Artificial agency and learning in quantum environments

Introduction

Quantum mechanics is our most fundamental theory of physics. It has formed, and often challenged, our understanding of physical reality. We use quantum mechanics to manipulate and control matter and light at the atomic scale, and it provides the basis for many new technologies.

The Rise of AI and ML in Science

At the same time, the rise of artificial intelligence (AI) and machine learning (ML) is gaining momentum in science and basic research. ML is already employed in different areas of physics, mostly for big data processing and classification.

Future Transformations in Basic Science

But the development of AI is heading much further and is likely to transform basic science in the near future. In this project, we will investigate the use of AI in basic science, with a focus on quantum physics and, more specifically, quantum information.

Development of Artificial Agency Models

We will develop models of artificial agency which are beneficial for basic research, both from a practical and a foundational perspective. Our specific goals include:

1. Developing classical artificial learning agents that can be used for adaptive schemes of quantum error correction.
2. Integrating these agents into large-scale quantum computing architectures.
3. Designing complex quantum communication networks.
4. Studying novel computational phases of matter.

Applications of Learning Agents

The models of learning agents that we develop will facilitate applications towards AI-driven quantum experiments and scientific discovery. Our focus will be on transparent and interpretable models for artificial agency that are beneficial, if not needed, for basic science.

Future Hybrid Laboratories

These models can be used in future hybrid laboratories where human researchers will interact with AI assistant systems.

Foundational Investigations

On the foundational side, we will investigate quantum agents and the role of agency in quantum theory. These investigations will shed light on:

• Possible ways of learning in a quantum environment.
• The physical dimension of AI.
• The explainability of quantum AI.
• The consistency of quantum theory.