PIRSA:18100093

Video URL

https://pirsa.org/18100093

Simulating quantum annealing via projective quantum Monte Carlo algorithms

Inack, E. (2018). Simulating quantum annealing via projective quantum Monte Carlo algorithms. Perimeter Institute for Theoretical Physics. https://pirsa.org/18100093

Inack, Estelle. Simulating quantum annealing via projective quantum Monte Carlo algorithms. Perimeter Institute for Theoretical Physics, Oct. 26, 2018, https://pirsa.org/18100093 

          @misc{ scivideos_PIRSA:18100093,
            doi = {10.48660/18100093},
            url = {https://pirsa.org/18100093},
            author = {Inack, Estelle},
            keywords = {Quantum Matter},
            language = {en},
            title = {Simulating quantum annealing via projective quantum Monte Carlo algorithms},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2018},
            month = {oct},
            note = {PIRSA:18100093 see, \url{https://scivideos.org/index.php/pirsa/18100093}}
          }

Estelle Maeva Inack Perimeter Institute for Theoretical Physics

Talk numberPIRSA:18100093
DOI10.48660/18100093
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

We implement projective quantum Monte Carlo (PQMC) methods to simulate quantum annealing on classical computers. We show that in the regime where the systematic errors are well controlled, PQMC algorithms are capable of simulating the imaginary-time dynamics of the Schroedinger equation both on continuous space models and discrete basis systems. We also demonstrate that the tunneling time of the PQMC method is quadratically faster than the one of incoherent quantum annealing. It shows remarkable stability when applied to frustrated systems compared to finite-temperature path integral Monte Carlo algorithm, the method mostly chosen to do comparisons with quantum annealers. However, a major drawback of the PQMC method comes from the finite number of random walkers needed
to implement the simulations. It grows exponentially with the system size when no or poor guiding wave-functions are utilized. Nevertheless, we demonstrate that when good enough guiding wave-functions are used – in our case we choose artificial neural networks – the computational complexity seems to go from exponential to polynomial in the system size. We advocate for a search of more efficient guiding wave functions since they could determine when PQMC simulations are feasible on classical computers, a question closely related to a provable need or speed-up of a quantum computer.

References:
- E. M. Inack and S. Pilati, Phys. Rev. E 92, 053304 (2015)
- E. M. Inack, G. Giudici, T. Parolini, G. Santoro and S. Pilati, Phys. Rev. A 97, 032307 (2018)
- E. M. Inack, G. Santoro, L. Dell’Anna, and S. Pilati, arXiv:1809.03562v1