13849
Autor:
Fangjun Hu, Saeed A. Khan, Nicholas T. Bronn, Gerasimos Angelatos, Graham E. Rowlands, Guilhem J. Ribeill, Hakan E. Türeci
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/de53eda380144824acff950d6c7003fc
Publikováno v:
Phys. Rev. X 11, 041062 (2021)
Efficient quantum state measurement is important for maximizing the extracted information from a quantum system. For multi-qubit quantum processors in particular, the development of a scalable architecture for rapid and high-fidelity readout remains
Externí odkaz:
http://arxiv.org/abs/2011.09652
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