Imagine a future where quantum computers supercharge machine learning—training models in seconds, extracting insights from ...

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 ...

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing. An experimental study shows that already ...

Finding high-performing candidates in the vast search space of bosonic qubit encodings represents a complex optimization task, which the researchers address with reinforcement learning, an advanced ...

About a year and a half ago, quantum control startup Quantum Machines and Nvidia announced a deep partnership that would bring together Nvidia’s DGX Quantum computing platform and Quantum Machine’s ...

Classical machine learning (ML) is a powerful subset of artificial intelligence. Machine learning has advanced from simple pattern recognition in the 1960s to today's advanced use of massive datasets ...

In this architecture, the training process adopts a joint optimization mechanism based on classical cross-entropy loss. WiMi treats the measurement probability distribution output by the quantum ...

Understanding the quantum universe is not an easy thing. Intuitive notions of space and time break down in the tiny realm of subatomic physics, allowing for behavior that seems, to our macro ...

The quantum tangent kernel method is a mathematical approach used to understand how fast and how well quantum neural networks can learn. A quantum neural network is a machine learning model that runs ...

Quantum computing made significant strides in 2024, but it’s yet to demonstrate a practical advantage over classical digital computers, according to a recent trends report released by Forrester ...