By Yohai Schweiger

Israeli company Quantum X Labs has filed a U.S. patent application for a technology designed to accelerate and improve the analysis of clinical-trial data by integrating quantum computing. According to the disclosure, the invention centers on a computational algorithm built to handle large volumes of complex biological and clinical data, enabling more efficient inference about treatment responses, patient sub-populations, and probabilistic patterns using quantum capabilities.

The patent filing was reported on Wednesday by Geeks Internet, a publicly traded company on the Tel Aviv Stock Exchange. Geeks Internet holds approximately 26.42% of Viewbix, which is currently in the process of acquiring Quantum X Labs. Viewbix stated that Quantum X Labs—the acquisition target—submitted the U.S. patent application, and that development was carried out in collaboration with CliniQuantum, a portfolio company of Quantum X Labs focused on applying quantum computing to clinical-trial analysis.

The patent application is titled “Generating Quantum Markov Chain Monte Carlo Sampling Points for Continuous Distribution Functions.” The title offers a clear window into the technology’s core. At its heart lies a well-established statistical and AI framework known as Markov Chain Monte Carlo (MCMC)—a family of algorithms used for probabilistic sampling and inference when systems are too complex for direct calculation, such as those involving many variables and high uncertainty. In clinical trials, MCMC methods help model treatment responses, estimate probabilities, and uncover hidden patterns within noisy or incomplete datasets.

According to the public description, the innovation Quantum X Labs seeks to protect does not alter clinical protocols themselves, but rather the algorithmic sampling methodology. In a clinical-trial context, “samples” are not only lab measurements but computational representations of possible scenarios—different combinations of patient characteristics, treatment responses, dosages, and outcomes over time. MCMC algorithms rely on such samples to construct a probabilistic picture of how a therapy performs, even when data are partial or imperfect. The disclosed algorithm introduces a quantum component to generate these sampling points more efficiently and accurately from complex, continuous distributions. This approach could shorten computation times, improve statistical precision, and potentially enable comparable conclusions with fewer patients or fewer real-world measurements. In that sense, the patent targets the algorithmic and methodological layer, with a clear application to medical research.

Importantly, the algorithm is not intended to run entirely on a standalone quantum computer. Instead, it follows a hybrid architecture: the overall statistical computation runs on classical computers, while a quantum processor is integrated as an accelerator for specific stages—primarily the generation of complex sampling points. This design aims to leverage today’s available quantum advantages without depending on fully mature, large-scale quantum hardware.

Development was carried out in collaboration with CliniQuantum, which focuses on applying advanced computing—including quantum methods—to clinical-trial analytics. CliniQuantum positions itself as a platform for identifying patient sub-populations, response patterns, and biological signatures within complex datasets, supporting probabilistic decision-making throughout clinical studies. Quantum X Labs develops quantum technologies across multiple domains, centralizes the intellectual property, and leads the core algorithmic work, while CliniQuantum serves as the applied arm for clinical use cases.

[Image above: Construction of a commercial-grade quantum computer at IBM. Photo: IBM]