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Variational quantum generative modeling by sampling expectation values of tunable observables.

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Summary
This summary is machine-generated.

Quantum generative models called Expectation Value Samplers (EVSs) can be resource-intensive. An Observable-Tunable EVS (OT-EVS) enhances expressivity and reduces sample complexity for efficient quantum generative modeling.

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Quantum informationQuantum physics

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Area of Science:

  • Quantum computing
  • Machine learning
  • Generative models

Background:

  • Expectation Value Samplers (EVSs) are quantum generative models for learning continuous distributions.
  • Standard EVSs often require significant quantum resources, limiting their practical application.

Purpose of the Study:

  • To investigate the impact of observable choices on EVS performance.
  • To propose an improved EVS with enhanced expressivity and reduced resource requirements.

Main Methods:

  • Introduced an Observable-Tunable Expectation Value Sampler (OT-EVS).
  • Utilized classical shadows measurement for reduced sample complexity.
  • Developed an adversarial training method prioritizing classical updates.

Main Results:

  • OT-EVS demonstrates greater expressivity compared to standard EVS.
  • The proposed methods significantly reduce sample complexity.
  • Numerical experiments confirm the model's efficiency and expressivity advantages.

Conclusions:

  • Observable choice is crucial for EVS performance.
  • OT-EVS offers a more resource-efficient approach to quantum generative modeling.
  • This work encourages further exploration of continuous generative models with lower quantum resource demands.