Computers that could solve the most mind-boggling scientific problems and unknown mysteries of deep space are one step closer after the discovery of minuscule new magnets. Scientists have created 2D magnets that are just one atom thick for the first time. The incredible invention could lead to super slim computers that can perform previously impossible experiments. The key to a quantum computer is its ability to operate on the basis of a circuit not only being'on' or'off' but occupying a state that is both'on' and'off' at the same time. This is in accordance with the laws of quantum mechanics, which allow very small particles to exist in multiple'superposition' states until they are observed or disturbed.
Phase transitions can be caused by temperature fluctuations or, more exotically, by quantum fluctuations at zero temperature. To describe some of these quantum phase transitions, researchers came up with a complex theory called deconfined quantum criticality. However, subsequent numerical simulations were inconsistent with some of the predictions of the theory, leading to a debate on its validity. By using quantum Monte Carlo simulations, Shao et al. show that it is possible to reconcile numerics with the theory for a specific model of 2D quantum magnetism.
Quantum simulation, a subdiscipline of quantum computation, can provide valuable insight into difficult quantum problems in physics or chemistry. Ultracold atoms in optical lattices represent an ideal platform for simulations of quantum many-body problems. Within this setting, quantum gas microscopes enable single atom observation and manipulation in large samples. We review recent experimental progress in this field and comment on future directions.
A century-old theoretical model of magnetism is giving rise to a hybrid computer, part classical and part quantum, that may capable of solving problems that overwhelm conventional computers. The so-called Ising machine, described in 100-bit and 2000-bit versions in two reports this week in Science, could tackle optimization problems that require finding the best solution among myriad possibilities, such as predicting how a protein will fold or allotting bandwidth in cellular communications networks. The machines take their name from the Ising model, which was developed in 1920 in an attempt to explain magnetism. Curiously, many optimization problems can be mapped onto the Ising model. Now, two overlapping groups at Stanford University in Palo Alto, California, and at NTT Basic Research Laboratories in Atsugi, Japan, have developed optical machines specifically designed to solve the model, at least approximately.