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

Recent successes in producing intermediate-scale quantum devices have focused interest
on establishing whether near-term devices could outperform classical computers for …

The second quantum revolution has been built on a foundation of fundamental research at
the intersection of physics and information science, giving rise to the discipline we now call …

The thermal or equilibrium ensemble is one of the most ubiquitous states of matter. For
models comprised of many locally interacting quantum particles, it describes a wide range of …

One of the most fundamental problems in quantum many-body physics is the
characterization of correlations among thermal states. Of particular relevance is the thermal …

We present a quantum algorithm that has rigorous runtime guarantees for several families of
binary optimization problems, including Quadratic Unconstrained Binary Optimization …

We study the problem of learning the Hamiltonian of a quantum many-body system given
samples from its Gibbs (thermal) state. The classical analog of this problem, known as …

The computational complexity of simulating quantum many-body systems generally scales
exponentially with the number of particles. This enormous computational cost prohibits first …

By exploiting the link between timeindependent Hamiltonians and thermalisation, heuristic
predictions on the performance of continuous-time quantum walks for MAX-CUT are made …

We consider the task of spectral estimation of local quantum Hamiltonians. The spectral
estimation is performed by estimating the oscillation frequencies or decay rates of signals …