Despite its undeniable success, classical machine learning remains a resource-intensive
process. Practical computational efforts for training state-of-the-art models can now only be …

Quantum Random Access Memory (QRAM) has the potential to revolutionize the area of
quantum computing. QRAM uses quantum computing principles to store and modify …

Distributed training across several quantum computers could significantly improve the
training time and if we could share the learned model, not the data, it could potentially …

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields
such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges …

Deep Neural Networks have offered numerous innovative solutions to brain-related
diseases including Alzheimer's. However, there are still a few standpoints in terms of …

The development that quantum computing technologies are achieving is beginning to attract
the interest of companies that could potentially be users of quantum software. Thus, it is …

We propose to use wave function overlaps obtained from a quantum computer as inputs for
the classical split-amplitude techniques, tailored and externally corrected coupled cluster, to …

This article aims to bring quantum computing to robotics. A quantum algorithm is developed
to minimize the distance traveled in warehouses and distribution centers where order …

Merging machine learning with the power of quantum computing holds great potential for
data-driven decision making and the development of powerful models for complex datasets …

Quantum machine learning (QML) has attracted significant research attention over the last
decade. Multiple models have been developed to demonstrate the practical applications of …