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Quantum autoencoders for efficient compression of quantum data
Jonathan Romero, Jonathan P. Olson, Alan Aspuru-Guzik
Classical autoencoders are neural networks that can learn efficient low dimensional representations of data in higher dimensional space. The task of an autoencoder is, given an inp…
Bounding the costs of quantum simulation of many-body physics in real space
Ian D. Kivlichan, Nathan Wiebe, Ryan Babbush +1
We present a quantum algorithm for simulating the dynamics of a first-quantized Hamiltonian in real space based on the truncated Taylor series algorithm. We avoid the possibility o…
Error Sensitivity to Environmental Noise in Quantum Circuits for Chemical State Preparation
Nicolas P. D. Sawaya, Mikhail Smelyanskiy, Jarrod R. McClean +1
Calculating molecular energies is likely to be one of the first useful applications to achieve quantum supremacy, performing faster on a quantum than a classical computer. However,…
qHiPSTER: The Quantum High Performance Software Testing Environment
Mikhail Smelyanskiy, Nicolas P. D. Sawaya, Alán Aspuru-Guzik
We present qHiPSTER, the Quantum High Performance Software Testing Environment. qHiPSTER is a distributed high-performance implementation of a quantum simulator on a classical comp…