Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to
solve combinatorial optimization problems. In recent years, advances in quantum …

Optimization, sampling and machine learning are topics of broad interest that have inspired
significant developments and new approaches in quantum computing. One such approach …

We propose digitized-counterdiabatic quantum optimization (DCQO) to achieve polynomial
enhancement over adiabatic quantum optimization for the general Ising spin-glass model …

One of the major application areas of interest for both near-term and fault-tolerant quantum
computers is the optimization of classical objective functions. In this work, we develop …

The adiabatic theorem provides sufficient conditions for the time needed to prepare a target
ground state. While it is possible to prepare a target state much faster with more general …

We present counterdiabatic reverse annealing, a quantum annealing protocol that extends
the range of application of reverse annealing to the previously inaccessible short-time …

We study adiabatic reverse annealing (ARA) in an open system. In the closed system
(unitary) setting, this annealing protocol allows avoidance of first-order quantum phase …

Introducing a nonstoquastic catalyst is a promising avenue to improve quantum annealing
with the transverse field. In the present paper, we propose a nonstoquastic catalyst for …

Quantum annealing (QA) is a promising method for solving combinational optimization
problems and performing quantum chemical calculations. The main sources of errors in QA …

We study the performance of quantum annealing for two sets of problems, namely, 2-
satisfiability (2-SAT) problems represented by Ising-type Hamiltonians and nonstoquastic …