Grant: Discovery Grants Program - Individual

Recent developments in quantum computation and the first claims of quantum supremacy, the outperformance of classical computers by quantum computers, show that overcoming conventional von-Neumann computing architectures promises novel knowledge. While these first results demonstrate achievable powers, existing quantum computers are still limited to systems of a few noisy qubits. Further exploitation requires progress in experimental realizations of qubit systems and understanding quantum many-body systems. A new research field now addresses these limitations with machine learning methods. Artificial neural networks (ANNs) are promising algorithmic models to develop advanced numerical methods simulating quantum many-body systems. These classical approximative studies promise novel knowledge impacting quantum information, quantum chemistry, and materials sciences. At the same time, the power of ANNs in optimizing operational processes can automate the control of tasks in quantum experiments such as tuning or calibration. Having followed and contributed to the field from the beginning, I am keen to continue exploring the intersection of ANNs and quantum technologies. Additionally, I am passionate about adding neuromorphic hardware to the field, enabling the implementation of neural networks on small and energy-efficient devices with a biologically inspired setup. Alongside quantum computers, neuromorphic computing devices provide another way to overcome conventional von-Neumann computing architectures. Over the next five years, I plan to establish a new research field combining machine learning, quantum technologies, and neuromorphic computing. A fundamental step towards achieving this goal is the exploration of ANN-based simulation methods for quantum systems, improving our understanding of their performance. I further explore networks of biologically inspired spiking neurons, whose emulation with electronic circuits promises accelerated simulations of qubit systems. I plan to compare implementations in state-of-the-art neuromorphic hardware with ANNs implemented in physical systems, such as photonic setups, in the context of quantum system simulation and automated control for quantum experiments. I base my research plans on a team of excellent students and postdocs, which I plan to grow over the next five years. My trainees contribute significantly to all stages of exploring the proposed new field. Working at the intersection of three impactful topics, I provide a unique training environment with close connections to leading experts in academia and industry. My close collaboration with researchers in Waterloo and Sherbrooke further gives access to local start-up ecosystems, broadening the future career paths of my trainees who specialize in a field with high potential for patentable discoveries. Establishing this novel research area will impact scientific and technological developments in the quantum sector in Canada and the world.