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 study the relationship between the Quantum Approximate Optimization Algorithm
(QAOA) and the underlying symmetries of the objective function to be optimized. Our …

Entanglement is one of the physical properties of quantum systems responsible for the
computational hardness of simulating quantum systems. But while the runtime of specific …

Monte Carlo simulations are useful tools for modeling quantum systems, but in some cases
they suffer from a sign problem, leading to an exponential slow down in their convergence to …

Quantum optimization, both for classical and quantum functions, is one of the most well-
studied applications of quantum computing, but recent trends have relied on hybrid methods …

Stoquastic Hamiltonians play a role in the computational complexity of the local Hamiltonian
problem as well as the study of classical simulability. In particular, stoquastic Hamiltonians …

Quantum information science offers a remarkable promise: by thinking practically about how
quantum systems can be put to work to solve computational and information processing …