In this paper, we eliminate the classical outer learning loop of the quantum approximate
optimization algorithm (QAOA) and present a strategy to find good parameters for QAOA …

In the study of quantum limits to parameter estimation, the high dimensionality of the density
operator and that of the unknown parameters have long been two of the most difficult …

One proposal to utilize near-term quantum computers for machine learning are
Parameterized Quantum Circuits (PQCs). There, input is encoded in a quantum state …

We present a quasi-polynomial time classical algorithm that estimates the partition function
of quantum many-body systems at temperatures above the thermal phase transition point. It …

Quantum simulators are widely seen as one of the most promising near-term applications of
quantum technologies. However, it remains unclear to what extent a noisy device can output …

The decision problem version of estimating the Shannon entropy is the Entropy Difference
problem (ED): given descriptions of two circuits, determine which circuit produces more …

The most general description of a quantum many-body system is given by a wave-function
that lives in a Hilbert space with dimension exponential in the number of particles. This …