The path integral Monte Carlo simulated quantum annealing algorithm is applied to the optimization of a large hard instance of the random satisfiability problem (N=10 000). The dynamical behavior of the quantum and the classical annealing are compared, showing important qualitative differences in the way of exploring the complex energy landscape of the combinatorial optimization problem. At variance with the results obtained for the Ising spin glass and for the traveling salesman problem, in the present case the linear-schedule quantum annealing performance is definitely worse than classical annealing. Nevertheless, a quantum cooling protocol based on field-cycling and able to outperform standard classical simulated annealing over short time scales is introduced.

Optimization by quantum annealing: Lessons from hard satisfiability problems / Battaglia, D. A.; Santoro, G. E.; Tosatti, E.. - In: PHYSICAL REVIEW E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS. - ISSN 1539-3755. - 71:6(2005), pp. 066707.066707-1-066707.066707-10. [10.1103/PhysRevE.71.066707]

Optimization by quantum annealing: Lessons from hard satisfiability problems

Santoro, G. E.;Tosatti, E.
2005-01-01

Abstract

The path integral Monte Carlo simulated quantum annealing algorithm is applied to the optimization of a large hard instance of the random satisfiability problem (N=10 000). The dynamical behavior of the quantum and the classical annealing are compared, showing important qualitative differences in the way of exploring the complex energy landscape of the combinatorial optimization problem. At variance with the results obtained for the Ising spin glass and for the traveling salesman problem, in the present case the linear-schedule quantum annealing performance is definitely worse than classical annealing. Nevertheless, a quantum cooling protocol based on field-cycling and able to outperform standard classical simulated annealing over short time scales is introduced.
2005
71
6
066707-1
066707-10
066707
https://arxiv.org/pdf/cond-mat/0502468.pdf
Battaglia, D. A.; Santoro, G. E.; Tosatti, E.
File in questo prodotto:
File Dimensione Formato  
Battaglia_PRE05.pdf

non disponibili

Licenza: Non specificato
Dimensione 283.62 kB
Formato Adobe PDF
283.62 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

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/20.500.11767/16539
Citazioni
  • ???jsp.display-item.citation.pmc??? 8
  • Scopus 87
  • ???jsp.display-item.citation.isi??? 78
social impact