The Demonstrated Proprietary Technology for Converting Continuous Data into Quantum-Compatible Energy Maps and Achieves More Than 10x Faster Runtime with GPU Acceleration
Tel-Aviv, Israel, July 14, 2026 (GLOBE NEWSWIRE) -- Quantum X Labs Inc. (the "Company") (NASDAQ: QXL) today announced that the CliniQuantum operation, has successfully validated a quantum sampling workflow that enables continuous probability distributions to be represented and analyzed within a quantum computing framework using Quantum X Labs' proprietary algorithmic technology and related intellectual property portfolio. The milestone demonstrates the ability to transform continuous data into quantum-compatible energy map representations capable of supporting advanced quantum algorithms.
Many real-world problems in healthcare, life sciences, artificial intelligence, financial modeling, and advanced analytics are naturally expressed as continuous probability distributions. To address this challenge, Quantum X Labs developed a proprietary methodology that converts continuous data into an energy landscape representation suitable for quantum computation. This representation enables the application of Quantum Markov Chain Monte Carlo (QMCMC) techniques while preserving the statistical properties of the original dataset.
As part of the validation, the team tested the workflow using a multi-modal probability distribution composed of two Gaussian functions. This benchmark was selected because it provides a visually verifiable continuous landscape containing multiple high-probability regions. The resulting samples accurately reproduced the underlying structure of the target distribution, confirming that Quantum X Labs' energy-map representation effectively captures key probability features while supporting quantum-based sampling.
The workflow combines quantum state evolution with a classical Metropolis-Hastings acceptance process. Continuous variables are discretized, transformed into a quantum-compatible energy landscape, and encoded into a problem Hamiltonian. Quantum dynamics are then used to generate proposed samples, while the classical acceptance step preserves the desired target distribution. This hybrid quantum-classical architecture allows continuous data to be explored using quantum-generated proposals while maintaining established statistical guarantees.
"Our objective was to demonstrate that continuous probability data can be reliably translated into a quantum-operable representation without compromising the integrity of the underlying distribution," said Prof. Nir Sharon, Chief Scientist of Quantum X Labs. "The successful execution of the QMCMC workflow validates a foundational capability of our proprietary technology and further supports the value of the intellectual property portfolio we are building around practical quantum computing applications."