A Quantum Mechanics Approach to AI Can Improve Cancer Outcomes

For a child diagnosed with neuroblastoma—the most common infant cancer, occurring when early nerve cells grow out of control—the path to treatment isn’t simple. Some types of neuroblastoma resolve on their own, while others require aggressive intervention. Researchers have tried matching treatments to patients based on one-gene mutations with limited success. This is because patients’ outcomes depend on their entire molecular background containing millions or even billions of features, such as DNA and RNA from tissues and blood.

“It’s much more than just one gene—everything that’s happening in the cells of the patient matters,” said Orly Alter, a University of Utah associate professor of biomedical engineering and a researcher in the Scientific Computing and Imaging and Huntsman Cancer Institute’s Cancer Control and Population Sciences program.

Current artificial intelligence and machine learning (AI/ML) approaches require massive amounts of training data, and, specifically, vastly more patient samples than genetic features. This makes them poorly suited for predicting patient outcomes in most clinical trials, which typically enroll just 20 to 100 people. For example, a recent large language model of the 30,000-nucleotide genome of the COVID-19 virus required about 110 million samples. Translating this to the 3-billion-nucleotide human genome, a conventional AI approach would need 33 trillion patients.

By using the mathematics of quantum mechanics, Alter and her collaborators developed a novel AI/ML technique that can improve treatment selections and drug success rates. 

Billions of molecular features

“Our quantum approach allows us to find the relevant information in every layer of the data, for example, from the patients’ blood in addition to their tumors,” Alter said. “Even for very few patients, we can still take everything in—their millions to billions of molecular features—and make sense of them. We can, therefore, understand the disease mechanisms, and predict drug targets to improve patients’ outcomes. We also validate our AI/ML predictions of targets and outcomes experimentally, which is widely considered a biotechnology holy grail.”

The technique deploys a set of algorithms, called multitensor comparative spectral decompositions, which Alter built on the quantum mechanical concepts of entanglement and superposition. Like a prism splitting white light into individual colors, this approach breaks down a patient’s multiple layers of molecular data—such as their tumor and blood genomes and tumor transcriptome (or the RNA messages driving the cancer’s growth)—into linked patterns that predict health outcomes.

Alter and her team demonstrated their technique with an analysis of open-source data of neuroblastoma cases. The algorithms discovered two new predictors of patients’ life expectancy in response to treatment, and these predictors consistently outperformed standard biomarkers across tumor and blood DNA and tumor RNA. These findings held up across separate groups of children treated at different times and hospitals, meaning that the method can be applied to the general population in order to provide a clearer roadmap for patient care and drug development.

Developing more targeted treatments

“Neural network models are black boxes, but our predictors are interpretable; they point to disease mechanisms and suggest genes to target to sensitize tumors to treatment,” Alter said. Her team also experimentally validated their predictions of adult glioblastoma patient outcomes and drug targets in clinical trials and preclinical studies harnessing CRISPR-Cas9, the gene-editing tool.

An expert in computational medicine, Alter holds an adjunct appointment in the U’s Department of Human Genetics, part of the School of Medicine. Her university spinoff company, Prism AI Therapeutics, Inc., uses the algorithms and predictors to help biotech and pharmaceutical companies to better develop drugs by identifying which patients would benefit most from a clinical trial, and which genes should be targeted to additionally improve outcomes.

Looking ahead, Alter hopes that as her team continues this work, they’ll be able to apply it to individual patients. “That’s the ultimate precision medicine,” she said. “You have a single person. Can you take the data from just that one person and come up with a treatment for them? I think we can get there.”

Alter also hopes to use the quantum approach to solve other challenges. “The algorithms are completely data agnostic, and there could be endless applications also outside of medicine,” she said, highlighting sustainable energy as one possibility.

Reference: Alter O, Newman E, Ponnapalli SP, Tsai JW. Quantum mechanics-based multitensor AI/ML uniquely able to discover, validate, and interpret predictors from small-cohort noisy high-dimensional multiomic data. APL Quantum. 2026;3(2):026116. doi: 10.1063/5.0305656

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