Speaker
Description
Quantum hardware has made striking progress, and I will open with a brief theorist’s snapshot of where today’s devices stand: what current qubit platforms can do reliably and what the roadmaps of leading providers suggest for the next few years. The central theme of the talk, however, is the field’s biggest open challenge: finding compelling uses—problems where quantum devices can produce real scientific value with realistic resources. To organize that search, I will highlight a small set of directions that look most promising. These include simulating quantum systems in real time (with an eye toward lattice models and gauge theories), computing electronic structure for molecules and materials, improving measurement and sensing through quantum techniques, and a few carefully chosen machine-learning tasks where quantum methods might help with sampling or representation. A key message will be that progress depends not only on new algorithms, but also on “translation”: turning ideas into complete workflows that fit the constraints of actual devices, use data in practical ways, and can be tested fairly against the best classical approaches. I will close by connecting these themes to high-energy physics, outlining where quantum computing could complement existing tools and, just as importantly, how high-energy physics can help steer the field by providing hard benchmarks and realistic problem settings.