Evolution and Applications of Quantum Computing

Siddharth Kote, Dnyaneshwar Kshirsagar, Raj B. Kulkarni +3 more

Jincheng Zhang

This paper explores the application of self-adaptive chaos models to quantum computation evolution, aiming to provide precise simulations of complex systems and dynamic geometric transformations. Traditional computational methods often struggle with the intricacies of dynamic systems, and this integrated approach offers a novel pathway to enhanced simulation fidelity and evolution. We propose a framework utilizing quantum computation's inherent properties to achieve accurate, dynamic geometric modifications to system evolution. The core mechanism involves the iterative application of self-adaptive chaos dynamics, generating subtle geometric shifts that influence the system's trajectory. The resulting simulations demonstrate improved accuracy and robustness compared to traditional methods, particularly in scenarios involving complex, nonlinear dynamics. This research presents a fundamentally new method for quantum computation evolution, leveraging the principles of self-adaptive chaos to achieve unprecedented precision and flexibility.

Mikio Nakahara

Tao Yue, Yuechen Li, Man Zhang

Quantum software engineering (QSE) requires understanding application requirements, quantum programs and algorithms, quantum computation paradigms, executions, and deployment. However, relevant quantum computing (QC) concepts are often defined across physical, mathematical, and paradigm-specific levels of abstraction, making it difficult for the QSE community and hence proposed methods to refer to them consistently. To this end, we present QSoftCore, a conceptual model that reorganizes established QC concepts from a software engineering perspective and connects quantum applications, computational problems, algorithms, configurations, programs, and paradigm-specific computation specifications with state evolution, execution results, and backends. Our assessment against QSE literature across key software engineering activities indicates that QSoftCore provides shared QC referents for quantum computations, executions, deployment configurations, backends, and measurement evidence across the QSE lifecycle.

Shigeo Sato, Mitsunaga Kinjo, Koji Nakajima

A quantum computer employing a single quantum as a qubit executes real parallel computation and has various applications. Several algorithms have been proposed for quantum computation. However, these algorithms are applicable only to a limited number of applications. Therefore, a general purpose algorithm should be studied and developed for practical use in the near future. In this paper, we focus on the adiabatic evolution algorithm for general purpose quantum computation and discuss how to use this algorithm for solving an optimization problem. We show a new algorithm incorporating an artificial neural network (ANN)-like method in order to compose another Hamiltonian. The new algorithm is helpful for reducing computation cost and is easy to implement. Successful simulation results are shown.

Daowen Qiu