Presenting CQT's Annual Report for 2020

 

In his first Director's letter, José Ignacio Latorre writes, “One lesson we all know: science is truly unstoppable. The year of the pandemic has been a time of dramatic actions in quantum research.” Rounding up a year like no other in CQT's history, our report highlights research that continued through a pandemic and the handover of our Directorship. We hope you find it informative and interesting.



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Meet A CQTian: José Ignacio Latorre

“When I look back at what it means to be a scientist, I always divide my life into three parts. One is generating knowledge, the second is passing it to new generations – this can be by teaching and outreach – and the third is making the first steps to translation. These are for me the three pillars of being a scientist and I think it is the same for CQT,” says José Ignacio. Read about José Ignacio’s views on the quantum landscape and his vision for CQT in this interview.

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Logic can replace physics in deriving fluctuation relations

Physics takes a back seat in a new method to construct fluctuation relations proposed by CQT’s Valerio Scarani and his collaborator Francesco Buscemi. They find they can use a concept known as logical retrodiction instead. “We were very apprehensive that someone would tell us the logical retrodiction approach was known already, or that this has been proved wrong. But strangely enough, everybody is saying that this is new, and for this we are very proud,” Valerio says.

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A surprise twist in their QKD  workshop engaged school students, inspiring CQT researchers to publish details of the hands-on demo they designed.

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CQT’s Charles Lim and Artur Ekert contribute to a mathematical analysis that can certify secure keys from less quantum measurement data.

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Machine learning technique could aid photonic crystal design

Designers of photonic crystals might be able to better predict the properties of new light-controlling materials with an approach proposed by CQT Senior Research Fellow Daniel Leykam and Principal Investigator Dimitris Angelakis. The approach uses a technique from machine learning known as ‘persistent homology’ and could make it easier to design man-made photonic crystals for specific applications.

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Centre for Quantum Technologies, NUS
Science Drive 2 Block S15-03-18
Singapore 117543

cqtsec@nus.edu.sg
+65 6516 2818