Quantum autoencoders for efficient compression of quantum data
Abstract
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, 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 autoencoder is trained to compress a particular data set of quantum states, where a classical compression algorithm cannot be employed. The parameters of the quantum autoencoder are trained using classical optimization algorithms. 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 18, 2017
- Source ID
- 10.1088/2058-9565/aa8072
Entities
People
- Alán Aspuru-Guzik
- Jonathan P. Olson
- Jonathan Romero
Organizations
- Air Force Office of Scientific Research
- Army Research Office
- Office of Naval Research