Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Peking University-World Scientific advanced physics series/Peking University-World Scientific advance physics series

Simulation of Spin Systems

Published Nov 1, 2018• 0 citations
Publisher
DOI
10.1142/9789813270763_0016

Related papers

Monte Carlo Simulations of Spin Systems

Wolfhard Janke E

Springer eBooks•Jan 1, 1996•39 citations

Title: Quantum Spin-Based Complex System Simulation

Jincheng Zhang

This paper presents a novel approach to complex system simulation utilizing the unique properties of quantum spin. Traditional simulation methods often struggle with the complexity of real-world systems, limiting their accuracy and computational efficiency. We propose a model based on quantum spin evolution, offering a potentially more precise and efficient method for simulating these systems. The core of this work lies in establishing a fundamental model that leverages quantum spin's inherent properties for accurate representation and prediction of complex system behavior. This research focuses on the creation of a robust and scalable framework for simulating systems with intricate interactions and dynamics. The aim is to demonstrate the potential of quantum spin simulations to achieve significant advancements in various fields, including materials science, chemistry, and physics.

•Sep 7, 2026•0 citations

Monte Carlo simulation of model spin systems

S. Ramasesha

Proceedings of the Indian Academy of Sciences Section C Engineering Sciences•Mar 1, 1979•1 citation

Photonic simulation of disordered spin systems on a silicon chip

Alif Laila Muthali, Patrik Isene Sund, Carlos F. D. Faurby +11 more

The ability to simulate physical systems is essential for an understanding of their physical properties and advancing related technologies. However, simulating certain complex systems at scale, including many-body spin models, is often a significant challenge for conventional computing approaches. Quantum simulators, employing controlled quantum systems that mimic the dynamics of a target system, can offer a more efficient approach. This work demonstrates the use of photonic circuits for quantum simulation of disordered spin systems, where quantum properties such as superposition and interference can be leveraged for a more efficient simulation. We fabricate and characterize a low-loss silicon-on-insulator chip with a buried aluminum mirror and a reconfigurable interferometer, which is employed in conjunction with two input photons to map the dynamics of a spin Hamiltonian with four-body interactions. Our results demonstrate several characteristics associated with the target system, such as spin-degenerate ground states and phase transitions, confirming the device’s functionality as a quantum simulator of the spin system.

Advanced Photonics Nexus•Mar 3, 2026•0 citations

Highly efficient quantum spin dynamics simulation algorithms

Luke Edwards

Spin dynamics simulations are used to gain insight into important magnetic resonance experiments in the fields of chemistry, biochemistry, and physics. Presented in this thesis are investigations into how to accelerate these simulations by making them more efficient. Chapter 1 gives a brief introduction to the methods of spin dynamics simulation used in the rest of the thesis. The `exponential scaling problem' that formally limits the size of spin system that can be simulated is described. Chapter 2 provides a summary of methods that have been developed to overcome the exponential scaling problem in liquid state magnetic resonance. The possibility of utilizing the multiple processors prevalent in modern computers to accelerate spin dynamics simulations provides the impetus for the investigation found in Chapter 3. A number of different methods of parallelization leading to acceleration of spin dynamics simulations are derived and discussed. It is often the case that the parameters defining a spin system are time-dependent. This complicates the simulation of the spin dynamics of the system. Chapter 4 presents a method of simplifying such simulations by mapping the spin dynamics into a larger state space. This method is applied to simulations incorporating mechanical spinning of the sample with powder averaging. In Chapter 5, implementations of several magnetic resonance experiments are detailed. In so doing, use of techniques developed in Chapters 2 and 3 are exemplified. Further, specific details of these experiments are utilized to increase the efficiency of their simulation.

•Jan 1, 2014•0 citations

Monte Carlo simulation of quantum spin systems

俊文 坂口

•Jan 1, 1985•0 citations
This paper presents a novel approach to complex system simulation utilizing the unique properties of quantum spin. Traditional simulation methods often struggle with the complexity of real-world systems, limiting their accuracy and computational efficiency. We propose a model based on quantum spin evolution, offering a potentially more precise and efficient method for simulating these systems. The core of this work lies in establishing a fundamental model that leverages quantum spin's inherent properties for accurate representation and prediction of complex system behavior. This research focuses on the creation of a robust and scalable framework for simulating systems with intricate interactions and dynamics. The aim is to demonstrate the potential of quantum spin simulations to achieve significant advancements in various fields, including materials science, chemistry, and physics.
The ability to simulate physical systems is essential for an understanding of their physical properties and advancing related technologies. However, simulating certain complex systems at scale, including many-body spin models, is often a significant challenge for conventional computing approaches. Quantum simulators, employing controlled quantum systems that mimic the dynamics of a target system, can offer a more efficient approach. This work demonstrates the use of photonic circuits for quantum simulation of disordered spin systems, where quantum properties such as superposition and interference can be leveraged for a more efficient simulation. We fabricate and characterize a low-loss silicon-on-insulator chip with a buried aluminum mirror and a reconfigurable interferometer, which is employed in conjunction with two input photons to map the dynamics of a spin Hamiltonian with four-body interactions. Our results demonstrate several characteristics associated with the target system, such as spin-degenerate ground states and phase transitions, confirming the device’s functionality as a quantum simulator of the spin system.
Spin dynamics simulations are used to gain insight into important magnetic resonance experiments in the fields of chemistry, biochemistry, and physics. Presented in this thesis are investigations into how to accelerate these simulations by making them more efficient. Chapter 1 gives a brief introduction to the methods of spin dynamics simulation used in the rest of the thesis. The `exponential scaling problem' that formally limits the size of spin system that can be simulated is described. Chapter 2 provides a summary of methods that have been developed to overcome the exponential scaling problem in liquid state magnetic resonance. The possibility of utilizing the multiple processors prevalent in modern computers to accelerate spin dynamics simulations provides the impetus for the investigation found in Chapter 3. A number of different methods of parallelization leading to acceleration of spin dynamics simulations are derived and discussed. It is often the case that the parameters defining a spin system are time-dependent. This complicates the simulation of the spin dynamics of the system. Chapter 4 presents a method of simplifying such simulations by mapping the spin dynamics into a larger state space. This method is applied to simulations incorporating mechanical spinning of the sample with powder averaging. In Chapter 5, implementations of several magnetic resonance experiments are detailed. In so doing, use of techniques developed in Chapters 2 and 3 are exemplified. Further, specific details of these experiments are utilized to increase the efficiency of their simulation.