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SUMMARY:Joint MARVEL-QSE seminar: Andrew Briggs (Oxford)
DTSTART:20230616T110000
DTEND:20230616T120000
DTSTAMP:20260513T015834Z
UID:b8cd0af038dd1cf8477409cdc86bce378086e3f9b7daf953d167dd38
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Andrew Briggs (Oxford)\nhttps://epfl.zoom.us/j/672927281
 20\nPasscode: 8486\n\nProf. Andrew Briggs\nUniversity of Oxford\n\nAcceler
 ating Quantum Technologies with Machine Learning\nA basic challenge in qua
 ntum computing is to tune and characterise qubits on an ever-expanding sca
 le [1]. We have developed machine learning methods for quantum technologie
 s\, which are able to learn how to do this more efficiently than even expe
 rienced humans [2]. This requires moving beyond methods which demand large
  amounts of readily available data\, because in quantum technologies the d
 ata are often sparse and costly to acquire.\nThe machine learning is requi
 red not simply to classify the measurements which have been taken but to d
 ecide what parameters to set next [3]. Without being reprogrammed\, the ma
 chine is able to learn how to tune different architectures [4]\, and to ch
 aracterise the variability of nominally identical devices [5].\nTo meet th
 e commercial need for the techniques developed in our laboratory\, we foun
 ded a new company [6]. The product will be launched at the end of August.\
 nAs scientists we have the responsibility and the privilege of advocating 
 the responsible use of the progress to which we contribute. This calls for
  insight from science and wisdom from other disciplines to learn how toget
 her we can seek to promote human flourishing in times which seem to be inc
 reasingly subject to uncertainty [7].\n[1] Efficiently measuring a quantum
  device using machine learning. npj Quantum Information 5\, 79 (2019)\n
 [2] Machine learning enables completely automatic tuning of a quantum devi
 ce faster than human experts. Nat. Commun. 11\, 4161 (2020)\n[3] Quantum
  device fine-tuning using unsupervised embedding learning. New J. Phys. 
 22\, 095003 (2020)\n[4] Cross-architecture tuning of silicon and SiGe-base
 d quantum devices using machine learning. arXiv:2107.12975\n[5] Bridging 
 the reality gap in quantum devices with physics-aware machine learning. a
 rXiv:2111.11285\n[6] https://quantrolox.com/\n[7] Human Flourishing: Sci
 entific insight and spiritual wisdom in uncertain times. Oxford University
  Press (2021)\n\nAbout the speaker\nAndrew Briggs is Professor Emeritus of
  Nanomaterials at the University of Oxford and Executive Chairman of Qu
 antrolOx. His research interests focus on nanomaterials for quantum techno
 logies and their incorporation into practical devices\, and the nanoscale 
 thermodynamics of timekeeping and learning. From 2002-2009\, he directed t
 he UK Interdisciplinary Research Collaboration in Quantum Information Proc
 essing. In 2021 he co-founded QuantrolOx to commercialise machine learning
  for tuning and characterizing quantum devices\, with performance that gre
 atly exceeds what is feasible for humans.\nHe is a Fellow of St Anne’s C
 ollege\, Oxford\, Fellow of Wolfson College\, Oxford\, Honorary Fellow of 
 the Royal Microscopical Society\, Fellow of the Institute of Physics\, Fel
 low of the Cambridge Philosophical Society\, Fellow of the International S
 ociety for Science and Religion\, and Member of Academia Europaea. He has 
 over 650 publications\, with nearly 30\,000 citations.\nHis books for a ge
 neral readership include “The Penultimate Curiosity: How Science Swims i
 n the Slipstream of Ultimate Questions”\, for which there is a document
 ary film and a six-book series for children\; “It Keeps Me Seeking: Th
 e Invitation from Science\, Philosophy and Religion”\; and “Human Flou
 rishing: Scientific Insight and Spiritual Wisdom in Uncertain Times”. Hi
 s most recent book carries endorsements by the Archbishop of Canterbury an
 d the Astronomer Royal.\n\n 
LOCATION:MED 2 1124 https://plan.epfl.ch/?room==MED%202%201124 https://epf
 l.zoom.us/j/67292728120?pwd=SGNBMVl3UGhwbWtqT2kvTjh0d2FKQT09
STATUS:CONFIRMED
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