This collection supports and amplifies research related to SDG 9 - Industry, innovation and infrastructure. Quantum Machine Learning is currently listed as one of the most promising candidates for ...

Quantum computing appears on track to help companies in three main areas: optimization, simulation and machine learning. The appeal of quantum machine learning lies in its potential to tackle problems ...

The computational demands of today’s AI systems are starting to outpace what classical hardware can deliver. How can we fix this? One possible solution is quantum machine learning (QML). QML ...

When a quantum computer processes data, it must translate it into understandable quantum data. Algorithms that carry out this 'quantum compilation' typically optimize one target at a time. However, a ...

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...

This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms. All methods ...

At the forefront of discovery, where cutting-edge scientific questions are tackled, we often don't have much data. Conversely, successful machine learning (ML) tends to rely on large, high-quality ...

Turbulence is one of the most expensive problems in computing. Simulating the chaotic swirl of air over a wing or the churn ...

CML Unlocks AI’s Full Potential with Enhanced Pattern Recognition, Prediction, and Real-Time Decision-Making for Defense, Autonomous Systems, and Next-Gen Computing BOULDER, Colo.--(BUSINESS ...

Pushing against years of scepticism, an analysis suggests quantum computers may offer real advantages for running machine learning and similar algorithms in the near future ...