February 10, 2017

Quantum autoencoders for efficient compression of quantum data

  • Jhonathan Romero Fontalvo
  • Jonathan Olson

Autoencoders are impressive machine learning tools for learning how to very efficiently encode and decode complex data distributions such as pictures, handwritten digits, and so on. In this paper, we build a quantum analog to autoencoders that allows the encoding and decoding of quantum information (which is notoriously difficult to do without prior knowledge of the data) and show in a toy example that it can learn to encode ground state of hydrogen without significant decoherence. 


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 input x, is to map x to a lower dimensional point y such that x can likely be recovered from y. The structure of the underlying autoencoder network can be chosen to represent the data on a smaller dimension, effectively compressing the input. Inspired by this idea, we introduce the model of a quantum autoencoder to perform similar tasks on quantum data. The quantum au- toencoder is trained to compress a particular dataset of quantum states, where a classical compression algorithm cannot be employed. The parameters of the quantum autoencoder are trained using classical optimization algo- rithms. We show an example of a simple programmable circuit that can be trained as an efficient autoencoder. We apply our model in the context of quantum simulation to compress ground states of the Hubbard model and molecular Hamiltonians.

Jhonathan Romero Fontalvo
Zapata Author

Jhonathan Romero Fontalvo , Ph.D.

Director of Professional Services & Co-Founder
Jonathan Olson
Zapata Author

Jonathan Olson , Ph.D.

Associate Director of Quantum Science IP & Co-Founder