We study complexity of several problems related to the Transverse field Ising Model (TIM). First, we consider the problem of estimating the ground state energy known as the Local Hamiltonian Problem (LHP). It is shown that the LHP for TIM on degree-3 graphs is equivalent modulo polynomial reductions to the LHP for general k-local `stoquastic' Hamiltonians with any constant $k\ge 2$ . This result implies that estimating the ground state energy of TIM on degree-3 graphs is a complete problem for the complexity class StoqMA - an extension of the classical class MA. As a corollary, we complete the complexity classification of 2-local Hamiltonians with a fixed set of interactions proposed recently by Cubitt and Montanaro. Secondly, we study quantum annealing algorithms for finding ground states of classical spin Hamiltonians associated with hard optimization problems. We prove that the quantum annealing with TIM Hamiltonians is equivalent modulo polynomial reductions to the quantum annealing with a certain subclass of k-local stoquastic Hamiltonians. This subclass includes all Hamiltonians representable as a sum of a k-local diagonal Hamiltonian and a 2-local stoquastic Hamiltonian.We study complexity of several problems related to the Transverse field Ising Model (TIM). First, we consider the problem of estimating the ground state energy known as the Local Hamiltonian Problem (LHP). It is shown that the LHP for TIM on degree-3 graphs is equivalent modulo polynomial reductions to the LHP for general k-local `stoquastic' Hamiltonians with any constant $k\ge 2$. This result implies that estimating the ground state energy of TIM on degree-3 graphs is a complete problem for the complexity class StoqMA - an extension of the classical class MA. As a corollary, we complete the complexity classification of 2-local Hamiltonians with a fixed set of interactions proposed recently by Cubitt and Montanaro. Secondly, we study quantum annealing algorithms for finding ground states of classical spin Hamiltonians associated with hard optimization problems. We prove that the quantum annealing with TIM Hamiltonians is equivalent modulo polynomial reductions to the quantum annealing with a certain subclass of k-local stoquastic Hamiltonians. This subclass includes all Hamiltonians representable as a sum of a k-local diagonal Hamiltonian and a 2-local stoquastic Hamiltonian.

arXiv:1410.0703Oct 2, 20148 citations

Multimodal discrete diffusion for quantum measurement generation in the one-dimensional transverse-field Ising model

Anonymous, Ho-Kin Tang

Diffusion-based large language models have recently attracted significant interest as a promising class of nonautoregressive generative models. However, their applications to quantum many-body systems remain largely unexplored. We introduce M-Diffushadow, a multimodal diffusion language model for quantum many-body systems. In M-Diffushadow, we adopt a full attention mechanism alongside iterative decoding, which better aligns with the physical characteristics of quantum systems and thereby enables a more faithful modeling of such systems. As a multimodal approach, M-Diffushadow enables bidirectional cross-modal inference, reconstruction from partially observed multimodal records, and fully joint generation without prior measurement outcomes, aligning naturally with realistic experimental constraints where measurement access is limited or incomplete. We demonstrate the effectiveness of the framework on the one-dimensional transverse-field Ising model (TFIM), where generated data accurately reproduce ground-state energies and correlation functions across different phases. We further provide an additional numerical example on the spin chain to support the applicability of M-Diffushadow beyond the original TFIM benchmark. M-Diffushadow shows the potential to capture and analyze the complex quantum phenomena in quantum many-body systems.Diffusion-based large language models have recently attracted significant interest as a promising class of nonautoregressive generative models. However, their applications to quantum many-body systems remain largely unexplored. We introduce M-Diffushadow, a multimodal diffusion language model for quantum many-body systems. In M-Diffushadow, we adopt a full attention mechanism alongside iterative decoding, which better aligns with the physical characteristics of quantum systems and thereby enables a more faithful modeling of such systems. As a multimodal approach, M-Diffushadow enables bidirectional cross-modal inference, reconstruction from partially observed multimodal records, and fully joint generation without prior measurement outcomes, aligning naturally with realistic experimental constraints where measurement access is limited or incomplete. We demonstrate the effectiveness of the framework on the one-dimensional transverse-field Ising model (TFIM), where generated data accurately reproduce ground-state energies and correlation functions across different phases. We further provide an additional numerical example on the spin chain to support the applicability of M-Diffushadow beyond the original TFIM benchmark. M-Diffushadow shows the potential to capture and analyze the complex quantum phenomena in quantum many-body systems.

APS Open ScienceAug 17, 20260 citations

Dynamics of Quantum Ising Systems

Lecture notes in physics. M, MonographsJan 1, 19960 citations

Quantum Ising Model

Geoffrey Grimmett

Cambridge University Press eBooksJan 15, 20182 citations

Neural-Network Quantum State of Transverse-Field Ising Model*

Han-Qing 汉青 Shi 石, Xiao-Yue 小岳 Sun 孙, Ding-Fang 定方 Zeng 曾

Abstract Along the way initiated by Carleo and Troyer [G. Carleo and M. Troyer, Science 355 (2017) 602], we construct the neural-network quantum state of transverse-field Ising model (TFIM) by an unsupervised machine learning method. Such a wave function is a map from the spin-configuration space to the complex number field determined by an array of network parameters. To get the ground state of the system, values of the network parameters are calculated by a Stochastic Reconfiguration (SR) method. We provide for this SR method an understanding from action principle and information geometry aspects. With this quantum state, we calculate key observables of the system, the energy, correlation function, correlation length, magnetic moment, and susceptibility. As innovations, we provide a high efficiency method and use it to calculate entanglement entropy (EE) of the system and get results consistent with previous work very well .Abstract Along the way initiated by Carleo and Troyer [G. Carleo and M. Troyer, Science 355 (2017) 602], we construct the neural-network quantum state of transverse-field Ising model (TFIM) by an unsupervised machine learning method. Such a wave function is a map from the spin-configuration space to the complex number field determined by an array of network parameters. To get the ground state of the system, values of the network parameters are calculated by a Stochastic Reconfiguration (SR) method. We provide for this SR method an understanding from action principle and information geometry aspects. With this quantum state, we calculate key observables of the system, the energy, correlation function, correlation length, magnetic moment, and susceptibility. As innovations, we provide a high efficiency method and use it to calculate entanglement entropy (EE) of the system and get results consistent with previous work very well .

arXiv:1905.11066Communications in Theoretical PhysicsNov 1, 20195 citations

k\ge 2

. This result implies that estimating the ground state energy of TIM on degree-3 graphs is a complete problem for the complexity class StoqMA - an extension of the classical class MA. As a corollary, we complete the complexity classification of 2-local Hamiltonians with a fixed set of interactions proposed recently by Cubitt and Montanaro. Secondly, we study quantum annealing algorithms for finding ground states of classical spin Hamiltonians associated with hard optimization problems. We prove that the quantum annealing with TIM Hamiltonians is equivalent modulo polynomial reductions to the quantum annealing with a certain subclass of k-local stoquastic Hamiltonians. This subclass includes all Hamiltonians representable as a sum of a k-local diagonal Hamiltonian and a 2-local stoquastic Hamiltonian.