Abstract Machine learning has become a ubiquitous and effective technique for data
processing and classification. Furthermore, due to the superiority and progress of quantum …

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 …

Neural networks enjoy widespread success in both research and industry and, with the
advent of quantum technology, it is a crucial challenge to design quantum neural networks …

Sensory memory, formed at the beginning while perceiving and interacting with the
environment, is considered a primary source of intelligence. Transferring such biological …

Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …

Deep learning achieves unprecedented success involves many fields, whereas the high
requirement of memory and time efficiency tolerance have been the intractable challenges …

The accumulation of noise in quantum computers is the dominant issue stymieing the push
of quantum algorithms beyond their classical counterparts. We do not expect to be able to …

Entangled states are an important resource for quantum computation, communication,
metrology, and the simulation of many-body systems. However, noise limits the experimental …

Neural networks have achieved impressive breakthroughs in both industry and academia.
How to effectively develop neural networks on quantum computing devices is a challenging …

Quantum computing has been proven to excel in factorization issues and unordered search
problems due to its capability of quantum parallelism. This unique feature allows …