Minimizing couplings in renormalization by preserving short-range mutual information

The connections between renormalization in statistical mechanics and information theory are intuitively evident, but a satisfactory theoretical treatment remains elusive. Recently, Koch-Janusz and Ringel proposed selecting a real-space renormalization map for classical lattice systems by minimizing the loss of long-range mutual information [Nat. Phys. 14, 578 (2018)]. The success of this technique has been related in part to the minimization of long-range couplings in the renormalized Hamiltonian [Lenggenhager et al., Phys. Rev. X 10, 011037 (2020)]. We show that to minimize these couplings the renormalization map should, somewhat counterintuitively, instead be chosen to minimize the loss of short-range mutual information between a block and its boundary. Moreover, the previous minimization is a relaxation of this approach, which indicates that the aims of preserving long-range physics and eliminating short-range couplings are related in a nontrivial way.

[1]  O'Connor,et al.  Field theory entropy, the H theorem, and the renormalization group. , 1995, Physical review. D, Particles and fields.

[2]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[3]  T. Osborne,et al.  Renormalisation as an inference problem , 2013, 1310.3188.

[4]  J. Sethna,et al.  Parameter Space Compression Underlies Emergent Theories and Predictive Models , 2013, Science.

[5]  Tobias J. Osborne,et al.  Information geometric approach to the renormalisation group , 2012, 1206.7004.

[6]  Michael Levin,et al.  Tensor renormalization group approach to two-dimensional classical lattice models. , 2006, Physical review letters.

[7]  Zohar Ringel,et al.  Optimal Renormalization Group Transformation from Information Theory , 2018, Physical Review X.

[8]  G. Evenbly,et al.  Tensor Network Renormalization. , 2014, Physical review letters.

[9]  Peter E. Latham,et al.  Mutual Information , 2006 .

[10]  L. Kadanoff Scaling laws for Ising models near T(c) , 1966 .

[11]  A. Davis Markov Chains as Random Input Automata , 1961 .

[12]  Zohar Ringel,et al.  Mutual information, neural networks and the renormalization group , 2017, ArXiv.

[13]  J. M. Hammersley,et al.  Markov fields on finite graphs and lattices , 1971 .

[14]  T. Nishino,et al.  Corner Transfer Matrix Renormalization Group Method , 1995, cond-mat/9507087.

[15]  K. Wilson The renormalization group: Critical phenomena and the Kondo problem , 1975 .

[16]  R. Swendsen Monte Carlo renormalization-group studies of the d=2 Ising model , 1979 .

[17]  S. M. Apenko Information theory and renormalization group flows , 2009, 0910.2097.

[18]  Cédric Bény,et al.  The renormalization group via statistical inference , 2014, 1402.4949.

[19]  Andreas Winter,et al.  Squashed Entanglement, k-Extendibility, Quantum Markov Chains, and Recovery Maps , 2018 .