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F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
Chiara Leadbeater1, Louis Sharrock1,2, Brian Coyle1,3
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.
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.
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