Learning hard distributions with quantum-enhanced Variational
Autoencoders
- URL: http://arxiv.org/abs/2305.01592v2
- Date: Thu, 18 May 2023 07:05:03 GMT
- Title: Learning hard distributions with quantum-enhanced Variational
Autoencoders
- Authors: Anantha Rao, Dhiraj Madan, Anupama Ray, Dhinakaran Vinayagamurthy,
M.S.Santhanam
- Abstract summary: We introduce a quantum-enhanced VAE (QeVAE) that uses quantum correlations to improve the fidelity over classical VAEs.
We empirically show that the QeVAE outperforms classical models on several classes of quantum states.
Our work paves the way for new applications of quantum generative learning algorithms.
- Score: 2.545905720487589
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: An important task in quantum generative machine learning is to model the
probability distribution of measurements of many-body quantum systems.
Classical generative models, such as generative adversarial networks (GANs) and
variational autoencoders (VAEs), can model the distributions of product states
with high fidelity, but fail or require an exponential number of parameters to
model entangled states. In this paper, we introduce a quantum-enhanced VAE
(QeVAE), a generative quantum-classical hybrid model that uses quantum
correlations to improve the fidelity over classical VAEs, while requiring only
a linear number of parameters. We provide a closed-form expression for the
output distributions of the QeVAE. We also empirically show that the QeVAE
outperforms classical models on several classes of quantum states, such as
4-qubit and 8-qubit quantum circuit states, haar random states, and quantum
kicked rotor states, with a more than 2x increase in fidelity for some states.
Finally, we find that the trained model outperforms the classical model when
executed on the IBMq Manila quantum computer. Our work paves the way for new
applications of quantum generative learning algorithms and characterizing
measurement distributions of high-dimensional quantum states.
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