Hermitian matrix multiplication is one of the most common actions that is performed on quantum matrices, for example, it is used to apply observables onto a given state vector/density matrix. ρ→Hρ Our goal is to create an algorithm to perform the matrix multiplication within the constraints of QuEST [1], a high-performance simulator for quantum circuits. QuEST provides a system-independent platform for implementing and simulating quantum algorithms without the need for access to quantum machines. The current implementation of QuEST supports CUDA, MPI, and OpenMP, which allows programs to run on a wide variety of systems.
Adiabatic quantum computing (AQC) is a computation protocol to solve difficult problems exploiting quantum advantage, directly applicable to optimization problems. In performing the AQC, different configurations of the Hamiltonian path could lead to dramatic differences in the computation efficiency. It is thus crucial to configure the Hamiltonian path to optimize the computation performance of AQC. Here we apply a reinforcement learning approach to configure AQC for integer programming, where we find the learning process automatically converges to a quantum algorithm that exhibits scaling advantage over the trivial AQC using a linear Hamiltonian path. This reinforcement-learning-based approach for quantum adiabatic algorithm design for integer programming can well be adapted to the quantum resources in different quantum computation devices, due to its built-in flexibility.