Provable and scalable quantum Gaussian processes for quantum learning

2022-09-15 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Quantum Computing · Depth: Expert, medium

Summary

Researchers have introduced quantum Gaussian processes (QGPs), a Bayesian framework designed for learning from quantum systems by establishing priors over unknown quantum transformations. This approach addresses limitations in existing quantum machine learning methods, which often suffer from fragile data access models or difficult optimization and interpretability issues. QGPs enable regression, classification, and Bayesian optimization directly on quantum data by leveraging physics-informed inductive bias through quantum kernels. The framework demonstrates that unitary quantum stochastic processes can define Gaussian processes under specific conditions. A key finding is that matchgate, or free-fermionic, evolutions provide a provable and scalable family of QGPs where the unknown unitary acts non-trivially on all qubits. The framework has been successfully applied to long-range extrapolation, phase-diagram learning in many-body systems, and sample-efficient Bayesian optimization in quantum sensing tasks.

Key takeaway

For AI Scientists and Research Scientists developing quantum machine learning models, adopting quantum Gaussian processes (QGPs) offers a path to more structured and interpretable learning. You should consider integrating physics-informed inductive biases through quantum kernels, especially for tasks involving quantum data. This framework provides a robust method for regression, classification, and Bayesian optimization, potentially overcoming the scalability and interpretability challenges of current variational quantum algorithms.

Key insights

Quantum Gaussian processes offer a scalable, interpretable Bayesian framework for learning from quantum data.

Principles

• Physics-informed inductive bias improves quantum learning.
• Unitary quantum stochastic processes can define Gaussian processes.

Method

Define a quantum kernel and Bayesian prior using partial knowledge of an unknown quantum process and measurement, then apply the QGP for regression, classification, or optimization.

In practice

• Use matchgate evolutions for scalable all-qubit QGPs.
• Apply QGPs for quantum sensing Bayesian optimization.

Topics

• Quantum Gaussian Processes
• Quantum Machine Learning
• Bayesian Framework
• Quantum Kernels
• Matchgate Evolutions

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.