Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics.
In this Letter, we propose the first algorithm to achieve the Heisenberg limit for learning an …

We demonstrate a new approach for fast preparation, manipulation, and collective readout
of an atomic Rydberg-state qubit. By making use of Rydberg blockade inside a small atomic …

A large ongoing research effort focuses on obtaining a quantum advantage in the solution of
combinatorial optimization problems on near-term quantum devices. A particularly promising …

We study the correlation clustering problem using the quantum approximate optimization
algorithm (QAOA) and qudits, which constitute a natural platform for such non-binary …

Quantum many-body systems are characterized by patterns of correlations defining highly
nontrivial manifolds when interpreted as data structures. Physical properties of phases and …

Quantum simulation offers the possibility to study quantum mechanical problems which are
untraceable on classical computers. This thesis introduces a novel platform for quantum …

Quantum mechanics is one of the most successful and striking theories in physics. It predicts
atomic particles can have exotic properties, such as quantum entanglement, that any …

Quantum Ising model on a triangular lattice hosts a finite temperature Berezinskii-Kosterlitz-
Thouless (BKT) phase with emergent U (1) symmetry, and it will transit into an up-up-down …