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

许多相互作用的粒子的量子行为导致了奇异量子材料的出现，这些材料有望使我们的交通工具、计算机和电源更快、更节能、更便宜。不幸的是，我们没有量子多体问题的解析解，而现实的数值模拟也远远超出了世界超级计算机的能力范围。我们的模拟和现实世界之间的鸿沟让我们在黑暗中寻找量子材料所承诺的社会革命。近年来，围绕量子计算的炒作不断升级，认为它可以解决许多尚未解决的问题，包括量子多体问题。量子计算为基于量子多体系统的精确工程和控制的计算提供了一个全新的范例，实验已经超出了概念验证。虽然量子计算机仍处于起步阶段，但我们已经在日常生活中体验到了机器学习的影响。鉴于量子计算的潜力，探索量子技术如何培育更强大的机器学习工具是很自然的，反过来，机器学习如何促进量子计算机的使用。这个跨学科的研究项目有一个雄心勃勃的目标，即利用近量子计算机实现实际的量子优势。我们将建立量子计算机的实际应用，超越经典数值方法的访问。该计划旨在利用和发展量子纠缠，量子计算和机器学习之间的关系。我们将这些关系置于我们提案的中心，以产生新的方法来面对量子体问题，这可能最终指导新量子材料的合成。

The quantum behaviour of many interacting particles leads to the emergence of exotic quantum materials, which promise to makeour transport, computers, and power supplies faster, more energy efficient, and cheaper to run. Unfortunately, we have no analyticsolution to the quantum many-body problem and realistic numerical simulations are far beyond the reach of the world'ssupercomputers. This chasm between our simulations and the real-world leaves us searching in the dark for the societal revolutionspromised by quantum materials.In recent years, hype has been escalating around quantum computing as a solution to many unsolved problems, including thequantum many-body problem. Quantum computing provides a completely new paradigm for computation based on the preciseengineering and control of quantum many-body systems, with experiments already beyond proof-of-concept. While quantumcomputers are still in their infancy, we are already experiencing in our everyday lives the impact of machine learning. Given thepotential of quantum computing, it is natural to explore how quantum technology can foster even more powerful machine learningtools, 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-termquantum computers. We will establish practical applications for quantum computers, beyond the access of classical numericalmethods. 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 quantummany-body problem, which could ultimately guide the synthesis of new quantum materials.