Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Patrick Rebentrost"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract A derivative is a financial asset whose future payoff is a function of underlying assets. Pricing a financial derivative involves setting up a market model, finding a martingale (“fair game”) probability measure for the model from the ex
Externí odkaz:
https://doaj.org/article/553671e96afe4debacc1d0348601eb04
Efficient quantum circuits for machine learning activation functions including constant T-depth ReLU
Publikováno v:
Physical Review Research, Vol 6, Iss 4, p 043048 (2024)
In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of act
Externí odkaz:
https://doaj.org/article/75c25afe1a05475f91a4220c4a356ae5
Publikováno v:
Quantum, Vol 7, p 1174 (2023)
At the interface of machine learning and quantum computing, an important question is what distributions can be learned provably with optimal sample complexities and with quantum-accelerated time complexities. In the classical case, Klivans and Goel d
Externí odkaz:
https://doaj.org/article/66c7dcb25a584d15a10502bf732c6cee
Autor:
Keren Li, Shijie Wei, Pan Gao, Feihao Zhang, Zengrong Zhou, Tao Xin, Xiaoting Wang, Patrick Rebentrost, Guilu Long
Publikováno v:
npj Quantum Information, Vol 7, Iss 1, Pp 1-7 (2021)
Abstract The gradient descent method is central to numerical optimization and is the key ingredient in many machine learning algorithms. It promises to find a local minimum of a function by iteratively moving along the direction of the steepest desce
Externí odkaz:
https://doaj.org/article/ee291d3fa219422aa4c45b7a814186f3
Autor:
Yihui Quek, Patrick Rebentrost
Publikováno v:
Physical Review Research, Vol 4, Iss 1, p 013144 (2022)
The polar decomposition of a matrix is a key element in the quantum linear algebra toolbox. We show that the problem of quantum polar decomposition, recently studied in Lloyd et al. [arXiv:2006.00841], has a simple and concise implementation via the
Externí odkaz:
https://doaj.org/article/8d963c42b6f24431bd28a736782170ef
Publikováno v:
New Journal of Physics, Vol 23, Iss 11, p 113021 (2021)
Solving linear systems of equations is essential for many problems in science and technology, including problems in machine learning. Existing quantum algorithms have demonstrated the potential for large speedups, but the required quantum resources a
Externí odkaz:
https://doaj.org/article/0fac1c67207042cfb7cbe4677a9b0548
Publikováno v:
New Journal of Physics, Vol 21, Iss 7, p 073023 (2019)
Optimization problems in disciplines such as machine learning are commonly solved with iterative methods. Gradient descent algorithms find local minima by moving along the direction of steepest descent while Newton’s method takes into account curva
Externí odkaz:
https://doaj.org/article/41626ce357b7402daa6c01808c70b9fe
Publikováno v:
New Journal of Physics, Vol 19, Iss 3, p 033005 (2017)
We develop an efficient quantum implementation of an important signal processing algorithm for line spectral estimation: the matrix pencil method , which determines the frequencies and damping factors of signals consisting of finite sums of exponenti
Externí odkaz:
https://doaj.org/article/abe1461cb3cf49b98bc8fb7e95397904
Publikováno v:
Quantum Information and Computation. 19:1325-1349
Submodular functions are set functions mapping every subset of some ground set of size $n$ into the real numbers and satisfying the diminishing returns property. Submodular minimization is an important field in discrete optimization theory due to its
Publikováno v:
Quantum Machine Intelligence. 3
Efficiently processing basic linear algebra subroutines is of great importance for a wide range of computational problems. In this paper, we consider techniques to implement matrix functions on a quantum computer. We embed given matrices into 3 times