Hi all,
Tomorrow Christa will tell us about “Quantum Generative Adversarial
Networks for Learning and Loading Random Distributions” at the usual time
and place; see below for the abstract.
Best,
Joe
Abstract: Quantum algorithms have the potential to outperform their
classical counterparts in a variety of tasks. The realization of the
advantage often requires the ability to load classical data efficiently
into quantum states. However, the best known methods for loading generic
data into an n-qubit state require O(2^n) gates. This scaling can easily
predominate the complexity of a quantum algorithm and, thereby, impair
potential quantum advantage.
Our work demonstrates that quantum Generative Adversarial Networks (qGANs)
facilitate
efficient loading of generic probability distributions implicitly given by
data samples into
quantum states. More specifically, the qGAN scheme employs the interplay of
a quantum channel, a variational quantum circuit, and a classical neural
network to learn the probability distribution underlying training data
samples and load it into the quantum channel.
Effectively, the scheme results in a quantum channel that loads the learned
distribution with
O(poly (n)) gates. This distribution loading method can, thus, enable the
exploitation of quantum advantage induced by other quantum algorithms, such
as Quantum Amplitude Estimation. We implement the qGAN distribution
learning and loading method with Qiskit and test it using a quantum
simulation as well as actual quantum processors provided by the IBM Q
Experience. Furthermore, we employ quantum simulation to demonstrate the
use of the trained quantum channel in a quantum finance application.