ВАРІАЦІЙНИЙ КВАНТОВИЙ АЛГОРИТМ РОЗВ’ЯЗКУ ЛІНІЙНИХ СИСТЕМ ДЛЯ СИСТЕМ ПІДТРИМКИ ПРИЙНЯТТЯ РІШЕНЬ

Abstract

A hybrid quantum–classical approach to solving the load balancing problem in real-time computer systems is proposed, with a focus on reliability assessment and control. The approach is based on the use of a Variational Quantum Linear Solver (VQLS) to process large sparse linear systems arising from the spatio-temporal discretization of load distribution models and task flows among computational nodes. An algorithmic scheme is presented for transforming an engineering model of a real-time system into a quantum-realizable problem formulation. Previously proposed quantum algorithms for solving linear systems of equations cannot be implemented in the near term due to the required circuit depth. This scheme includes decomposing the system matrix into a linear combination of Pauli operators, preparing a quantum state corresponding to the right-hand side, and optimizing a variational ansatz. It is shown that the resulting quantum solution state is most effective when used not for full reconstruction of the load vector, but for direct estimation of aggregated and local metrics, such as peak load, slack to deadline violations, and integral stability indicators, which are directly related to system failures and performance degradation. The proposed approach is considered as a tool for quantum benchmarking of classical load balancing algorithms, as well as a potential foundation for building reliability management systems for computer systems operating in a near–real-time regime.

Key words. Reliability of computer systems, Bayesian neural networks, ensemble methods, artificial intelligence, hybrid models, risk assessment, fault tolerance, uncertainty modeling, failure prediction, software security, system resilience, neural networks, fault trees, machine learning, quantum systems, quantum algorithms, quantum machine learning, hybrid models.