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Abstract:
The theory of fault-tolerant quantum computing promises tremendous opportunities with clear quantum advantages for certain computational tasks. However, the development of full-fledged quantum computing hardware remains a long-term prospect. Meanwhile, noisy intermediate-scale quantum (NISQ) computers are expected to be available in the near future. These quantum devices can execute only a limited size of quantum circuits reliably due to noise but can surpass the capabilities of classical digital computers. An important issue now is to find problems and applications for which the near-term quantum technology can bring an advantage. On the other hand, the world is rapidly evolving into the big data era, in which we are flooded with a large volume of data every day, increasing at an overwhelming rate. Developing efficient computational tools to extract useful information from big data is a critical problem that our society faces now.
In this talk, I will show that quantum computing opens up enormous opportunities for data science and artificial intelligence. In particular, I will present quantum machine learning algorithms that can enhance previous methods for kernel-based supervised learning, convolutional neural network, and unsupervised clustering. Moreover, I will present a quantum algorithm that can improve existing data generation and sampling methods for simulating discrete stochastic processes, which has potential applications in finance. Finally, I will also discuss that machine learning techniques can help in the development of quantum computing technology. In particular, I will show how deep learning can be used to reduce quantum readout error, which is an important step towards building reliable quantum hardware.