Johannes Knörzerjohannes-knorzer-53793

Ground state preparation is a key area where quantum computers are expected to prove advantageous. Double-bracket quantum algorithms (DBQAs) have been recently proposed to diagonalize Hamiltonians and in this work we show how to use them to prepare ground states. We propose to improve an initial state preparation by adding a few steps of DBQAs. The interfaced method systematically achieves a better fidelity while significantly reducing the computational cost of the procedure. For a Heisenberg model, we compile our algorithm using CZ and single-qubit gates into circuits that match capabilities of near-term quantum devices. Moreover, we show that DBQAs can benefit from the experimental availability of increasing circuit depths. Whenever an approximate ground state can be prepared without exhausting the available circuit depth, then DBQAs can be enlisted to algorithmically seek a higher fidelity preparation.

Multipartite entanglement is an essential resource for quantum information theory and technologies, but its quantification has been a persistent challenge. Recently, Concentratable Entanglement (CE) has been introduced as a promising candidate for a multipartite entanglement measure, which can be efficiently estimated across two state copies. In this work, we introduce Generalized Concentratable Entanglement (GCE) measures, highlight a natural correspondence to quantum Tsallis entropies, and conjecture a new entropic inequality that may be of independent interest. We show how to efficiently measure the GCE in a quantum computer, using parallelized permutation tests across a prime number of state copies. We exemplify the practicality of such computation for probabilistic entanglement concentration into W states with three state copies. Moreover, we show that an increased number of state copies provides an improved error bound on this family of multipartite entanglement measures in the presence of imperfections. Finally, we prove that GCE is still a well-defined entanglement monotone as its value, on average, does not increase under local operations and classical communication (LOCC).