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pro vyhledávání: '"Henderson, Maxwell P."'
This paper explores the advantages of optimizing quantum circuits on $N$ wires as operators in the unitary group $U(2^N)$. We run gradient-based optimization in the Lie algebra $\mathfrak u(2^N)$ and use the exponential map to parametrize unitary mat
Externí odkaz:
http://arxiv.org/abs/2203.00601
Autor:
Enos, Graham R., Reagor, Matthew J., Henderson, Maxwell P., Young, Christina, Horton, Kyle, Birch, Mandy, Rigetti, Chad
The availability of high-resolution weather radar images underpins effective forecasting and decision-making. In regions beyond traditional radar coverage, generative models have emerged as an important synthetic capability, fusing more ubiquitous da
Externí odkaz:
http://arxiv.org/abs/2111.15605
Autor:
Coyle, Brian, Henderson, Maxwell, Le, Justin Chan Jin, Kumar, Niraj, Paini, Marco, Kashefi, Elham
Finding a concrete use case for quantum computers in the near term is still an open question, with machine learning typically touted as one of the first fields which will be impacted by quantum technologies. In this work, we investigate and compare t
Externí odkaz:
http://arxiv.org/abs/2008.00691
Quantum computing is a transformative technology with the potential to enhance operations in the space industry through the acceleration of optimization and machine learning processes. Machine learning processes enable automated image classification
Externí odkaz:
http://arxiv.org/abs/2004.03079
Convolutional neural networks (CNNs) have rapidly risen in popularity for many machine learning applications, particularly in the field of image recognition. Much of the benefit generated from these networks comes from their ability to extract featur
Externí odkaz:
http://arxiv.org/abs/1904.04767
Accurate, reliable sampling from fully-connected graphs with arbitrary correlations is a difficult problem. Such sampling requires knowledge of the probabilities of observing every possible state of a graph. As graph size grows, the number of model s
Externí odkaz:
http://arxiv.org/abs/1802.00069
In Deep Learning, a well-known approach for training a Deep Neural Network starts by training a generative Deep Belief Network model, typically using Contrastive Divergence (CD), then fine-tuning the weights using backpropagation or other discriminat
Externí odkaz:
http://arxiv.org/abs/1510.06356
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Akademický článek
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Publikováno v:
Quantum Machine Intelligence; June 2020, Vol. 2 Issue: 1 p1-9, 9p
