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Updated: Oct 16, 2025

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
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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.

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|October 23, 2021
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Summary
This summary is machine-generated.

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 machinef-divergencegenerative modellinglocal 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.