Nav: Home

Machine learning at the quantum lab

September 26, 2019

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.

Controlled via applied voltages

In 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.

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.

Automation thanks to machine learning

Now, scientists from the Universities of Oxford, Basel, and Lancaster have 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.

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.

"For the first time, we've applied machine learning to perform efficient measurements in gallium arsenide quantum dots, thereby allowing for the characterization of large arrays of quantum devices," says Dr. Natalia Ares from the University of Oxford. "The 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," adds Professor Dr. Dominik Zumbühl from the Department of Physics and the Swiss Nanoscience Institute at the University of Basel. "With this work, we've made a key contribution that will pave the way for large-scale qubit architectures."
Original source:

Efficiently measuring a quantum device using machine learning
D.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
npj Quantum Information, doi: 10.1038/s41534-019-0193-4

Further information:

Dr. Natalia Ares
Materials Department, Oxford University
16 Parks Road
Oxford, OX1 3PH, UK
Tel: +44 (0)1865 273719

Prof. Dr. Dominik M. Zumbühl
Department of Physics, University of Basel
Klingelbergstrasse 82
4056 Basel, Switzerland
T +41 (0)61 207 36 93

Dr. Edward Laird
Department of Physics, Lancaster University
Office: A051, A - Floor, Physics Building
Lancaster LA1 4YB, UK
Tel: +44 (0)1524 510831

Swiss Nanoscience Institute, University of Basel

Related Quantum Dots Articles:

What a pair! Coupled quantum dots may offer a new way to store quantum information
Researchers at the National Institute of Standards and Technology (NIST) and their colleagues have for the first time created and imaged a novel pair of quantum dots -- tiny islands of confined electric charge that act like interacting artificial atoms.
Spinning quantum dots
A new paper in EPJ B presents a theoretical analysis of electron spins in moving semiconductor quantum dots, showing how these can be controlled by electric fields in a way that suggests they may be usable as information storage and processing components of quantum computers.
Towards high quality ZnO quantum dots prospective for biomedical applications
Scientists from Warsaw together with colleagues from Grenoble have moved a step closer to creating stable, high quality colloidal zinc oxide quantum dots (ZnO QDs) for use in modern technologies and nanomedicine.
Controlling the charge state of organic molecule quantum dots in a 2D nanoarray
Australian researchers have fabricated a self-assembled, carbon-based nanofilm where the charge state (ie, electronically neutral or positive) can be controlled at the level of individual molecules.
Modified quantum dots capture more energy from light and lose less to heat
Los Alamos National Laboratory scientists have synthesized magnetically-doped quantum dots that capture the kinetic energy of electrons created by ultraviolet light before it's wasted as heat.
Using quantum dots and a smartphone to find killer bacteria
A combination of off-the-shelf quantum dot nanotechnology and a smartphone camera soon could allow doctors to identify antibiotic-resistant bacteria in just 40 minutes, potentially saving patient lives.
Synthesizing single-crystalline hexagonal graphene quantum dots
A KAIST team has designed a novel strategy for synthesizing single-crystalline graphene quantum dots, which emit stable blue light.
US Naval Research Laboratory 'connects the dots' for quantum networks
Researchers at the US Naval Research Laboratory developed a novel technique that could enable new technologies that use properties of quantum physics for computing, communication and sensing, which may lead to 'neuromorphic' or brain-inspired computing.
Quantum rebar: Quantum dots enhance stability of solar-harvesting perovskite crystals
Engineering researchers have combined two emerging technologies for next-generation solar power -- and discovered that each one helps stabilize the other.
2D gold quantum dots are atomically tunable with nanotubes
Gold atoms ski along boron nitride nanotubes and stabilize in metallic monolayers.
More Quantum Dots News and Quantum Dots Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Teaching For Better Humans 2.0
More than test scores or good grades–what do kids need for the future? This hour, TED speakers explore how to help children grow into better humans, both during and after this time of crisis. Guests include educators Richard Culatta and Liz Kleinrock, psychologist Thomas Curran, and writer Jacqueline Woodson.
Now Playing: Science for the People

#556 The Power of Friendship
It's 2020 and times are tough. Maybe some of us are learning about social distancing the hard way. Maybe we just are all a little anxious. No matter what, we could probably use a friend. But what is a friend, exactly? And why do we need them so much? This week host Bethany Brookshire speaks with Lydia Denworth, author of the new book "Friendship: The Evolution, Biology, and Extraordinary Power of Life's Fundamental Bond". This episode is hosted by Bethany Brookshire, science writer from Science News.
Now Playing: Radiolab

One of the most consistent questions we get at the show is from parents who want to know which episodes are kid-friendly and which aren't. So today, we're releasing a separate feed, Radiolab for Kids. To kick it off, we're rerunning an all-time favorite episode: Space. In the 60's, space exploration was an American obsession. This hour, we chart the path from romance to increasing cynicism. We begin with Ann Druyan, widow of Carl Sagan, with a story about the Voyager expedition, true love, and a golden record that travels through space. And astrophysicist Neil de Grasse Tyson explains the Coepernican Principle, and just how insignificant we are. Support Radiolab today at