The Symbiosis of Machine Learning and Quantum Information

CQIA Joint Student Seminar with Applied Physics & Material Science Department

Quantum information science has now progressed to a stage where it is not just a domain of Physics but a multidisciplinary field involving various aspects of engineering, chemistry, physics, and other disciplines along with the industrialization and scaling-up of quantum technologies. Some difficulties emerging from controlling and probing large quantum systems involving close to 100 qubits can be efficiently dealt with replying the knowledge and technologies accumulated over the past decades in different fields of engineering. For example, state tomography of complicated quantum systems containing multiple qubits can be substantially accelerated with machine learning methods as discussed in the journal club. On the other hand, whether quantum algorithms can lead to better machine learning tools has also attracted a lot of attention in the past few years as one of the topics in center of near-term quantum computing applications. Robert also discussed with students in the journal club about if this is true from the information theory point of view. This journal club was very popular since the room for the talk was filled with a mixture of students from either APhMS Department and CQIA. The convergence of physics and other fields has the potential to inspire breakthroughs and innovations beyond what was conceived in quantum information science and engineering.

Let us thank Robert for sharing his ideas with us about the combination of machine learning techniques and quantum algorithms.

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