Aqarios' platform Luna v1.0 marks a major milestone in quantum optimization. This release significantly improves usability, ...

Optimization problems can be tricky, but they make the world work better. These kinds of questions, which strive for the best way of doing something, are absolutely everywhere. Your phone’s GPS ...

A paper describing their work, "Computing High-degree Polynomial Gradients in Memory," appears in the journal Nature ...

Researchers have developed a new, data-driven machine-learning technique that speeds up software programs used to solve complex optimization problems that can have millions of potential solutions.

The researchers write, “Instead of formally defining the optimization problem and deriving the update step with a programmed solver, we describe the optimization problem in natural language ...

It’s been difficult to find important questions that quantum computers can answer faster than classical machines, but a new algorithm appears to do so for some critical optimization tasks.

More information: Xiaomeng Sui et al, Non-convex optimization for inverse problem solving in computer-generated holography, Light: Science & Applications (2024). DOI: 10.1038/s41377-024-01446-w.

We were visiting a hedge fund some years back when we had our first taste of the problem with mean-variance optimization—the tool advisors use to balance risk and reward in client portfolios.

According to Battacharya, much of the currently proposed, state-of-the-art hardware for solving these kinds of issues are limited to second-order problems. The main benefit of their hardware, he noted ...