
Ultimate limit of quantum beam tracking
August 2018
Tracking small transverse displacements of an optical beam with ultrahigh accuracy is a fundamental problem underlying numerous important applications ranging from pointing, acquisition and tracking for establishing a lasercom link, to atomic force microscopy for imaging with atomicscale resolution.
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Machine learning method for state preparation and gate synthesis on photonic quantum computers
August 2018
We show how techniques from machine learning and optimization can be used to find circuits of photonic quantum computers that perform a desired transformation between input and output states.
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We study what is arguably the most experimentally appealing Boson Sampling architecture: Gaussian states sampled with threshold detectors. We show that in this setting, the probability of observing a given outcome is related to a matrix function that we name…
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We propose a novel squeezed light source capable of meeting the stringent requirements of continuous variable quantum sampling. Using the effective χ2 interaction induced by a strong driving beam in the presence of the χ3 response in an integrated microresonator…
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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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