One of the best places where a quantum computer can be used for advantage over a classical approach is with optimization problems. These are problems where one seeks to find the lowest energy value in a mathematical equation expressed as a QUBO (Quadratic Unconstrained Binary Optimization) and they are applicable in a great many areas including finance, logistics, drug discovery, cybersecurity, machine learning, and many others. To this end, Quantum Computing Inc. (QCI) has announced commercial availability of their Qatalyst software (formerly called Mukai) to solve these types of problems efficiently on a variety of hardware platforms, both classical and quantum.

One advantage to the Qatalyst software is that many data scientists are quite familiar with using optimization problems with classical computing solvers and they should be able to quickly learn to use the software and compare the solutions of these problems on several different computing platforms, both classical and quantum, including quantum machines from Rigetti, D-Wave, and IonQ. So the big hurdle often faced by application developers with other QC approaches of learning completely new software algorithms, languages, and development kits is minimized. QCI has indicated that they software has solved large optimization problems that contain up to 110,000 variables with 8,000 constraints. The Los Alamos National Laboratory (LANL) tested the software and recently posted a paper on arXiv comparing the performance of the Qatalyst software (referred to as Mukai in the paper) with another QUBO solver available from D-Wave called Qbsolv and found that Qatalyst had better performance.

Additional information about QCI’s announcement can be found in their announcement press release here. You can also view a previous articles we published about the Mukai software here and here. In addition, QCI has created a QikStart initiative for accelerating quantum use cases that we reported on here.

February 17, 2021