Quantum Computing: A Way to Break Complexity?

Shanmugam Jagan, S. B. G. Tilak Babu, S Nagakishore Bhavanam +1 more

The end of this work is to explore the implicit operation of amount computing ways to reactive molecular dynamics simulations. Enhancing the perfection and effectiveness of simulations in the process of understanding molecular relations and responses is possible because of computing's capacity to handle complex amount circumstances. With a thorough analysis of the current literature and ways, this study clarifies the possible uses of amount computing in reactive molecular dynamics simulations. It demonstrates how quantum computing may be used to break computationally demanding problems including determining response pathways, describing transition countries, and making extraordinarily accurate prognostications about response dynamics. Also, by examining the advantages and disadvantages of applying amount computing ways in this specific field, the study provides perceptivity into possible unborn directions for exploration and development.

Aida Todri‐Sanial

Mehdi Ghayoumi

Quantum computing combines ideas from quantum mechanics and computer science, and it could change deep learning. By using the special abilities of quantum systems, we might soon be able to handle and analyze data in ways that regular computers can’t. This chapter will look at how quantum computing can make deep learning better, such as speeding up the learning process, improving complex models, and even solving problems that were once impossible. We will focus on important quantum algorithms, like Shor’s algorithm, which can break encryption, and Grover’s algorithm, which can search through large databases quickly. We will also discuss how these algorithms can help deep learning by providing new methods to optimize and manage data.

Susan M. Reiss

Paul M. B. Vitanyi

Yunong Shi, Pranav Gokhale, Prakash Murali +11 more

Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing (QC) applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity. In this review, we point out that greater efficiency of QC systems can be achieved by breaking the abstractions between these layers. We review several works along this line, including two hardware-aware compilation optimizations that break the quantum instruction set architecture (ISA) abstraction and two error-correction/information-processing schemes that break the qubit abstraction. Last, we discuss several possible future directions.