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Variational quantum generative modeling by sampling expectation values of tunable observables.
Kevin Shen1,2,3, Andrii Kurkin1,2,3, Adrián Pérez-Salinas1,4
1aQaL Applied Quantum Algorithms, Leiden University, Leiden, The Netherlands.
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.
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.

