![diskmark 64 diskmark 64](https://gramfile.com/wp-content/uploads/2018/09/CrystalDiskMark-Screenshot-550x300.jpg)
Subsequently, Bennewitz and her colleagues used a variational Monte Carlo (VMC) algorithm to improve the existing representation of the unknown ground state. NQST is a machine-learning approach that can reconstruct complex quantum state by analyzing a limited number of experimentally collected measurements. First, the team used a technique known as neural quantum state tomography (NQST) to train a so-called NQS ansalz to represent an approximate ground state prepared by a noisy quantum device. Neural error mitigation (NEM), the new strategy devised by the researchers, has two key components or steps. Bennewitz and her colleagues wrote in their paper.
![diskmark 64 diskmark 64](http://www.thessdreview.com/wp-content/uploads/2018/11/Samsung-860-QVO-SATA-3-SSD-18.jpg)
"We introduce neural error mitigation, which uses neural networks to improve estimates of ground states and ground-state observables obtained using near-term quantum simulations," Elizabeth R. This strategy, introduced in a paper published in Nature Machine Intelligence, is based on machine-learning algorithms. Researchers at 1QB Information Technologies (1QBit), University of Waterloo and the Perimeter Institute for Theoretical Physics have recently developed neural error mitigation, a new strategy that could improve ground state estimates attained using quantum simulations. However, near-term quantum computing approaches are still limited by existing hardware components and by the adverse effects of background noise. Some quantum computers developed over the past few years have proved to be fairly effective at running quantum simulations.