Discovery of the Quantum

Jagdish Mehra, Helmut Rechenberg

Kurt Gottfried

Dirac’s contributions to the discovery of nonrelativistic quantum mechanics and quantum electrodynamics, prior to his discovery of the relativistic wave equation, are described.

Patrick A. Heelan

Yashraj Mukherjee, Dr. Praveen Pathak, Shweta Sinha

Discovery of drugs is one the time consuming and resource intensive process in the pharmaceutical industry since it takes years of research and investments. Discovery of new drugs is crucial for preserving human health and eradication of diseases. Modern drug discovery processes like CADD relies on Traditional Computing which is constrained by its raw computing power because it struggles to simulate complex interactions between biomolecules. Therefore, pharmaceutical industry can be one of the largest beneficiaries of quantum technology pushing boundaries of innovation. Quantum Computing can accelerate the drug discovery process and execute them within few minutes to hours which would require millions of years on today’s fastest supercomputer. This paper explains Quantum Computing basics, principles of quantum computing, algorithms used in quantum computing like Shor, Grover’s etc., Process of traditional drug discovery, limitations of CADD, Quantum Simulations, Virtual Screening, Molecular Docking and how Quantum Technology speeds up drug discovery process. It also addresses challenges regarding quantum technologies such as regulatory compliances, hardware limitations, cost & accessibility etc. and provides recent breakthroughs that demonstrates the application of quantum computing in drug discovery. Note that some very important terminologies related to quantum computing which are not mentioned in detail are mentioned in brief in our glossary section.

Amit Goswami

Jincheng Zhang

This paper introduces DQP, a novel dynamic quantum path discovery algorithm designed to enhance the efficiency and robustness of quantum computation. Traditional quantum path discovery methods often rely on pre-defined path parameters, limiting the algorithm's adaptability to complex and varying input data. DQP overcomes this limitation by dynamically adjusting path parameters during the search process, allowing for optimized solutions across a wider range of quantum systems. The core mechanism centers around a feedback loop that iteratively refines path parameters based on observed error metrics, ultimately leading to more efficient and reliable quantum computations.