Summary
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. 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. 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. We will develop models of artificial agency which are beneficial for basic research, both from a practical and a foundational perspective. We will develop classical artificial learning agents that can be used, e.g., for adaptive schemes of quantum error correction and integrated in large-scale quantum computing architectures, the design of complex quantum communication networks, and the study of novel computational phases of matter. The models of learning agents that we develop will facilitate applications towards AI-driven quantum experiment and scientific discovery. Our focus will be on transparent and interpretable models for artificial agency that are beneficial if not needed for basic science. These models can be used in future hybrid laboratories where human researchers will interact with AI assistant systems. On the foundational side, we will investigate quantum agents and the role of agency in quantum theory. These investigations will shed light not only on the possible ways of learning in a quantum environment, but also on the physical dimension of AI, the explainability of quantum AI, and the consistency of quantum theory.
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| Web resources: | https://cordis.europa.eu/project/id/101055129 |
| Start date: | 01-10-2022 |
| End date: | 30-09-2027 |
| Total budget - Public funding: | 2 472 435,00 Euro - 2 472 435,00 Euro |
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Original description
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. 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. 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. We will develop models of artificial agency which are beneficial for basic research, both from a practical and a foundational perspective. We will develop classical artificial learning agents that can be used, e.g., for adaptive schemes of quantum error correction and integrated in large-scale quantum computing architectures, the design of complex quantum communication networks, and the study of novel computational phases of matter. The models of learning agents that we develop will facilitate applications towards AI-driven quantum experiment and scientific discovery. Our focus will be on transparent and interpretable models for artificial agency that are beneficial if not needed for basic science. These models can be used in future hybrid laboratories where human researchers will interact with AI assistant systems. On the foundational side, we will investigate quantum agents and the role of agency in quantum theory. These investigations will shed light not only on the possible ways of learning in a quantum environment, but also on the physical dimension of AI, the explainability of quantum AI, and the consistency of quantum theory.Status
SIGNEDCall topic
ERC-2021-ADGUpdate Date
09-02-2023
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