
Evaluating analytic gradients on quantum hardware
November 2018
An important application for nearterm quantum computing lies in optimization tasks, with applications ranging from quantum chemistry and drug discovery to machine learning.
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Exact gate decompositions for photonic quantum computing
November 2018
We propose a method for decomposing continuousvariable operations into a universal gate set, without the use of any approximations. We fully characterize a set of transformations admitting exact decompositions and describe a process for obtaining them systematically.
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Nonnegativity for hafnians of certain matrices
November 2018
We show that a certain class of complex symmetric matrices has a nonnegative hafnian. These are positive scalar multiples of matrices that are encodable in a Gaussian boson sampler.
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A faster calculation of FranckCondon factors and Fock matrix elements of Gaussian unitaries using loop hafnians
November 2018
The FranckCondon Factor (FCF) associated to a transition between initial and final vibrational states in two different potential energy surfaces is equivalent to calculating the number of perfect matchings of a weighted graph with loops that has P=N+M vertices.
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PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantumclassical computations. The library provides a unified architecture for nearterm quantum computing devices, supporting both qubit and continuousvariable paradigms.
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Graph isomorphism and Gaussian boson sampling
October 2018
We introduce a connection between a nearterm quantum computing device, specifically a Gaussian boson sampler, and the graph isomorphism problem. We propose a scheme where graphs are encoded into quantum states of light, whose properties are then probed with photonnumberresolving detectors.
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In this work, we employ a recent exact sampling algorithm for GBS with threshold detectors to perform classical simulations on the Titan supercomputer.
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Neardeterministic production of universal quantum photonic gates enhanced by machine learning
September 2018
We introduce architectures for neardeterministic implementation of fully tunable weak cubic phase gates requisite for universal quantum computation.
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We describe a quantum algorithm for preparing states that encode solutions of nonhomogeneous linear partial differential equations. The algorithm is a continuousvariable version of matrix inversion: it efficiently inverts differential operators that are polynomials in the variables and their partial derivatives.
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Photonic quantum algorithm for Monte Carlo integration
September 2018
We present a continuousvariable photonic quantum algorithm for the Monte Carlo evaluation of multidimensional integrals. Our algorithm encodes ndimensional integration into n+3 modes and can provide a quadratic speedup in runtime compared to the classical Monte Carlo approach.
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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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