Mphasis gets U.S. patent for boosting scalability, performance of quantum machine learning

Mphasis, Bengaluru-based IT firm specialising in cloud and cognitive services, on Wednesday said it was awarded 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, as per company communique.

According to the company, 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 would minimise the need for additional qubits for higher-dimensional data, would handle large feature sets and high volumes of data, and would ensure efficient convergence during quantum machine learning (QML) model training.

“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.