Towards a practical quantum advantage: Confronting the quantum many-body problem using quantum computers

The quantum behaviour of many interacting particles leads to the emergence of exotic quantum materials, which promise to make our transport, computers, and power supplies faster, more energy efficient, and cheaper to run. Unfortunately, we have no analytic solution to the quantum many-body problem and realistic numerical simulations are far beyond the reach of the world's supercomputers. This chasm between our simulations and the real-world leaves us searching in the dark for the societal revolutions promised by quantum materials. In recent years, hype has been escalating around quantum computing as a solution to many unsolved problems, including the quantum many-body problem. Quantum computing provides a completely new paradigm for computation based on the precise engineering and control of quantum many-body systems, with experiments already beyond proof-of-concept. While quantum computers are still in their infancy, we are already experiencing in our everyday lives the impact of machine learning. Given the potential of quantum computing, it is natural to explore how quantum technology can foster even more powerful machine learning tools, and in-turn how machine learning can facilitate the use of quantum computers. This cross-disciplinary research programme has the ambitious goal of realising a practical quantum advantage using near-term quantum computers. We will establish practical applications for quantum computers, beyond the access of classical numerical methods. This programme is designed to exploit and develop relationships between quantum entanglement, quantum computing, and machine learning. We put these relationships at the centre of our proposal to generate new approaches to confront the quantum many-body problem, which could ultimately guide the synthesis of new quantum materials.