<\/p>\n
Many people will know of Walter\u2019s sad death on August 18th , having only recently moved house from Lancaster. \u00a0Walter was a founder member of the department in 1964, a keen Scottish country dancer, and was for several years before retirement the University\u2019s senior pro-VC.\u00a0 He was one of the most reliable and fair-minded gentlemen I have known. His kindness and honesty attracted friends and he was universally respected.<\/p>\n
A short memorial event will take place this Saturday October 5 at 2pm in the United Reformed Church on Bowerham Road (near the \u201cBowerham top\u201d junction with Coulston Road). \u00a0His wife Barbara, some family and several Lancaster friends will contribute.<\/p>\n
Tony Gu\u00e9nault<\/strong><\/p>\n <\/p>\n World Mental Health Day, 10th October\u00a0<\/strong> The Mental Health Foundation<\/a> invites you to consider how you can create learning opportunities for your colleagues, raise their awareness and overall level of understanding of mental health and specifically address the stigma around suicide. New Menopause Group & Policy<\/strong> Members of the Lancaster University Women\u2019s Network<\/a> have formed a new group to raise awareness and develop a policy for the University on the Menopause. Contact Pam Pickles<\/a> to find out more and help shape the new policy.<\/p>\n Bright Club at The Borough, October 17th<\/strong> Defying Dementia Day, 19th October<\/strong> Black History Month, October<\/strong> Collaboration Cafe on third Tuesdays Work in Progress Space, Alexandra Square<\/strong> Supporting Staff to Support Students<\/strong> Amanda Ross<\/strong><\/p>\n <\/p>\n The electron spin of individual electrons in quantum dots could serve as the smallest information unit of a quantum computer. Scientists from the Universities of Basel, Oxford and Lancaster have developed an algorithm that can be used to measure quantum dots automatically. Writing in the <\/em>Nature-family journal npj Quantum Information, they describe how they can speed up this hugely time-consuming process by a factor of four with the help of machine learning. Their approach to the automatic measurement and control of qubits therefore represents a key step toward their large-scale application.<\/em><\/p>\n For several years, the electron spin of individual electrons in a quantum dot has been identified as an ideal candidate for the smallest information unit in a quantum computer, otherwise known as a qubit.<\/p>\n Controlled via applied voltages<\/strong> For each quantum dot, the applied voltages must therefore be tuned carefully in order to achieve the optimum conditions. When several quantum dots are combined to scale the device up to a large number of qubits, this tuning process becomes enormously time-consuming because the semiconductor quantum dots are not completely identical and must each be characterized individually.<\/p>\n Automation thanks to machine learning <\/strong> First, the scientists train the machine with data on the current flowing through the quantum dot at different voltages. Like facial recognition technology, the software gradually learns where further measurements are needed with a view to achieving the maximum information gain. The system then performs these measurements and repeats the process until effective characterization is achieved according to predefined criteria and the quantum dot can be used as a qubit.<\/p>\n \u201cFor the first time, we\u2019ve applied machine learning to perform efficient measurements in gallium arsenide quantum dots, thereby allowing for the characterization of large arrays of quantum devices,\u201d says Dr. Natalia Ares from the University of Oxford. \u201cThe next step at our laboratory is now to apply the software to semiconductor quantum dots made of other materials that are better suited to the development of a quantum computer,\u201d adds Professor Dr. Dominik Zumb\u00fchl from the University of Basel. \u201cWith this work, we\u2019ve made a key contribution that will pave the way for large-scale qubit architectures.\u201d According to Dr. Edward Laird from Lancaster University, \u201cA machine is so much faster than a human at making measurement decisions that it could let us run experiments that we wouldn\u2019t even be able to set up unaided.\u201d<\/p>\n Original source:<\/strong> Further information:<\/strong> Tel: +44 1524 510831 Dr. Natalia Ares Tel: +44 1865 273719 Prof. Dr. Dominik M. Zumb\u00fchl Tel: +41 (0)61 207 36 93Wellbeing Copy September 2019<\/strong><\/h2>\n
\nWorld Mental Health Day is an opportunity for all of us to raise awareness of mental health issues and advocate against social stigma.\u00a0The theme of this year\u2019s event on is suicide and suicide prevention.<\/p>\n
\nMental health charity MIND have produced some useful resources on \u2018what are suicidal feelings<\/a>?\u2019 and you can see our wellbeing pages for information about support available through the Employee Assistance Programme<\/a> and specialist support groups<\/a>.<\/p>\n
\nAt Lancaster University, according to the June 2019 staff download, there are 818 female staff aged 46 and over out of a total workforce of 3,633. This means that 22.5% of staff may be going through the menopause or experiencing perimenopausal symptoms<\/a> at any time. In addition, between 1% and 10% of women experience an early or premature menopause and so may be trying to deal with the same symptoms.<\/p>\n
\nLaugh as you learn from a wide range of researchers braving their first stand-up gig. Compered and headlined by founder of Bright Club and Mr Science Showoff himself, Steve Cross. All ticket sales to Homestart Morecambe and Lancaster to provide Christmas food, toys and treats for local families who really struggle. Read More<\/a><\/p>\n
\nThis one-day event on \u2018Living with dementia and developments in research,\u2019 run by our Defying Dementia team<\/a>, is an opportunity to bring together everybody who has an interest in dementia \u2013 people living with dementia, staff & volunteers of dementia associations, family members, clinicians, care professionals and scientists \u2013 to share and learn from one another. If you can\u2019t make the event, check out their resources page<\/a>. Register Now<\/a><\/p>\n
\nThroughout October organisations across the UK will celebrate the enormous contribution Black Britons have made to our vibrant and diverse society. You can read more and download a resource pack here<\/a>. Our Students’ Union is curating a range of student-led activity to mark Black History Month 2019. All are welcome. Read more<\/a><\/p>\n
\nMeet up with people you may or may not know on campus. Drop in to a ninety-minute open collaboration session where informal conversations with potential collaborators may develop into new research opportunities or new approaches to existing work. Get some questions answered by a Business Development Manager or Research Development Manager. Have a coffee and a cookie on us. Times for this term: 17 September 8.30 – 10am, 15 October 12.30 – 2pm, 19 November 8.30 – 10am, 17 December 12.30 – 2pm.<\/p>\n
\nWith the start of the new term, if you want to find out more about the services the Counselling and Mental Health Service offer, review their updated web pages. Read more<\/a><\/u><\/p>\nMachine learning at the quantum lab<\/strong><\/h2>\n
\nIn quantum dots made of layered semiconductor materials, individual electrons are caught in a trap, so to speak. Their spins can be determined reliably and switched quickly, with researchers keeping the electrons under control by applying voltages to the various nanostructures within the trap. Among other things, this allows them to control how many electrons enter the quantum dot from a reservoir via tunneling effects. Here, even small changes in voltage have a considerable influence on the electrons.<\/p>\n
\nNow, a collaboration between scientists from Oxford University, the University of Basel, and Lancaster University has developed an algorithm that can help to automate this process. Their machine-learning approach reduces the measuring time and the number of measurements by a factor of approximately four in comparison with conventional data acquisition.<\/p>\n
\nEfficiently measuring a quantum device using machine learning<\/a>
\nD.T. Lennon, H. Moony, L.C. Camenzind, Liuqi Yu, D.M. Zumbuhl, G.A.D. Briggs, M.A. Osborne, E.A. Laird, and N. Ares
\nnpj Quantum Information <\/em>5<\/strong> 79<\/p>\n
\n<\/a>Dr Edward Laird
\nDepartment of Physics, Lancaster\u00a0University
\nLancaster LA1 4YB, UK<\/p>\n
\ne.a.laird@lancaster.ac.uk<\/a>
\nhttp:\/\/wp.lancs.ac.uk\/laird-group\/<\/a><\/p>\n
\nMaterials Department, Oxford University
\n16 Parks Road
\nOxford, OX1 3PH, UK<\/p>\n
\nnatalia.ares@materials.ox.ac.uk<\/a>
\nhttps:\/\/www.natalia-ares.com\/<\/a><\/p>\n
\nDepartment of Physics, University of Basel
\nKlingelbergstrasse 82
\n4056 Basel, Switzerland<\/p>\n
\ndominik.zumbuhl@unibas.ch<\/a><\/p>\n