Quantum Computing and Many-Particle Problems

Jincheng Zhang

Particle Field Quantum Computation Optimization (PFQC) is a novel algorithm designed to automatically optimize the parameters of particle field quantum computations, aiming to enhance both computational efficiency and accuracy. This research introduces a unique approach to problem formulation and implementation, leveraging the inherent properties of quantum mechanics to directly address optimization challenges within the context of quantum computing. The core mechanism centers around a dynamic mapping of optimization problems onto the quantum system, employing iterative adjustments to quantum state to achieve optimal results. This paper details the algorithm's design, its theoretical underpinnings, and preliminary experimental results demonstrating its potential to significantly improve quantum computation.

Fauseweh, Benedikt

Hanna, Isaac

Quantum mechanics arose out of the question "Is light a particle or a wave?" and has laid forth a model of reality in which particles are modeled by wave functions. The particle is in a superposition of states and can be entangled with other particles to create more complex systems. Observation of the system collapses the wave function to a single point. By using quantum gates, we can manipulate these particles to create algorithms to solve computational problems. Quantum computing does not collapse the complexity hierarchy by providing an across the board exponential speedup but can provide such a speedup for certain problems using algorithms that are specifically tailored to the nature of the problem, most notable Shor’s algorithm for integer factorization. Presented at the National Collegiate Research Conference at Harvard University, January 2023.

Adam G. D’Souza

Bala Krushna Tripathy, Apoorv Singh

There is a strong consensus that quantum computers are likely to solve previously intractable problems, especially in data science and AI. Nowadays, processing power of machine learning algorithms is limited by that of conventional computers. Quantum computing (QC) is one of the digital trends with the quickest rate of growth, and it seems to provide an answer to large data problems. It is a combination of the fields of computer science, information theory, and quantum physics. Quantum computers use the ability of subatomic particles to exist in more than one state at once to solve complex problems. In spite of its recent origin, quantum computing is already being used in the field of data analytics. QC can process huge datasets at much quicker rates and can provide data to AI models. By comparing schemas to swiftly assess and comprehend the link between two counterparts, QC can also aid in the integration of data. The ability to perform more sophisticated analysis and build machine learning models is a benefit of employing quantum computers.

Philip L. Roe