Mphasis announced the award of a U.S. patent on System and method for optimized processing of information on quantum systems. The newly issued patent outlines a pipeline to improve the scalability and performance of quantum machine learning (QML) on near-term quantum computing systems including quantum simulators.
This solution transforms high-dimensional classical input data into an enhanced feature space in quantum format. The feature space transformation ensures efficient mapping and preparation for quantum state loading, paving the way for improved quantum data processing and analysis. The optimal representation method for classical data on quantum systems minimizes the need for additional qubits for higher-dimensional data, handles large feature sets and high volumes of data, and ensures efficient convergence during quantum machine learning (QML) model training. QML leverages its ability to process high-dimensional, complex data, delivering solutions beyond the reach of classical high performance computing hardware.
Quantum Machine Learning (QML) is emerging as a transformative paradigm, enabling academia and industry to solve real-world AI challenges with unprecedented efficiency. This technology and the associated patent stand as a testament to our commitment to innovation and the advancement of next-generation technologies, said Srikumar Ramanathan, Chief Solutions Officer, Mphasis.
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