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 ...

Learning how a physical system behaves usually means repeating measurements and using statistics to uncover patterns. That ...

Large language models are powering a new generation of AI agents that could transform computational chemistry from a ...

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 ...

Integrating quantum computing into AI doesn’t require rebuilding neural networks from scratch. Instead, I’ve found the most effective approach is to introduce a small quantum block—essentially a ...

A team of researchers has shown that even small-scale quantum computers can enhance machine learning performance, using a novel photonic quantum circuit. Their findings suggest that today s quantum ...

Superconductors sit at the heart of some of the most ambitious technologies on the horizon, from lossless power grids to ...

There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...

Artificial intelligence has grown so large and power hungry that even cutting edge data centers strain to keep up, yet a technique borrowed from quantum physics is starting to carve these systems down ...

Classical computations rely on binary bits, which can be in either of the two states, 0 or 1. In contrast, quantum computing is based on qubits, which can be 0, 1, or a superposition or entanglement ...