Cross-fertilizing machine learning and synthetic quantum systems

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Following the initial development of quantum mechanics, scientific discovery accelerated with major technological inventions such as the transistor, superconductivity, the laser, and other technological advances. Currently, we are at the dawn a second quantum revolution, where quantum information processing is exploited to generate new technology. A central concept in quantum physics and quantum technologies is the many--body wave function, which is one of the most complex mathematical objects in physics. Remarkably, the power of future quantum technologies relies on our ability to accurately control the wave function of brittle quantum devices. As part of a large trend in several scientific areas, quantum many-body physicists have turned their attention to machine learning ideas, which are currently revolutionizing scientific discovery in areas as diverse as chemistry, materials science, high energy physics, and other. In a series of recent developments, Carrasquilla's research has taken the fundamental viewpoint that the state of a quantum system, whose exponential complexity is reminiscent of the "curse of dimensionality" encountered in machine learning, can be understood as a "generative model" of microscopic phenomena. The most important long--term goal of Carrasquilla's research program is to expand this viewpoint into a broader machine learning perspective on the quantum many--body problem and to explore its applications in quantum technology. The goals advanced by this Discovery Grant include the design of machine learning models inspired by the dynamics of physical quantum systems, and the development of neural--network representations of quantum states. Applications of this research include quantum state reconstruction, as well as applications in condensed matter physics and quantum chemistry in the form of exact and approximate solutions to the Schrodinger equation.    At the heart of this program is a substantial research component on artificial intelligence and deep learning. This is either because quantum technology may boost artificial intelligence or because machine learning may propel the design of quantum devices and algorithms. Ultimately,  my research strategy and long--term vision are designed to advance the development of Canada's fundamental research on quantum science, artificial intelligence, and the emerging quantum industry. This strategy includes the training of Master's and PhD students who will pioneer methods hybridizing modern theoretical and numerical methods in many-body physics with artificial intelligence techniques, with impact in areas such as condensed matter physics, quantum simulation, materials science, and quantum chemistry. In turn, this will generate state--of--the--art artificial intelligence research with an emphasis on quantum science, and a specialized workforce ready to tackle research problems relevant to the emerging quantum artificial intelligence industry in Canada.