We’re working on bringing the power of quantum computing – and quantum machine learning - to particle physics, an area that is the first to be impacted by quantum computers such as ours, that cannot be simulated.
Read our blogpost: https://lnkd.in/eCi3fHpy
Particle physics, or High Energy Physics (#HEP), is incredibly computationally demanding, with annual data generated by accelerators in excess of exabytes (a billion gigabytes), tens of millions of lines of code written to support the experiments, and incredibly demanding hardware requirements. Quantum computing promises to solve many problems the HEP community faces, and we are working hard to help them realize that as our H2 Series quantum computer is the first that literally cannot be simulated.
In a recent paper, our team partnered with DESY, the Leiden Institute of Advanced Computer Science (LIACS), and Northeastern University to explore using a generative quantum machine learning model, called a “quantum Boltzmann machine” to untangle data from CERN’s LHC. Notably, Boltzmann machines were a key part of the 2024 Nobel Prize in Physics.
Read the scientific paper here: https://lnkd.in/eQwkCtnS