Abstract

I will start with a general introduction on quantum simulation and computation with near term processors, the state of the art and the current race for building operational quantum processors. I will then review our work with Google on quantum simulations with superconducting qubits [1] and as well as our recent works in analog quantum supremacy [2] and quantum machine learning [3] with NISQ devices. If time, I will briefly summarise our work on qubit efficient quadratic optimization algorithms [4]. The first part of the talk should be accessible to non-specialists. P. Roushan, C. Neill, ...D.G. Angelakis, and J. Martinis. Science, 01 Dec 2017: Vol. 358, Issue 6367, (2017)

signatures of many-body localization with interacting photons

[2] Quantum supremacy and quantum phase transitions J. Tangpanitanon, S. Thanasilp, M. A. Lemonde, N. Dangiam, D. G. Angelakis Phys. Rev. B 103 , 165132 (2021)

[3] Expressibility and trainability of parameterized analog quantum systems for machine learning applications J. Tangpanitanon, S. Thanasilp, M. A. Lemonde, N. Dangiam, D. G. Angelakis Phys. Rev. Research 2, 043364 2020

[4] Qubit efficient algorithms for binary optimization problems B. Tan, M. A. Lemonde, S. Thanasilp, J. Tangpanitanon, D. G. Angelakis Quantum 5, 454 (2021) ]

Where: Virtually on Zoom https://us02web.zoom.us/j/88908652048?pwd=MDV3N3k0YnNWMlhKOEk1NDZlUEtaUT09