This talk is motivated by the question: why do we put so much effort and investment into quantum computing? A short answer is that we expect the existence of quantum computational advantage, especially for practical problems. In 2019, Google claimed to achieve quantum advantage, also known as quantum supremacy. In the first part of this talk, we question this claim and reveal fundamental limitations in their approach by constructing efficient classical algorithms to solve the same task.
Due to the shortcomings of current protocols, it is imperative to design the next generation of experiments with a more solid theoretical foundation, and ideally to find quantum advantage on problems with practical applications. In the second part of this talk, we propose a new neural sequence quantum model for language translation with better expressive power than any reasonable classical neural network. This protocol is based on quantum contextuality. Finally, I will briefly mention another potential approach: quantum algorithms for combinatorial optimization problem.
Bio: Xun Gao is a postdoc at Harvard (MPHQ fellowship). Xun received his PhD from Tsinghua University. His work explores the power and applications of near-term quantum computers, including quantum machine learning, quantum optimization algorithm and simulation of noisy quantum devices.
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