Using principles of superposition and entanglement, researchers develop an AI framework to tailor patients’ cancer treatment to their entire molecular background.
By Mara Johnson-Groh, freelance science writer for American Institute of Physics Publishing’s Scilights, and Kelly Hermans, University of Utah Scientific Computing and Imaging Institute
A version of this story was published on @theU
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. Their work appears in the journal Applied Physics Letters (APL) Quantum.
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.
Using the quantum mechanical principles of entanglement and superposition, Alter and team’s novel AI/ML framework split approximately 6 million tumor and blood genomic features from only 101 neuroblastoma patient samples into 3 orthogonal sets of 2 linked patterns each, where 2 of the sets predict health outcomes, and the third is correlated with normal gender variation. (Credit: Orly Alter, University of Utah)
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.
The quantum mechanics-based technique allowed Alter and team to take in approximately 6 million tumor and blood DNA and tumor RNA features from only 71 neuroblastoma patient samples, and derive, test, and interpret new predictors of patients’ life expectancy in response to treatment. The new predictors consistently outperformed standard biomarkers and are applicable to the general population. (Credit: Orly Alter, University of Utah)
This study was published June 22, 2026, under title, “Quantum Mechanics-Based Multitensor AI/ML Uniquely Able to Discover, Validate, and Interpret Predictors from Small-Cohort Noisy High-Dimensional Multiomic Data,” in the journal Applied Physics Letters (APL) Quantum. Co-authors include Elizabeth Newman (Tufts University), Sri Priya Ponnapalli (Scale AI, Inc.), and Jessica W. Tsai (Children’s Hospital of Los Angeles and Keck School of Medicine of the University of Southern California).
The research was supported by the National Institutes of Health (NIH) and National Cancer Institute Physical Sciences in Oncology project “Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics,” the National Science Foundation (NSF) and American Institute of Mathematics Quantum Research Community project “AIM Q,” the Musella Foundation in partnership with StacheStrong, the NIH Office of the Director, the NSF Division of Mathematical Sciences, Alex’s Lemonade Stand Foundation, the Rally Foundation, and St. Baldrick’s Foundation in partnership with Griffin’s Guardians.
The critical research happening every day at Huntsman Cancer Institute is supported by the National Institutes of Health/National Cancer Institute, including cancer center support grant P30 CA042014, as well as Huntsman Cancer Foundation.
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