Embedding p-body interacting models onto the 2-body networks implemented on commercial quantum annealers is a relevant issue. For highly interacting models, requiring a number of ancilla qubits, that can be sizable and make unfeasible (if not impossible) to simulate such systems. In this manuscript, we propose an alternative to minor embedding, developing a new approximate procedure based on genetic algorithms, allowing to decouple the p-body in terms of 2-body interactions. A set of preliminary numerical experiments demonstrates the feasibility of our approach for the ferromagnetic p-spin model and paves the way towards the application of evolutionary strategies to more complex quantum models.

An evolutionary strategy for finding effective quantum 2-body Hamiltonians of p-body interacting systems / Acampora, G.; Cataudella, V.; Hegde, P. R.; Lucignano, P.; Passarelli, G.; Vitiello, A.. - In: QUANTUM MACHINE INTELLIGENCE. - ISSN 2524-4906. - 1:3-4(2019), pp. 113-122. [10.1007/s42484-019-00011-8]

An evolutionary strategy for finding effective quantum 2-body Hamiltonians of p-body interacting systems

Acampora G.;Cataudella V.;Hegde P. R.;Lucignano P.;Passarelli G.;Vitiello A.
2019

Abstract

Embedding p-body interacting models onto the 2-body networks implemented on commercial quantum annealers is a relevant issue. For highly interacting models, requiring a number of ancilla qubits, that can be sizable and make unfeasible (if not impossible) to simulate such systems. In this manuscript, we propose an alternative to minor embedding, developing a new approximate procedure based on genetic algorithms, allowing to decouple the p-body in terms of 2-body interactions. A set of preliminary numerical experiments demonstrates the feasibility of our approach for the ferromagnetic p-spin model and paves the way towards the application of evolutionary strategies to more complex quantum models.
2019
An evolutionary strategy for finding effective quantum 2-body Hamiltonians of p-body interacting systems / Acampora, G.; Cataudella, V.; Hegde, P. R.; Lucignano, P.; Passarelli, G.; Vitiello, A.. - In: QUANTUM MACHINE INTELLIGENCE. - ISSN 2524-4906. - 1:3-4(2019), pp. 113-122. [10.1007/s42484-019-00011-8]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/938191
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? ND
social impact