Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Hsin-yuan Huang"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract Two 2,7-dicyaonfluorene-based molecules 27-DCN and 27-tDCN are utilized as acceptors (A) to combine with hexaphenylbenzene-centered donors (D) TATT and DDT-HPB for probing the exciplex formation. The photophysical characteristics reveal that
Externí odkaz:
https://doaj.org/article/0d72f2bfd253474581b8fbddd897bbdf
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-8 (2024)
Abstract Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geom
Externí odkaz:
https://doaj.org/article/87019dd60c254c2fab03bb0955c59007
Publikováno v:
PRX Quantum, Vol 5, Iss 4, p 040306 (2024)
While quantum state tomography is notoriously hard, most states hold little interest to practically minded tomographers. Given that states and unitaries appearing in nature are of bounded gate complexity, it is natural to ask if efficient learning be
Externí odkaz:
https://doaj.org/article/730c469988fd4b67b5bce8209329f52e
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-6 (2023)
Abstract The recent proliferation of NISQ devices has made it imperative to understand their power. In this work, we define and study the complexity class NISQ, which encapsulates problems that can be efficiently solved by a classical computer with a
Externí odkaz:
https://doaj.org/article/ded42b39d069451d88be0784314026b0
Autor:
Matthias C. Caro, Hsin-Yuan Huang, Nicholas Ezzell, Joe Gibbs, Andrew T. Sornborger, Lukasz Cincio, Patrick J. Coles, Zoë Holmes
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-9 (2023)
Abstract Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training
Externí odkaz:
https://doaj.org/article/70f24e87b63943c980a107a5799fddd6
Autor:
Joe Gibbs, Zoë Holmes, Matthias C. Caro, Nicholas Ezzell, Hsin-Yuan Huang, Lukasz Cincio, Andrew T. Sornborger, Patrick J. Coles
Publikováno v:
Physical Review Research, Vol 6, Iss 1, p 013241 (2024)
Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Her
Externí odkaz:
https://doaj.org/article/a995f3ee90b94a3db452aa089bbc0cbc
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/4102ed4cc14740ab87208df0d440c47d
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/7e54600e69894634ae905b50329cdf54
Publikováno v:
PRX Quantum, Vol 4, Iss 4, p 040337 (2023)
We present an efficient machine-learning (ML) algorithm for predicting any unknown quantum process E over n qubits. For a wide range of distributions D on arbitrary n-qubit states, we show that this ML algorithm can learn to predict any local propert
Externí odkaz:
https://doaj.org/article/94b3c873b3c34d9b9fa447447a50c5d2
Autor:
Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, Patrick J. Coles
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
The power of quantum machine learning algorithms based on parametrised quantum circuits are still not fully understood. Here, the authors report rigorous bounds on the generalisation error in variational QML, confirming how known implementable models
Externí odkaz:
https://doaj.org/article/b4bb435a92dd4f239c5b6469734606ea