Researchers Develop Machine Learning Algorithm to Help Further Understand Quantum Systems

The University of Bristol's Quantum Engineering Technology Labs scientists recently developed machine learning algorithms that offer valuable understandings into the physics underlying quantum systems, paving the way for substantial advances in both quantum computation and sensing, and possibly turning a new page in scientific exploration.

According to a Phys.org report, in physics, systems of particles, as well as their evolution are described by mathematical representations, necessitating the successful interplay of theoretical debates and investigational verifications.

Even more multifaceted is the description of the particles' systems that interact with each other at the "quantum mechanical level," which is frequently done through the use of a Hamiltonian model.

This particular process of formulating Hamiltonian models from observations is made even more difficult by the nature of quantum states, which collapse when attempts are made for their inspection.

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Bayers Factors

In the paper entitled "Learning models of quantum systems from experiments," published in Nature Physics, quantum mechanics from QET Labs of Bristol are describing an algorithm that overcomes such challenges by functioning as an independent agent, through the use of machine learning to oppose or contradict engineer Hamiltonian models.

Essentially, the research team developed a new protocol for the formulation and validation of approximate models for quantum systems of interest.

As indicated in their study, the researchers' algorithm is working independently, designing and carrying out experiments on the aimed quantum system, with the resulting data being fed back into the algorithm.

It suggests candidate Hamiltonian models define the target system and determine them through the use of statistical metrics described in the Department of Statistics at the University of Washington as Bayers factors.

Algorithm

Interestingly, the team was able to successfully present the ability of the algorithm on real-life quantum experimentation involving deficiency centers in a diamond, and a well-examined platform for quantum detail processing and quantum sensing.

Such algorithm could be utilized to help automated characterization of new devices, like quantum sensors. Therefore, this development denotes a substantial innovation in the development of quantum technologies.

Integrating supercomputers with machine learning, the researchers said they were able to discover automatically, the structure in quantum systems.

As new quantum simulators or computers become available, explained Brian Flynn, from the University of Bristol's QETLabs and Quantum Engineering Center for Doctoral Training, the algorithms turn out to be more interesting. First, it can help validate the device's performance itself, then have those devices exploited to understand ever-bigger systems.

Meanwhile, according to Andreas Gentile, formerly from Bristol's QETLabs and currently at Qu & Co., the automation level is making it possible to entertain a multitude of hypothetical models prior to the selection of an optimal one, a task that would otherwise be intimidating for systems whose complication is ever rising.

Further Algorithm to Discover

Commenting on this new study, QuetLabs co-Director and Associate Professor in School of Physics of Bristol, Anthony Laing said, in the past, they have depended on the scientists' genius and hard work to uncover new physics.

Here, Laing, who is also an author of the study added, the team has potentially opened a new page in scientific experimentation by conferring machines with the ability to learn from trials and discover new physics.

The next step for this study is to extend the algorithm to discover larger systems and different classes of quantum models representing various physical regimes or fundamental structures.

Related information about algorithms is shown on TED's YouTube video below: