Feedback-based quantum optimization
Quantum computing promises to change how numerical algorithms are designed and executed across a wide range of applications. However, current devices remain constrained by a range of error sources that limit scalability beyond classical reach, and even under idealized hardware, only a restricted class of algorithms is known to offer provable or expected computational speedups. Optimization algorithms are a promising direction, but current approaches suffer from sensitivity to errors, high gate complexities and long computational times.
FeedbacQ addresses this gap by exploiting feedback across multiple layers of the quantum computing stack. We will expand the practical applicability of quantum optimization algorithms by developing a systematic framework for mapping generic optimization problems to quantum computers. To solve these optimization problems with reduced circuit depth and sampling requirements, we will devise new algorithms for quantum optimization which exploit non-unitary components such as weak measurements with feedback and open system dynamics. To tackle errors, we will design continuous-time quantum error correction protocols via quantum feedback control as well as
learning-based gate synthesis techniques. To realize the developed algorithms and error correction protocols on the circuit level, we will develop tailored circuit compilation techniques for non-unitary components.
The results will be experimentally evaluated on real quantum hardware, including a real gate-based device present at one of our partner institutions.
Interdisciplinary Approach
Our project is interdisciplinary and encompasses PIs and research topics spanning physics, mathematics, engineering, and computer science. The central underlying theme for achieving our project goals and our common language is control theory, in particular the application of feedback and measurement.
FeedbacQ Team
- Nina Amini (L2S, CentraleSupélec, France)
- Christiane Koch (Freie Universität Berlin, Germany)
- Carlo Novara (Politecnico di Torino, Italy)
- Andrea Simonetto (ENSTA, France)
- Gianluca Valentino (University of Malta, Malta)
FeedbacQ is funded within the QuantERA Programme, a transnational network of national research funding organizations in quantum technologies, supported by the European Union. National funding is provided by the participating agencies in Germany, France, Italy, and Malta. Open positions will be announced soon.