We establish a dataset of over $1.6\times10^4$ experimental images of Bose--Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About $33~\%$ of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet -- an implementation of a physics-informed ML data analysis framework -- consisting of a convolutional-neural-network-based classifier and OD as well as a statistically motivated physics-informed classifier and a quality metric. This technical note constitutes the definitive reference of the dataset, providing an opportunity for the data science community to develop more sophisticated analysis tools, to further understand nonlinear many-body physics, and even advance cold atom experiments.

A physicist in the UK has created the fifth state of matter from her living room during the coronavirus lockdown using quantum technology. Dr Amruta Gadge from the University of Sussex created a Bose-Einstein Condensate (BEC) – a state of matter where extremely cold atoms clump together and act as if they were a single entity. Despite working from her living room two miles away from the lab, Dr Gadge was able to use her computer to control lasers and radio waves and create the BEC. Researchers at the university's quantum department think it's the first time someone has established a BEC remotely in a lab that didn't previously have one. The achievement could provide a blueprint for using a computer to operate quantum technology remotely, in inaccessible environments such as space or underwater. Quantum technology makes use of the spooky effects of quantum physics to vastly speed up information processing, which could lead to the most powerful computer on Earth.

We model a piece of text of human language telling a story by means of the quantum structure describing a Bose gas in a state close to a Bose-Einstein condensate near absolute zero temperature. For this we introduce energy levels for the words (concepts) used in the story and we also introduce the new notion of 'cogniton' as the quantum of human thought. Words (concepts) are then cognitons in different energy states as it is the case for photons in different energy states, or states of different radiative frequency, when the considered boson gas is that of the quanta of the electromagnetic field. We show that Bose-Einstein statistics delivers a very good model for these pieces of texts telling stories, both for short stories and for long stories of the size of novels. We analyze an unexpected connection with Zipf's law in human language, the Zipf ranking relating to the energy levels of the words, and the Bose-Einstein graph coinciding with the Zipf graph. We investigate the issue of 'identity and indistinguishability' from this new perspective and conjecture that the way one can easily understand how two of 'the same concepts' are 'absolutely identical and indistinguishable' in human language is also the way in which quantum particles are absolutely identical and indistinguishable in physical reality, providing in this way new evidence for our conceptuality interpretation of quantum theory.

Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment. The experiment, developed by physicists from The Australian National University (ANU) and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from the ANU Research School of Physics and Engineering. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.

Researchers recently used an artificial intelligence to run a complex experiment, which it learnt to perform from scratch in under an hour. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out," said co-lead researcher Paul Wigley from the Australian National University Research School of Physics and Engineering. This suggests that even physicists are on track to having their jobs augmented if not outright captured by artificial intelligence. The experiment involved the creation of a Bose-Einstein condensate, an extremely cold gas trapped in a laser beam. At a billionth of a degree Kelvin, it is even colder than outer space.

Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment. The experiment, developed by physicists from The Australian National University (ANU) and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from the ANU Research School of Physics and Engineering. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.

Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment. The experiment, developed by physicists from The Australian National University (ANU) and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from the ANU Research School of Physics and Engineering. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.

In a first, a team of physicists is using artificial intelligence (AI) to run a complex experiment to create an extremely cold gas trapped in a laser beam known as a Bose-Einstein condensate -- thus replicating the experiment that won the 2001 Nobel Prize. Bose-Einstein condensates are some of the coldest places in the universe -- far colder than outer space and typically less than a billionth of a degree above absolute zero. They can be used for mineral exploration or navigation systems as they are extremely sensitive to external disturbances, which allows them to make very precise measurements such as tiny changes in the Earth's magnetic field or gravity. Indian physicist Satyendra Nath Bose, along with German-born theoretical physicist Albert Einstein, founded the basis for Bose-Einstein statistics. It describes the statistical distribution of identical particles with integer spin, now called subatomic particle or the "God particle" Boson.

Everywhere you turn these days there are more and more automated processes appearing all the time. From automatic vacuum cleaners to self-order counters at restaurants, to cars that automatically park themselves, robots are all around us in one way or another and physics is no different. In using the latest artificial intelligence to do the same tasks as people, we are not only saving time and money but saving on resources too. A recent physics experiment developed by physicists from The Australian National University (ANU) and the University of New South Wales at the Australian Defence Force Academy (UNSW ADFA) was shown to be completed by artificial intelligence (AI) just as a human would. The test was to create a replica of "Laser Beam" experiment that won the 2001 Nobel Prize and produced an extremely cold gas trapped in a laser beam (known as Bose-Einstein condensate) and the incredible AI literally taught itself how to do the experiment, from start to finish, in under one hour!

Australian physicists have used an online optimization process based on machine learning to produce effective Bose-Einstein condensates (BECs) in a fraction of the time it would normally take the researchers. A BEC is a state of matter of a dilute gas of atoms trapped in a laser beam and cooled to temperatures just above absolute zero. BECs are extremely sensitive to external disturbances, which makes them ideal for research into quantum phenomena or for making very precise measurements such as tiny changes in the Earth's magnetic field or gravity. The experiment, developed by physicists from ANU, University of Adelaide and UNSW ADFA, demonstrated that "machine-learning online optimization" can discover optimized condensation methods "with less experiments than a competing optimization method and provide insight into which parameters are important in achieving condensation," the physicists explain in an open-access paper in the Nature group journal Scientific Reports. Optical dipole trap used in the experiment, showing the three laser beams and the condensate (red-yellow oval in blue square) (credit: P. B. Wigley et al./Scientific Reports) The team cooled the gas to around 5 microkelvin.