March 2021: Our new work with Daniel, who joined our group recently, is published at Applied Physics Letters! We merge machine learning with topology and quantum physics for applications at material design. We use a technique known as ‘persistent homology’ to show that it can calculate how nano-structured materials will transmit light. That could make it easier to design man-made photonic crystals, fabricated for example with layers of materials, etched patterns or embedded structures, for specific applications. Check out CQT’s highlight here

December 2020: We have been busy this month. Three of our works are out, two preprints and one publication ! (1) New work out on machine learning with topology (persistent homology) for probing exotic topological bands in condensed matter physics. (2) Our preprint on one on analog quantum machine learning with near term quantum processors is published in Physical Review Research. (3) Our new work on quantum supremacy and quantum phase transition is out as preprint! We propose how our quantum supremacy proposal with driven analog many-body systems can also be used for probing phases of matter.

November 2020: Dimitris was interviewed on Deutsch Welle News Asia on the recent quantum supremacy experiment in China. Check out the interview here.

September 2020: Two new PhD students and a Senior Research Fellow join us. Chee, Beng Yee and Daniel. Welcome guys!

July 2020: New work on qubit efficient algorithms for solving binary optimization problems is out as preprint! We propose and analyze a set of variational quantum algorithms for solving quadratic unconstrained binary optimization problems where a problem consisting of n classical variables can be implemented on O(log(n))number of qubits.

May 2020: Our new work on analog quantum processors for machine learning is out as a preprint! We discuss how the interplay between external driving and disorder in quantum many-body systems can dictate their trainability and expressibility and apply it to solve a generative modelling problem.