Hi all,
Tomorrow Jonathan will talk at group meeting. See below for his title and
abstract.
All the best,
Ian
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Title: Quantum autoencoders: theory and applications
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 introduced the model of a quantum autoencoder to
perform similar tasks on quantum data [1].
A quantum autoencoder consists of a quantum circuit representing a
parameterized unitary that is trained to compress a particular dataset of
quantum states, where a classical compression algorithm would require
exponential resources. Given a set of states in N qubits and a choice of
reference state in k qubits with N>k, the autoencoder finds a unitary that
factorizes all the states in the training set as a tensor product of a
state in N-k qubits and the reference state. The parameters associated with
the circuit are optimized using a classical optimization algorithm. In this
talk, we present the details of the autoencoder implementation on a quantum
computer and explore applications to quantum simulation and quantum
information.
References:
[1] J. Romero, J. P. Olson and A. Aspuru-Guzik, *Quantum Sci. Technol.* In
press, 2017.
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