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Using Basic Quantum Circuits to Simulate 1D Ising Model

Jiaming Luo

Published Jan 10, 2024• 0 citations
Publisher

Abstract

The Ising model, a cornerstone in the understanding of critical points, phase transitions, and magnetic systems, has been pivotal in advancing our knowledge of statistical physics. Although analytical solutions exist for the 1D and 2D Ising models, complexities rise significantly with the inclusion of external magnetic fields and in higher dimensions. Here, we demonstrate a novel quantum computational approach to simulate the 1D Ising model with basic quantum gates in the QISKIT(Python) that is both accessible to beginners and executable on personal computers, bypassing the need for advanced computational infrastructure. By initializing the system in a comprehensive superposition state, we effectively utilize quantum gates to replicate the intricate interactions between spins and external fields. This method offers a more intuitive and direct observational means of the system's evolution, distinguishing it from previous methodologies. Our results not only repeat all the physics behavior in the 1D Ising model but also showcase the expanding capabilities of quantum computing to tackle complex physical systems, promising advancements in both theoretical and applied physics.

DOI
10.20944/preprints202401.0768.v1

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The Ising model, a cornerstone in the understanding of critical points, phase transitions, and magnetic systems, has been pivotal in advancing our knowledge of statistical physics. Although analytical solutions exist for the 1D and 2D Ising models, complexities rise significantly with the inclusion of external magnetic fields and in higher dimensions. Here, we demonstrate a novel quantum computational approach to simulate the 1D Ising model with basic quantum gates in the QISKIT(Python) that is both accessible to beginners and executable on personal computers, bypassing the need for advanced computational infrastructure. By initializing the system in a comprehensive superposition state, we effectively utilize quantum gates to replicate the intricate interactions between spins and external fields. This method offers a more intuitive and direct observational means of the system's evolution, distinguishing it from previous methodologies. Our results not only repeat all the physics behavior in the 1D Ising model but also showcase the expanding capabilities of quantum computing to tackle complex physical systems, promising advancements in both theoretical and applied physics.
The field of quantum computing has grown fast in recent years, both in theoretical advancements and the practical construction of quantum computers. These computers were initially proposed, among other reasons, to efficiently simulate and comprehend the complexities of quantum physics. In this paper, we present a comprehensive scheme for the exact simulation of the 1-D XY model on a quantum computer. We successfully diagonalize the proposed Hamiltonian, enabling access to the complete energy spectrum. Furthermore, we propose a novel approach to design a quantum circuit to perform exact time evolution. Among all the possibilities this opens, we compute the ground and excited state energies for the symmetric XY model with spin chains of n=4 n = 4 and n=8 n = 8 spins. Further, we calculate the expected value of transverse magnetization for the ground state in the transverse Ising model. Both studies allow the observation of a quantum phase transition from an antiferromagnetic to a paramagnetic state. Additionally, we have simulated the time evolution of the state all spins up in the transverse Ising model. The scalability and high performance of our algorithm make it an ideal candidate for benchmarking purposes, while also laying the foundation for simulating other integrable models on quantum computers.
A new interactive simulator for Quantum Computation has been developed for simulation of the universal set of quantum gates and for construction of new gates of up to 3 qubits. The simulator also automatically generates an equivalent quantum circuit for any arbitrary unitary transformation on a qubit. Available quantum computer simulators attempt to emulate the various physical realisations of quantum computation, simulate existing quantum algorithms or are aimed at facilitating the development of new algorithms. However, because of the level of advancement and complexity of quantum computation algorithms, these simulators tend to be quite complex, at least from a novice's point of view. As a result of this, beginners are often at a loss when trying to interact with them. The simulator here proposed therefore is aimed at bridging the gap somewhat, making quantum computer simulation more accessible to novices in the field. Journal of the Nigerian Association of Mathematical Physics Vol. 10 2006: pp. 433-446
Quantum computing offers a powerful new perspective on probabilistic and collective behaviors traditionally taught in statistical physics. This paper presents two classroom-ready modules that integrate quantum computing into the undergraduate curriculum using Qiskit: the quantum random walk and the Ising model. Both modules allow students to simulate and contrast classical and quantum systems, deepening their understanding of concepts such as superposition, interference, and statistical distributions. We outline the quantum circuits involved, provide sample code and student activities, and discuss how each example can be used to enhance student engagement with statistical physics. These modules are suitable for integration into courses in statistical mechanics, modern physics, or as part of an introductory unit on quantum computing.
The investigation of many-body quantum systems stands as a cornerstone in contemporary physics research. These systems exhibit a richness of phenomena, characterized by the emergence of collective behavior, which leads to the formation of quasi-particle excitations with occasionally unconventional statistics, alongside the occurrence of quantum phase transitions. Their significance extends further as they serve as models for understanding the behavior of real quantum materials, underlining the necessity for their comprehension.In the realm of theoretical physics, exact solutions are often elusive for many-body systems and are limited to a few select cases or toy models. However, real materials are often more complicated with additional dominant interactions beyond solvable limits. Consequently, the study and understanding of generic Hamiltonians necessitate resorting to computational simulations. Utilizing computational resources becomes indispensable, albeit challenging, as the Hilbert space expands exponentially with the number of spins, demanding substantial memory and computational bandwidth.However, recent advancements in quantum computing present a promising avenue for tackling these challenges. Quantum computers offer the potential to simulate many-body quantum systems with minimal overhead, overcoming the limitations of classical computational techniques. Although current quantum computing platforms operate within the confines of the NISQ era -noisy intermediate-scale quantum- they hold immense promise for the exact modelling of many-body systems.In this doctoral research, our objective is to investigate the predictive capabilities of quantum computers through simulating and benchmarking quantum many-body systems relevant to magnetic materials. We showcase three different projects on studying frustrated magnetism: 1) simulating non-equilibrium dynamics of frustrated Ising models on an analog quantum annealer, 2) studying frustrated Rydberg atom quench dynamics on a neutral atom platform, and 3) simulating the Kitaev honeycomb model and probing its topological order on a digital quantum computer. By leveraging the potential of quantum computing in this domain, this research aims to advance both theoretical understanding and practical applications in quantum physics.
Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.
Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.