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arXiv:2407.13428 · Journal of Mathematical Physics

The structure of emulations in classical spin models: Modularity and universality

Tobias Reinhart, Benjamin Engel, Gemma De les Coves

Published Jul 1, 2026• 1 citation
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Abstract

The theory of spin models intersects with condensed matter physics, complex systems, graph theory, combinatorial optimization, computational complexity and neural networks. Many ensuing applications rely on transforming complicated spin models to simpler ones. What is the structure of such transformations? Here, we provide a framework to study and construct emulations between spin models. A spin model is a set of spin systems, and emulations are efficiently computable families of simulations with arbitrary energy cut-off, where a source spin system simulates a target system if, below the cut-off, the target Hamiltonian is encoded in the source Hamiltonian. We prove that emulations preserve important properties, as they induce reductions between computational problems such as computing ground states, approximating partition functions and approximate sampling from Boltzmann distributions. Emulations are modular (they can be added, scaled and composed), and allow for universality, i.e., under emulations, certain spin models have maximal reach. We prove that a spin model is universal if and only if it is scalable, closed and functional complete. Because the characterization is constructive, it provides a step-by-step guide to construct emulations. We prove that the 2d Ising model with fields is universal, for which we also provide two new crossing gadgets. Finally, we show that simulations can be computed by linear programs. While some ideas of this work are contained in G. De las Cuevas and T. S. Cubitt, Science 351, 1180 (2016), we provide new definitions and theorems. This framework provides a toolbox for applications involving emulations of spin models.

DOI
10.1063/5.0252369

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The theory of spin models intersects with condensed matter physics, complex systems, graph theory, combinatorial optimization, computational complexity and neural networks. Many ensuing applications rely on transforming complicated spin models to simpler ones. What is the structure of such transformations? Here, we provide a framework to study and construct emulations between spin models. A spin model is a set of spin systems, and emulations are efficiently computable families of simulations with arbitrary energy cut-off, where a source spin system simulates a target system if, below the cut-off, the target Hamiltonian is encoded in the source Hamiltonian. We prove that emulations preserve important properties, as they induce reductions between computational problems such as computing ground states, approximating partition functions and approximate sampling from Boltzmann distributions. Emulations are modular (they can be added, scaled and composed), and allow for universality, i.e., under emulations, certain spin models have maximal reach. We prove that a spin model is universal if and only if it is scalable, closed and functional complete. Because the characterization is constructive, it provides a step-by-step guide to construct emulations. We prove that the 2d Ising model with fields is universal, for which we also provide two new crossing gadgets. Finally, we show that simulations can be computed by linear programs. While some ideas of this work are contained in G. De las Cuevas and T. S. Cubitt, Science 351, 1180 (2016), we provide new definitions and theorems. This framework provides a toolbox for applications involving emulations of spin models.
Spin Models The idea of finding an all-encompassing model to describe the world around us has appealed to generations of scientists. Although we are very far from this ultimate goal, more modest steps can be taken if we focus on particular problems. De las Cuevas and Cubitt used concepts borrowed from computer science to show that all classical spin models (introduced initially to study magnetism) can be solved by tackling a slightly more complex, universal model (see the Perspective by Wehner). Thanks to these findings, it may be possible to physically simulate systems with complex interactions, using the well-understood two-dimensional Ising model with fields. Science , this issue p. [1180][1]; see also p. [1156][2] [1]: /lookup/doi/10.1126/science.aab3326 [2]: /lookup/doi/10.1126/science.aaf0748
We study mappings between distinct classical spin systems that leave the partition function invariant. As recently shown in [Phys. Rev. Lett. 100, 110501 (2008)], the partition function of the 2D square lattice Ising model in the presence of an inhomogeneous magnetic field, can specialize to the partition function of any Ising system on an arbitrary graph. In this sense the 2D Ising model is said to be "complete". However, in order to obtain the above result, the coupling strengths on the 2D lattice must assume complex values, and thus do not allow for a physical interpretation. Here we show how a complete model with real -and, hence, "physical"- couplings can be obtained if the 3D Ising model is considered. We furthermore show how to map general q-state systems with possibly many-body interactions to the 2D Ising model with complex parameters, and give completeness results for these models with real parameters. We also demonstrate that the computational overhead in these constructions is in all relevant cases polynomial. These results are proved by invoking a recently found cross-connection between statistical mechanics and quantum information theory, where partition functions are expressed as quantum mechanical amplitudes. Within this framework, there exists a natural correspondence between many-body quantum states that allow universal quantum computation via local measurements only, and complete classical spin systems.
One of the theoretical pillars that sustain certain machine learning models are universal approximation theorems, which prove that they can approximate all functions from a function class to arbitrary precision. Independently, classical spin models are termed universal if they can reproduce the behavior of any other spin model in their low energy sector. Universal spin models have been characterized via sufficient and necessary conditions, showing that simple models such as the 2d Ising with fields are universal. In this work, we prove that universal spin models are universal approximators of probability distributions. This enables us to leverage the characterization of the former to reveal conditions which are sufficient for universal approximation. Deriving universal approximation theorems thus amounts to verifying these conditions, yielding a unified recipe for universal approximation theorems applicable to a wide range of models. We explicitly test this recipe for restricted and deep Boltzmann machines, as well as for deep belief networks. This work illustrates that independently discovered universality statements may be intimately related, enabling the transfer of results.
We prove that the 2D Ising model is complete in the sense that the partition function of any classical q-state spin model (on an arbitrary graph) can be expressed as a special instance of the partition function of a 2D Ising model with complex inhomogeneous couplings and external fields. In the case where the original model is an Ising or Potts-type model, we find that the corresponding 2D square lattice requires only polynomially more spins with respect to the original one, and we give a constructive method to map such models to the 2D Ising model. For more general models the overhead in system size may be exponential. The results are established by connecting classical spin models with measurement-based quantum computation and invoking the universality of the 2D cluster states.
Spin models are widely studied in the natural sciences, from investigating magnetic materials in condensed matter physics to studying neural networks. Previous work has demonstrated that there exist simple classical spin models that are universal: they can replicate—in a precise and rigorous sense—the complete physics of any other classical spin model, to any desired accuracy. However, all previously known universal models break translational invariance. In this paper we show that there exist translationally invariant universal models. Our main result is an explicit construction of a translationally invariant, 2D, nearest-neighbour, universal classical Hamiltonian with a single free parameter. The proof draws on techniques from theoretical computer science, in particular recent complexity theoretic results on tiling problems. Our results imply that there exists a single Hamiltonian which encompasses all classical spin physics, just by tuning a single parameter and varying the size of the lattice. We also prove that our construction is optimal in terms of the number of parameters in the Hamiltonian; there cannot exist a translationally invariant universal Hamiltonian with only the lattice size as a parameter.
Spin models are widely studied in the natural sciences, from investigating magnetic materials in condensed matter physics to studying neural networks. Previous work has demonstrated that there exist simple classical spin models that are universal: they can replicate—in a precise and rigorous sense—the complete physics of any other classical spin model, to any desired accuracy. However, all previously known universal models break translational invariance. In this paper we show that there exist translationally invariant universal models. Our main result is an explicit construction of a translationally invariant, 2D, nearest-neighbour, universal classical Hamiltonian with a single free parameter. The proof draws on techniques from theoretical computer science, in particular recent complexity theoretic results on tiling problems. Our results imply that there exists a single Hamiltonian which encompasses all classical spin physics, just by tuning a single parameter and varying the size of the lattice. We also prove that our construction is optimal in terms of the number of parameters in the Hamiltonian; there cannot exist a translationally invariant universal Hamiltonian with only the lattice size as a parameter.