Topic: theoretical advances

The conference connects theoretical advances in quantum computing with the capabilities and constraints of real hardware. Academic and industrial researchers will discuss applied theory and pathways toward useful quantum-computing applications. The planned format combines keynote and industry talks with contributed research presentations and posters, creating opportunities to compare practical challenges with new algorithmic and theoretical approaches.

Saavik Ford gives a public lecture in the Simons Foundation's 2026 Black Holes series, which examines black holes from stellar remnants to supermassive galactic objects through observational breakthroughs and theoretical advances.

There has been a surge of publications on using machine learning (ML) on experimental data from physical systems: social, biological, statistical, and quantum. However, can these methods discover fundamentally new physics? It can be that their biggest impact is in better data preprocessing, while inferring new physics is unrealistic without specifically adapting the learning machine to find what we are looking for — that is, without the “intuition” — and hence without having a good a priori guess about what we will find. Is machine learning a useful tool for physics discovery? Which minimal