Your research is located at the intersection of quantum information processing, machine learning, and philosophy. What specific developments are you working on?
Hans J. Briegel: We investigate the application of artificial intelligence (AI), that is, models of adaptive learning agents, some of which we are developing ourselves, to problems in quantum physics. The form of machine learning we’re dealing with here can be summarized by the term reinforcement learning. Algorithms from this field did a spectacularly good job in helping AI systems master the board game Go and are also playing an increasingly important role in everyday applications. We are studying how we can apply and develop these methods for use in quantum laboratories. Concrete applications exist, for example, in quantum information processing. Quantum computers are extremely complex and fragile systems with a large number of interacting parts. Quantum bits can inadvertently interact with each other or with the environment. The adaptive agents we are developing can help keep quantum computers functional by guiding the use of quantum error correction methods and adapting them to a specific implementation in the lab.
Can these adaptive agents also be formulated as algorithms for quantum computers themselves?
Briegel: That is a second research approach we’re taking. We are trying to exploit quantum mechanics to develop better learning methods for AI. So the interplay between AI methods and quantum physics goes both ways. We’ll be exploring this further in a new project funded by the European Research Council. An obvious question in this context is whether machine learning can be accelerated using quantum technologies. Several years ago, we were able to show that our learning agent model can be quantized in a natural way. However, research in this area is still in the very early stages. Another fundamental question is what impact artificial intelligence will have on basic research and science in the future. That’s another topic we’re working on – and one that’s particularly important to me.
What developments do you predict in this area?
Briegel: Some research groups are already working on the idea of self-running labs. This means that research itself will be automated to a certain degree, and in the future, some subtasks will be carried out by specialized AI systems. This is a dynamic new field that will eventually transform science as we know it. It is therefore far from clear what our laboratories and theoretical work will look like in a few decades. It is important that we do not settle for AI systems that are consulted like some kind of oracle and only present us with results.
It is important that we do not settle for AI systems that are consulted like some kind of oracle and only present us with results.Hans J. Briegel
AI systems built on deep neural networks alone have so far mostly been black-box systems, where it is difficult to understand how the output is related to the input, for example, what exactly causes an image to be classified one way or another. This might be good enough for some applications, like weather apps, for example. In research, however, we want to understand how we arrive at a particular result. That’s what we also want from an AI in the future. One possibility would be to use the funds from the FWF Wittgenstein Award to further develop transparent, explainable AI in basic research, particularly in quantum physics.
Why is this research particularly relevant?
Briegel: It contributes to a better understanding of the principal scope of artificial intelligence. Will it remain primarily an optimization and classification tool, or can it be developed significantly further – is there more to come? One key question is whether artificial agents are theoretically capable of creating models themselves. Creating models is a skill we use to summarize our experiences, make predictions, test them in new experiments, and help us make sense of the world. It is part of the basic underpinnings of what we call understanding. If we had AI systems that could learn by creating their own models and also communicate those models, that would be a big step forward.
What motivates you in your work?
Briegel: As a physicist, naturally I want to understand what intrinsically holds the world together (laughs). In the past, my research activities have also been characterized by extensive interaction with other disciplines – especially with philosophy. Thomas Müller at the University of Konstanz and I are investigating, for example, how to better understand the idea of freedom and agency in the context of quantum physics. This is a philosophical question, but I think that physics can contribute something to answering it. Quantum mechanics is the first physical theory in which the role of the observer is described mathematically in a formal way. However, it’s not clear what constitutes an “observer”, and whether the term really conveys the entire meaning. After all, experimenting is more than just observing. A better question, then, is what constitutes a system with the capability to act and how do artificial learning agents fit into this classification? This is a crucial question in terms of the development of artificial intelligence and what it will mean for us.
Hans J. Briegel is a professor and head of the research group Quantum Information & Computation at the Institute for Theoretical Physics at the University of Innsbruck. He studied physics and philosophy in Munich and Edinburgh. Other stages in his career include a postdoctoral fellowship at Harvard University, a visiting professorship at the University of Konstanz, and many years of leading a research group as Scientific Director at the Institute of Quantum Optics and Quantum Information (IQOQI) of the Austrian Academy of Sciences (OeAW) in Innsbruck. 2022 Hans Briegel was awarded an ERC Advanced Grant.
About the project: Quantum information systems and artificial intelligence are not only essential elements in current technological development. In the future, they will also have a fundamental influence on scientific working methods by allowing the acceleration and extensive automation of knowledge generation. With his team, Hans J. Briegel not only investigates fundamental aspects and potentials of quantum information systems and autonomously acting artificial intelligence, but also works on philosophical questions concerning AI and the principal capacity of physical systems to act.