首页|Findings from Indian Institute for Technology Provides New Data about Machine Learning (Machine Learning Assisted Construction of a Shallow Depth Dynamic Ansatz for Noisy Quantum Hardware)
Findings from Indian Institute for Technology Provides New Data about Machine Learning (Machine Learning Assisted Construction of a Shallow Depth Dynamic Ansatz for Noisy Quantum Hardware)
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NETL
NSTL
Royal Soc Chemistry
Investigators publish new report on Machine Learning. According to news reporting originating in Mumbai, India, by NewsRx journalists, research stated, “The development of various dynamic ansatz-constructing techniques has ushered in a new era, making the practical exploitation of Noisy Intermediate-Scale Quantum (NISQ) hardware for molecular simulations increasingly viable. However, such ansatz construction protocols incur substantial measurement costs during their execution.” Funders for this research include Industrial Research and Consultancy Centre, Council of Scientific & Industrial Research (CSIR) - India, Industrial Research and Consultancy Centre, IIT Bombay and Science and Engineering Research Board, Government of India. The news reporters obtained a quote from the research from Indian Institute for Technology, “This work involves the development of a novel protocol that capitalizes on regenerative machine learning methodologies and many-body perturbation theoretical measures to construct a highly expressive and shallow ansatz within the variational quantum eigensolver (VQE) framework with limited measurement costs. The regenerative machine learning model used in our work is trained with the basis vectors of a low-rank expansion of the N-electron Hilbert space to identify the dominant high-rank excited determinants without requiring a large number of quantum measurements. These selected excited determinants are iteratively incorporated within the ansatz through their low-rank decomposition. The reduction in the number of quantum measurements and ansatz depth manifests in the robustness of our method towards hardware noise, as demonstrated through numerical applications. Furthermore, the proposed method is highly compatible with state-of-the-art neural error mitigation techniques. This resource-efficient approach is quintessential for determining spectroscopic and other molecular properties, thereby facilitating the study of emerging chemical phenomena in the near-term quantum computing framework.”
MumbaiIndiaAsiaCyborgsEmerging TechnologiesMachine LearningIndian Institute for Technology
NETL
NSTL
Royal Soc Chemistry
