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Training of quantum circuits on a hybrid quantum computer.

Science advances·2019

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F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits.

Chiara Leadbeater¹, Louis Sharrock^1,2, Brian Coyle^1,3

¹Cambridge Quantum Computing Limited, London SW1E 6DR, UK.

Entropy (Basel, Switzerland)

|October 23, 2021

Summary

This study introduces a hybrid quantum-classical method for generative modeling using quantum circuit born machines trained with f-divergences. New heuristics improve training, and quantum algorithms promise faster divergence estimation.

Keywords:

born machine f-divergence generative modelling local cost function

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Area of Science:

Quantum Computing
Machine Learning
Artificial Intelligence

Background:

Generative modeling is a key unsupervised learning task.
Quantum circuit born machines offer a novel approach to generative modeling.
f-divergences are crucial for training generative models.

Purpose of the Study:

To explore a hybrid quantum-classical approach for generative modeling using quantum circuit born machines.
To investigate the training of quantum circuit born machines with f-divergences.
To develop strategies for improving the training process and explore long-term quantum advantages.

Main Methods:

Utilizing an adversarial framework for estimating f-divergences.
Implementing two heuristics: f-divergence switching and introducing locality to the divergence.
Generalizing existing quantum algorithms for divergence estimation.

Main Results:

Demonstrated improvement in training quantum circuit born machines using the proposed heuristics.
Established a framework for near-term estimation of any f-divergence.
Proposed a fault-tolerant quantum algorithm for Pearson divergence estimation.

Conclusions:

Hybrid quantum-classical generative modeling with f-divergences is a promising research direction.
The introduced heuristics effectively enhance the training of quantum circuit born machines.
Quantum computing holds significant potential for accelerating the computation of f-divergences.