Quantum computing for HEP

Quantum optimization and machine learning

The innate parallelism of quantum computers is very powerful for constrained optimization problems — problems where you can count the possible solutions, but the number of them grows extremely rapidly with the size of the problem.

Machine learning and computation frameworks

Research efforts to build quantum computers are poised to help solve a number of massive data processing and analysis problems due to become an issue in the middle of the next decade as the Deep Underground Neutrino Experiment and the High-Luminosity LHC begin to operate.

Quantum simulations

Scientists are working on algorithms to simulate the interactions of fermions (particles of matter) and bosons (force-carrying particles) using qubits of quantum computers.

High-energy physics applications

The Fermilab computing community is now pursuing a program to leverage quantum computing to solve problems in data analysis and theoretical calculations.

Foundational quantum science

Theoretical scientists are developing mathematical models that simulate quantum computing systems — and then mapping quantum physics systems onto those models.