Scientists at the Los Alamos National Laboratory have achieved extraordinary breakthroughs in quantum machine learning. They did this by taking advantage of Gaussian processes. Led by Marco Cerezo, the team recently detailed their findings in a new paper published in the journal Nature Physics. This study illustrates the tremendous power of Gaussian processes to improve prediction accuracy in the rapidly growing field of quantum computing. This is a historic advance in the discipline.
The study, DOI 10.1038/s41567-025-02883-z, underscores the importance of data availability. In the second, it highlights how this availability has a very real effect on the accuracy of predictions when using Bayesian inference via Gaussian process regression. This radical alternative aims to recreate all the magic of classic neural networks. Finding the right conditions to do so has long plagued researchers in the ever-evolving quantum computing atmosphere.
Understanding Gaussian Processes
Gaussian processes are statistical methods that converge into a bell curve, or Gaussian curve, as more data points become available. This feature allows for much more accurate forecasting. Even though this precision has drawn an enormous amount of interest for its use in quantum machine learning.
Researchers are still grappling with challenges that are ever-present. Until now, they’ve turned to classical neural networks as the ultimate template for quantum systems. This approach has obvious limitations. Traditional neural networks—which can reach up to hundreds of millions of mathematical nodes or “neurons”—often struggle to adapt to the vastly different quantum environments.
“We need to find new ways of doing quantum machine learning, not continue to beat a dead horse, so to say, by recycling old methods.”
These turned out to be a key stumbling blocks with parametric models inside QC, most notably: These models often lead to arid wastelands. These are calculational cul-de-sacs that hinder advances in quantum machine learning. Garcia-Martin, the paper’s first author, highlights the team’s objective:
Challenges in Quantum Machine Learning
This work by the team is a significant leap beyond the traditional approach of just taking classical neural networks and adapting them for quantum applications. Martin Larocca, who specializes in quantum algorithms and machine learning, expressed concerns about this approach:
“Our goal for this project was to see if we could prove that genuine quantum Gaussian processes exist.”
To his credit, his observation highlights the challenges and limits that come into play when we dangerously oversimplify the journey from classical to quantum computing paradigms.
“The issue with quantum neural networks is that we were copying and pasting classical neural networks and putting them in a quantum computer.”
Kudos to the Los Alamos team for a serious, major breakthrough! They mathematically proved that Gaussian processes can be an effective tool to use in quantum computing applications. They’re doing this work to lay the foundational groundwork to develop more robust, inclusive and effective learning algorithms. These algorithms in particular will be ideally tailored for quantum surroundings.
The Path Forward
This focus on going back to basic tenets of the practice will be key in addressing the gaps that still remain in the industry. The researchers believe that understanding how Gaussian processes can be effectively integrated into quantum frameworks could lead to significant advancements.
The potential real-world impact of this research is substantial. It opens a new frontier for real-world applications of quantum machine-learning, with great potential for improved performance and accuracy.
“This appears not to work as easily as one could have hoped. Hence, we wanted to go back to basics and find simpler, more restricted ways of learning, but which could actually work and also have certain guarantees.”
This emphasis on returning to foundational principles may be crucial for overcoming existing challenges in the field. The researchers believe that understanding how Gaussian processes can be effectively integrated into quantum frameworks could lead to significant advancements.
Garcia-Martin summarized the importance of their findings, calling it:
“This is the Holy Grail of Bayesian learning.”
The implications of this research extend beyond theoretical considerations; they open new avenues for practical applications in quantum machine learning, promising enhanced performance and reliability.