
Quantum generative adversarial learning
April 2018
Generative adversarial networks (GANs) represent a powerful tool for classical machine learning: a generator tries to create statistics for data that mimics those of a true data set, while a discriminator tries to discriminate between the true and fake data.
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Quantum generative adversarial networks
April 2018
Quantum machine learning is expected to be one of the first potential generalpurpose applications of nearterm quantum devices. A major recent breakthrough in classical machine learning is the notion of generative adversarial training, where…
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We introduce Strawberry Fields, an opensource quantum programming architecture for lightbased quantum computers.
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Boson sampling devices are a prime candidate for exhibiting quantum supremacy, yet their application for solving problems of practical interest is less well understood. Here we show that Gaussian boson sampling (GBS) can be used for dense subgraph identification.
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Hard optimization problems are often approached by finding approximate solutions. Here, we highlight the concept of proportional sampling and discuss how it can be used to improve the performance of stochastic algorithms for optimization.
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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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