Overparametrization enhances performance in quantum nachine learning

Researchers at Los Alamos National Laboratory have discovered a technique called overparametrization that can significantly enhance the performance of quantum machine learning. This technique proves particularly useful for applications that are challenging for classical computers.

The team believes that their findings will have practical applications in quantum materials research, specifically in the classification of different phases of matter. This task is highly complex and poses difficulties for classical computing systems. By leveraging overparametrization, machine learning can be employed to learn the properties of quantum data more effectively.

Diego Garcia-Martin, a co-author of the study and postdoctoral researcher at Los Alamos, participated in the research during the Quantum Computing Summer School in 2021 while he was a graduate student at the Autonomous University of Madrid.

Machine learning, also known as artificial intelligence, typically involves training neural networks to process and analyze data, enabling them to solve specific tasks. Neural networks can be thought of as boxes with adjustable knobs or parameters. These parameters are updated during the training phase as the algorithm learns, with the goal of finding their optimal configuration. Once the optimal parameters are determined, the neural network can generalize its learning to new and unseen data points.

Both classical and quantum artificial intelligence face the challenge of reaching sub-optimal configurations during parameter training, which can impede further progress. Overparametrization addresses this issue and offers a promising solution to enhance quantum machine learning performance. The research paper detailing this technique was published in Nature Computational Science.

A leap in performance

The concept of overparametrization, widely recognized in classical machine learning, proves to be a remedy for the stalling issue encountered during parameter training.

The true implications of overparametrization in quantum machine learning models have remained elusive until now. However, a recent paper by the Los Alamos team fills this knowledge gap by establishing a theoretical framework. This framework enables the prediction of the critical number of parameters at which a quantum machine learning model enters the overparametrized state. Beyond this critical point, the addition of parameters triggers a significant improvement in network performance, making the model much easier to train.

Martin Larocca, the lead author of the paper and a postdoctoral researcher at Los Alamos, emphasized the significance of this research. By providing a theoretical foundation for overparametrization in quantum neural networks, their work opens up avenues for optimizing the training process and achieving superior performance in practical quantum applications.

Quantum machine learning leverages the principles of quantum mechanics, such as entanglement and superposition, to offer the potential for quantum advantage—vastly increased speed and capabilities compared to classical computers.

Avoiding traps in a machine learning landscape

To help understand the Los Alamos team’s findings, Marco Cerezo, a quantum theorist and senior scientist on the paper, offered a thought experiment. Imagine a hiker navigating a dark landscape in search of the tallest mountain. The hiker’s movement is constrained to certain directions, and they rely on a limited GPS system to gauge their progress.

In this analogy, the number of parameters in the machine learning model corresponds to the available directions for the hiker to move. Cerezo explains that a single parameter permits only forward and backward movement, while two parameters enable lateral motion, and so on. It’s important to note that real data landscapes are likely to have more than three dimensions, unlike the simplified world of our hypothetical hiker.

When the model has too few parameters, the hiker is unable to thoroughly explore the landscape. They may mistakenly identify a small hill as the tallest mountain or become stuck in a flat region where progress seems impossible. However, as the number of parameters increases, the hiker gains the ability to move in multiple directions and across higher dimensions. What may have initially appeared as a local hill could be revealed as an elevated valley between peaks. With the addition of more parameters, the hiker can avoid becoming trapped and ultimately discover the true peak or solution to the problem they are facing.

This analogy helps to illustrate how overparametrization in quantum machine learning allows for a more comprehensive exploration of the problem space and enhances the model’s ability to find optimal solutions.

Source: Los Alamos National Laboratory

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