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Machine learning Gaussian process

Los Alamos Team Discovers Quantum Machine Learning "Holy Grail" A New Path Out of Convention

Los Alamos National Laboratory scientists mathematically proved that quantum neural networks may generate Gaussian processes, a key breakthrough that could change quantum machine learning. This significant discovery offers a robust framework for constructing quantum-native machine learning models, unlike previous attempts to apply classical methods to quantum systems.

Recently published in Nature Physics, the team's findings address an ongoing quantum computing issue. Marco Cerezo, Los Alamos' chief scientist, said, “Their goal for this project was to see if we could prove that genuine quantum Gaussian processes exist.” He said success will “spur innovations and new forms of performing quantum machine learning.”

The Unexpected Cons of Traditional Adaptation

Neural networks on traditional computers have revolutionised artificial intelligence, language translation, and self-driving cars. Researchers naturally wanted to transfer this massive power to quantum computers to improve their ability to accomplish complex tasks.

However, these measures caused unexpected issues. Variational parameters are modified to facilitate learning in quantum neural networks and other quantum parametric models. After years of research, the Lab team has shown that these models often generate major issues in quantum computing, such as barren plateaus that cause mathematical dead ends.

The Lab's quantum algorithms and machine learning expert Martin Larocca said, “The problem with quantum neural networks is that it was copying and pasting classical neural networks and putting them in a quantum computer.” “This seems harder than expected. Thus, it attempted to return to the basics and find simpler, more limited teaching approaches that were effective and guaranteed.

Non-parametric Gaussian Processes

Traditional machine learning relied on the fact that large neural networks spontaneously converge to Gaussian processes. A neural network with millions of mathematical “neurones” makes informed predictions and produces data that fits a Gaussian curve, or bell curve, allowing researchers to estimate averages.

Gaussian processes, unlike neural networks, are non-parametric. Due to this important difference, they automatically avoid several quantum parametric model issues, including barren plateaus. The Los Alamos team analytically showed that the Gaussian curve notion applies to specific quantum computing processes, promising to transform quantum computing.

“This is the Holy Grail of Bayesian learning,” said Diego Garcia-Martin, the paper's first author. He showed real-world applications by comparing it to real estate price forecasting:

Using the Gaussian process to update the bell curve as you collect data like property prices produces a more exact and refined distribution. Bayesian inference via Gaussian process regression improves predictions with additional data. “The result implies that the same principle can now be applied in quantum computing,” Garcia-Martin says. The scientists employed advanced mathematical methods to check their computations to guarantee that their unique strategy was Gaussian and suitable for processing quantum datasets on quantum computers.

A New Quantum-Native Model Search

This groundbreaking discovery shows that Gaussian processes on quantum computers may reproduce neural network power after years of hard labour. Even though quantum computers are new, this foundational theoretical study is significant. After strong quantum computers are constructed, researchers will have powerful machine learning models to solve some of the world's hardest and most intractable challenges.

This endeavour is a mandate that steers the quantum community, according to Los Alamos. In other words, scientists should cease applying classical computer models to quantum machine learning.