Machine-learning technique used to pinpoint quantum errors

Researchers at the University of Sydney and quantum control startup Q-CTRL today announced a way to identify sources of error in quantum computers through machine learning, providing hardware developers the ability to pinpoint ...

New framework applies machine learning to atomistic modeling

Northwestern University researchers have developed a new framework using machine learning that improves the accuracy of interatomic potentials—the guiding rules describing how atoms interact—in new materials design. The ...

Machine learning accelerates cosmological simulations

A universe evolves over billions upon billions of years, but researchers have developed a way to create a complex simulated universe in less than a day. The technique, published in this week's Proceedings of the National ...

Streamlining the process of materials discovery

Developing new materials and novel processes has continued to change the world. The M3I3 Initiative at KAIST has led to new insights into advancing materials development by implementing breakthroughs in materials imaging ...

A new way to visualize mountains of biological data

Studying genetic material on a cellular level, such as single-cell RNA-sequencing, can provide scientists with a detailed, high-resolution view of biological processes at work. This level of detail helps scientists determine ...

Solving 'barren plateaus' is the key to quantum machine learning

Many machine learning algorithms on quantum computers suffer from the dreaded "barren plateau" of unsolvability, where they run into dead ends on optimization problems. This challenge had been relatively unstudied—until ...

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