Microsoft's 2029 quantum supercomputer ambitions may have hit a roadblock, as critics claim the company's 2025 quantum ...

Researchers from the University of Sydney, working with IBM, have identified and quantified important factors limiting the ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Quantum computing is not just a faster processor; it is a model of computation based on quantum mechanics using qubits and superposition to process complex data in ways classical computers cannot.

Quantum computing isn't science fiction anymore—it's powering breakthroughs in drug discovery, cryptography, and AI optimization. As enterprises race to build quantum-ready teams, developers fluent in ...

Implementation and example training scripts of various flavours of graph neural network in TensorFlow 2.0. Much of it is based on the code in the tf-gnn-samples repo. The code is maintained by the ...

Will computers ever match or surpass human-level intelligence — and, if so, how? When the Association for the Advancement of Artificial Intelligence (AAAI), based in Washington DC, asked its members ...

Classiq Technologies Ltd., a maker of quantum software development tools, today announced that it has raised “tens of millions of dollars” in new funding. The capital was provided as an extension to a ...

Integrated quantum computing company Quantinuum Ltd. today unveiled new open-source software tools designed to accelerate software development for quantum computing with a more intuitive programming ...

Image classification is an important task in various machine learning applications. In recent years, a number of classification methods based on quantum machine learning and different quantum image ...

Human inventions, namely engineered systems, have relied on fundamental discoveries in physics and mathematics, e.g., Maxwell’s equations, Quantum mechanics, Information theory, etc., thereby applying ...

A distinguishing feature of the neural network models used in Physics and Chemistry is that they must obey basic underlying symmetries, such as symmetry to translations, rotations, and the exchange of ...