
We introduce a general method for building neural networks on quantum computers. The quantum neural network is a variational quantum circuit built in the continuousvariable (CV) architecture, which encodes quantum information…
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A faster hafnian formula for complex matrices and its benchmarking on the Titan supercomputer
May 2018
We introduce new and simple algorithms for the calculation of the number of perfect matchings of complex weighted, undirected graphs with and without loops.
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Implementing quantum algorithms is essential for quantum computation. We study the implementation of three quantum algorithms by performing homodyne measurements on a twodimensional…
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Financial derivatives are contracts that can have a complex payoff dependent upon underlying benchmark assets. In this work, we present a quantum algorithm for the Monte Carlo pricing of financial derivatives.
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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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