
Machine learning is a crucial aspect of artificial intelligence. This paper details an approach
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for quantum Hebbian learning through a batched version of quantum state exponentiation. 
The basic idea of quantum computing is surprisingly similar to that of kernel methods in machine learning, namely to efficiently perform computations in an intractably large Hilbert space.
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ON states as resource units for universal quantum computation with photonic architectures
February 2018
Universal quantum computation using photonic systems requires gates whose Hamiltonians are beyond quadratic in the quadrature operators. Proposals to implement such gates usually require intricate preparation of nonGaussian states…
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In this work, we decompose the timeevolution of the BoseHubbard model into a sequence of logic gates that can be implemented on a continuousvariable photonic quantum computer.
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We introduce an efficient scheme to correct errors due to the finite squeezing effects in continuousvariable cluster states. Specifically, we consider the typical situation where the class of algorithms consists of input states that are known.
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Quantum supremacy and highdimensional integration
December 2017
We establish a connection between continuousvariable quantum computing and highdimensional integration by showing that the outcome probabilities of continuousvariable instantaneous quantum polynomial (CVIQP) circuits…
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A famously hard graph problem with a broad range of applications is computing the number of perfect matchings, that is the number of unique and complete pairings of the vertices of a graph.
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A quantum hopfield neural network
October 2017
Quantum computing allows for the potential of significant advancements in both the speed and the capacity of widelyused machine learning techniques.
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