Combining Tensor Networks with Monte Carlo: Applications to the MERA Andy Ferris 1,2 Guifre Vidal 1,3 1 University of Queensland, Australia 2 Université.

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Combining Tensor Networks with Monte Carlo: Applications to the MERA Andy Ferris 1,2 Guifre Vidal 1,3 1 University of Queensland, Australia 2 Université de Sherbrooke, Québec 3 Perimeter Institute for Theoretical Physics, Ontario

Motivation: Make tensor networks faster Calculations should be efficient in memory and computation (polynomial in χ, etc) However total cost might still be HUGE (e.g. 2D) χ Parameters: d L vs. Poly(χ,d,L)

Monte Carlo makes stuff faster Monte Carlo: Random sampling of a sum – Tensor contraction is just a sum Variational MC: optimizing parameters Statistical noise! – Reduced by importance sampling over some positive probability distribution P(s)

Monte Carlo with Tensor networks

MPS: Sandvik and Vidal, Phys. Rev. Lett. 99, (2007). CPS: Schuch, Wolf, Verstraete, and Cirac, Phys. Rev. Lett. 100, (2008). Neuscamman, Umrigar, Garnet Chan, arXiv: (2011), etc… PEPS: Wang, Pižorn, Verstraete, Phys. Rev. B 83, (2011). (no variational) …

Monte Carlo with Tensor networks MPS: Sandvik and Vidal, Phys. Rev. Lett. 99, (2007). CPS: Schuch, Wolf, Verstraete, and Cirac, Phys. Rev. Lett. 100, (2008). Neuscamman, Umrigar, Garnet Chan, arXiv: (2011), etc… PEPS: Wang, Pižorn, Verstraete, Phys. Rev. B 83, (2011). (no variational) … Unitary TN: Ferris and Vidal, Phys. Rev. B 85, (2012). 1D MERA: Ferris and Vidal, Phys. Rev. B, 85, (2012).

Perfect vs. Markov chain sampling Perfect sampling: Generating s from P(s) Often harder than calculating P(s) from s! Use Markov chain update e.g. Metropolis algorithm: – Get random s’ – Accept s’ with probability min[P(s’) / P(s), 1] Autocorrelation: subsequent samples are “close”

Markov chain sampling of an MPS Choose P(s) = | | 2 where |s> = |s 1 >|s 2 > … Cost is O(χ 2 L) 2 <s1|<s1|<s2|<s2|<s3|<s3|<s4|<s4|<s5|<s5|<s6|<s6|’ Accept with probability min[P(s’) / P(s), 1] A. Sandvik & G. Vidal, PRL 99, (2007)

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) … Cost is now O(χ 3 L) !

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) … if = Unitary/isometric tensors:

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) …

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) …

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) …

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) …

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) …

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) … Can sample in any basis…

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) …

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) … Total cost now O(χ 2 L)

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) … Total cost now O(χ 2 L)

Perfect sampling of a unitary MPS Note that P(s 1,s 2,s 3,…) = P(s 1 ) P(s 2 |s 1 ) P(s 3 |s 1,s 2 ) … Total cost now O(χ 2 L)

Comparison: critical transverse Ising model Perfect samplingMarkov chain sampling Ferris & Vidal, PRB 85, (2012)

50 sites 250 sites Perfect sampling Markov chain MC Critical transverse Ising model Ferris & Vidal, PRB 85, (2012)

Multi-scale entanglement renormalization ansatz (MERA) Numerical implementation of real-space renormalization group – remove short-range entanglement – course-grain the lattice

Sampling the MERA Cost is O(χ 9 )

Sampling the MERA Cost is O(χ 5 )

Perfect sampling with MERA

Perfect Sampling with MERA Cost reduced from O(χ 9 ) to O(χ 5 ) Ferris & Vidal, PRB 85, (2012)

Extracting expectation values Transverse Ising model Worst case = - 2 Monte Carlo MERA

Optimizing tensors Environment of a tensor can be estimated Statistical noise  SVD updates unstable

Optimizing isometric tensors Each tensor must be isometric: Therefore can’t move in arbitrary direction – Derivative must be projected to the tangent space of isometric manifold: – Then we must insure the tensor remains isometric

Results: Finding ground states Transverse Ising model Samples per update Exact contraction result Ferris & Vidal, PRB 85, (2012)

Accuracy vs. number of samples Transverse Ising Model Samples per update Ferris & Vidal, PRB 85, (2012)

Discussion of performance Sampling the MERA is working well. Optimization with noise is challenging. New optimization techniques would be great – “Stochastic reconfiguration” is essentially the (imaginary) time-dependent variational principle (Haegeman et al.) used by VMC community. Relative performance of Monte Carlo in 2D systems should be more favorable.

Two-dimensional MERA 2D MERA contractions significantly more expensive than 1D E.g. O(χ 16 ) for exact contraction vs O(χ 8 ) per sample – Glen has new techniques… Power roughly halves – Removed half the TN diagram

Conclusions & Outlook Can effectively sample the MERA (and other unitary TN’s) Optimization is challenging, but possible! Monte Carlo should be more effective in 2D where there are more degrees of freedom to sample PRB 85, & (2012)