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 present a direct comparison between QAOA (Quantum Alternating Operator Ansatz), and
QA (Quantum Annealing) on 127 qubit problem instances. QAOA with p= 1, 2 rounds is …

Quantum annealing is a heuristic optimization algorithm that exploits quantum evolution to
approximately find lowest energy states. Quantum annealers have scaled up in recent years …

We revisit well-known protocols in quantum metrology using collective spins and propose a
unifying picture for optimal state preparation based on a semiclassical description in phase …

Finding the exact counterdiabatic potential is, in principle, particularly demanding. Following
recent progress about variational strategies to approximate the counterdiabatic operator, in …

Quantum annealing has the potential to provide a speedup over classical algorithms in
solving optimization problems. Just as for any other quantum device, suppressing …

The study of optimal control of quantum annealing by modulating the pace of evolution and
by introducing a counterdiabatic potential has gained significant attention in recent times. In …

Physical implementations of quantum annealing unavoidably operate at finite temperatures.
We point to a fundamental limitation of fixed finite temperature quantum annealers that …

We show the emergence of Floquet time crystal (FTC) phases in the Floquet dynamics of
periodically driven p-spin models, which describe a collection of spin-1/2 particles with all-to …

We solve the mean-field-like p-spin Ising model under a spatiotemporal inhomogeneous
transverse field to study the effects of inhomogeneity on the performance of quantum …