combining tensor networks with monte carlo: applications to the mera
DESCRIPTION
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. χ. - PowerPoint PPT PresentationTRANSCRIPT
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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, Australia2 Université de Sherbrooke, Québec
3 Perimeter Institute for Theoretical Physics, Ontario
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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: dL vs. Poly(χ,d,L)
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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)
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Monte Carlo with Tensor networks
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Monte Carlo with Tensor networks
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Monte Carlo with Tensor networksMPS: Sandvik and Vidal, Phys. Rev. Lett. 99, 220602 (2007).CPS: Schuch, Wolf, Verstraete, and Cirac, Phys. Rev. Lett. 100, 040501 (2008). Neuscamman, Umrigar, Garnet Chan, arXiv:1108.0900 (2011), etc…PEPS: Wang, Pižorn, Verstraete, Phys. Rev. B 83, 134421 (2011). (no variational)…
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Monte Carlo with Tensor networksMPS: Sandvik and Vidal, Phys. Rev. Lett. 99, 220602 (2007).CPS: Schuch, Wolf, Verstraete, and Cirac, Phys. Rev. Lett. 100, 040501 (2008). Neuscamman, Umrigar, Garnet Chan, arXiv:1108.0900 (2011), etc…PEPS: Wang, Pižorn, Verstraete, Phys. Rev. B 83, 134421 (2011). (no variational)…Unitary TN: Ferris and Vidal, Phys. Rev. B 85, 165146 (2012).1D MERA: Ferris and Vidal, Phys. Rev. B, 85, 165147 (2012).
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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”
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Markov chain sampling of an MPS
Choose P(s) = |<s|Ψ>|2 where |s> = |s1>|s2> …
Cost is O(χ2L)
2
<s1| <s2| <s3| <s4| <s5| <s6|’
Accept with probability min[P(s’) / P(s), 1]
A. Sandvik & G. Vidal, PRL 99, 220602 (2007)
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
Cost is now O(χ3L) !
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
if =
Unitary/isometric tensors:
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
Can sample in any basis…
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
Total cost now O(χ2L)
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
Total cost now O(χ2L)
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Perfect sampling of a unitary MPS
Note that P(s1,s2,s3,…) = P(s1) P(s2|s1) P(s3|s1,s2) …
Total cost now O(χ2L)
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Comparison: critical transverse Ising model
Perfect sampling Markov chain sampling
Ferris & Vidal, PRB 85, 165146 (2012)
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50 sites
250 sites
Perfect sampling
Markov chain MC
Critical transverse Ising model
Ferris & Vidal, PRB 85, 165146 (2012)
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Multi-scale entanglement renormalization ansatz (MERA)
• Numerical implementation of real-space renormalization group– remove short-range entanglement– course-grain the lattice
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Sampling the MERA
Cost is O(χ9)
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Sampling the MERA
Cost is O(χ5)
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Perfect sampling with MERA
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Perfect Sampling with MERA
Cost reduced from O(χ9) to O(χ5) Ferris & Vidal, PRB 85, 165147 (2012)
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Extracting expectation valuesTransverse Ising model
Worst case = <H2> - <H>2
Monte Carlo MERA
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Optimizing tensorsEnvironment of a tensor can be estimated
Statistical noise SVD updates unstable
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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
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Results: Finding ground statesTransverse Ising model
Samplesper update
1
2
4
8
Exactcontraction
result
Ferris & Vidal, PRB 85, 165147 (2012)
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Accuracy vs. number of samplesTransverse Ising Model
Samplesper update
1
4
16
64
Ferris & Vidal, PRB 85, 165147 (2012)
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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.
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Two-dimensional MERA
• 2D MERA contractions significantly more expensive than 1D
• E.g. O(χ16) for exact contraction vs O(χ8) per sample– Glen has new techniques…
• Power roughly halves– Removed half the TN diagram
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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, 165146 & 165147 (2012)