Modeling of quantum spin liquids using machine learning

Modeling of quantum spin liquids using machine learning

Figure 1: Using a machine learning algorithm that mimics the network of neurons in the brain, a RIKEN physicist and a collaborator have developed a method for modeling quantum spin fluid states. Credit: Jesper Klausen / Science Photo Library

The properties of a complex and exotic state of a quantum material can be predicted using a machine learning method created by a RIKEN researcher and a collaborator. This progress can help the development of future quantum computers.

We have all faced the painful challenge of choosing between two equally good (or bad) options. This frustration is also felt by fundamental particles when they feel two competing forces in a special type of quantum system.

In some magnets, particle spin – visualized as the axis about which a particle rotates – is all forced to adjust, while in others it has to change direction. However, in a small number of materials, these tendencies compete to adjust or resist, leading to so-called frustrated magnetism. This frustration means that the web oscillates between the directions, even at absolute zero temperature, where one would expect stability. This creates an exotic state of matter known as a quantum spin liquid.

“This exciting and unusual” liquid “state of quantum web is expected to have unique quantum entanglement properties that differ from those of a standard” solid “system,” explains Yusuke Nomura of the RIKEN Center for Emergent Matter Science. “And these entanglement properties are potentially useful for quantum computations in quantum computers.”

However, modeling a quantum spin fluid is very challenging because the number of interdependent spin configurations that make up its quantum state increases exponentially with the number of particles.

Now, Nomura and a partner have overcome this problem by developing a machine learning method that can model quantum-multi-body systems. It can reveal the existence of a quantum spin liquid phase in a frustrated magnet, where the next nearest neighbor spin interacts within a specific range of forces relative to those between the nearest neighbor spin.

“Our newly developed machine learning method has overcome the difficulties associated with these complex systems,” says Nomura. “It has established the existence of a quantum spin fluid in a two-dimensional spin system.”

The study provides a useful guideline for realizing quantum spin liquid phases in real materials. But there is a broader message: Research highlights the power of machine learning as a tool to solve major challenges in physics. “Using machine learning as a new tool, we have solved a long-standing problem in physics that was difficult to solve with the human brain without help,” says Nomura. “In the future, the use of ‘machine brains’ in addition to human brains will shed new light on other unsolved problems. It marks the beginning of a new era of research in physics.”

Understanding finite-temperature quantum effects better with machine learning

More information:
Yusuke Nomura et al., Dirac-Type Nodal Spin Liquid Detected by Raffined Quantum Many-Body Solver Using Neural Network Wave Function, Correlation Ratio and Level Spectroscopy, Physical review X (2021). DOI: 10.1103 / PhysRevX.11.031034

Citation: Modeling of quantum spin liquids using machine learning (2021, November 19) retrieved November 20, 2021 from

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