English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The utility of effective model spaces in quantum simulations ofnon-relativistic quantum many-body systems is explored in the context of theLipkin-Meshkov-Glick model of interacting fermions. We introduce an iterativehybrid-classical-quantum algorithm, Hamiltonian learning variational quantumeigensolver (HL-VQE), that simultaneously optimizes an effective Hamiltonian,thereby rearranging entanglement into the effective model space, and theassociated ground-state wavefunction. HL-VQE is found to provide an exponentialimprovement in Lipkin-Meshkov-Glick model calculations, compared to a naivetruncation without Hamiltonian learning, throughout a significant fraction ofthe Hilbert space. Quantum simulations are performed to demonstrate the HL-VQEalgorithm, using an efficient mapping where the number of qubits scales withthe $\log$ of the size of the effective model space, rather than the particlenumber, allowing for the description of large systems with small quantumcircuits. Implementations on IBM's QExperience quantum computers and simulatorsfor 1- and 2-qubit effective model spaces are shown to provide accurate andprecise results, reproducing classical predictions. This work constitutes astep in the development of entanglement-driven quantum algorithms for thedescription of nuclear systems, that leverages the potential of noisyintermediate-scale quantum (NISQ) devices.

physical review. c

Quantum Simulations in Effective Model Spaces (I): Hamiltonian  Learning-VQE using Digital Quantum Computers and Application to the  Lipkin-Meshkov-Glick Model