Classical information support for optimal control of dynamic processes is a set of methods, models, and computational tools that enable the collection, coordination, evaluation, forecasting, and interpretation of data on the state of an object in order to formulate an acceptable and optimal control action under conditions of uncertainty, constraints, and multicriteria. However, in today’s environment of sensor saturation and digitalization of technological processes, fundamental difficulties arise associated with the exponential growth of the computational complexity of algorithms for synthesizing optimal solutions as the dimensionality of the state space increases, environmental uncertainty increases, and a priori information on the current state of multidimensional processes/control objects is incomplete/noisy. The aim of this work is to propose a concept for information support for optimal decision-making in the control of dynamic processes in high-dimensional problems, based on the rejection of the deterministic calculation of a single optimal solution. Instead, a hybrid information space is formed where neural network operators perform continuous dimensionality reduction and fast feedback approximation, and quantum processors are used as specialized accelerators for two NP-hard subproblems. An example of implementing a dynamic neural network for approximate solutions at the second stage of the hybrid information system’s operational sequence is presented. A procedure for quadratic discrete quantum optimization without constraints based on adiabatic quantum annealing is described, applicable at the final stage of the search for an optimal solution.
Classical information support for optimal control of dynamic processes is a set of methods, models, and computational tools that enable the collection, coordination, evaluation, forecasting, and interpretation of data on the state of an object in order to formulate an acceptable and optimal control action under conditions of uncertainty, constraints, and multicriteria. However, in today’s environment of sensor saturation and digitalization of technological processes, fundamental difficulties arise associated with the exponential growth of the computational complexity of algorithms for synthesizing optimal solutions as the dimensionality of the state space increases, environmental uncertainty increases, and a priori information on the current state of multidimensional processes/control objects is incomplete/noisy. The aim of this work is to propose a concept for information support for optimal decision-making in the control of dynamic processes in high-dimensional problems, based on the rejection of the deterministic calculation of a single optimal solution. Instead, a hybrid information space is formed where neural network operators perform continuous dimensionality reduction and fast feedback approximation, and quantum processors are used as specialized accelerators for two NP-hard subproblems. An example of implementing a dynamic neural network for approximate solutions at the second stage of the hybrid information system’s operational sequence is presented. A procedure for quadratic discrete quantum optimization without constraints based on adiabatic quantum annealing is described, applicable at the final stage of the search for an optimal solution.
Adaptive constraint optimization is a critical challenge in various fields, particularly in areas like engineering design, logistics, and finance, where finding optimal solutions within complex, high-dimensional spaces is essential. Traditional methods often struggle with such problems, particularly when the solution space is intricate and the constraints are non-trivial. This paper proposes a novel algorithm combining quantum annealing and a neural network to address this challenge. We introduce a hybrid approach where a neural network dynamically adjusts constraint parameters during the optimization process, leveraging the quantum annealing process to efficiently explore the solution space. This hybrid method promises significant speed and accuracy improvements over existing techniques, offering a promising pathway towards more efficient and robust optimization solutions. The core mechanism focuses on leveraging quantum annealing's ability to efficiently search sparse solution spaces, coupled with the neural network's capacity to learn and adapt to the problem's characteristics. The paper details the algorithm's design, its performance evaluation, and potential applications.
The rapidly increasing number of heterogeneous data ecosystems, such as structured databases, semi-structured data lakes, real-time streams, distributed knowledge graphs, has further complicated the issue of query planning and optimization in modern information systems extensively. The optimization of queries is essentially an NP-hard combinatorial problem especially when based on multi-join queries, distributed execution plan, dynamic cost model, and uncertainty in the data access patterns. Even decades of concept and engineering advances, classical database optimizers are rapidly becoming unable to offer a globally optimal execution plan when faced with high-dimensional data, high data volatility, with a very tight latency constraint. Good news Recent breakthroughs in quantum computing propose promising computational paradigms that can be used to solve combinatorial optimization problems via quantum parallelism, superposition, and entanglement. Nevertheless, even the near-term quantum hardware is limited in the number of available qubits, noise, and decoherence, making it impractical to achieve fully quantum database systems in any perceived future. This is also the reason why quantum-classical hybrid algorithms are being developed: designed to add quantum optimization subroutines to classical database architectures is guaranteed without breaking their compatibility with existing execution engines. The following paper presents a framework of scalable quantum-classical hybrid computing of complex query planning in heterogeneous data ecosystems. The framework breaks down query optimization into classical and quantum-amenable optimization, which allows NP-hard subproblems, such as join ordering, operator placement, and cost minimization, to be transformed into quantum optimization models such as Quadratic Unconstrained Binary Optimization (QUBO) and Ising Hamiltonians. Classical elements handle query parsing, semantic analysis, constraint verification, and orchestration, whereas quantum processors are called selectively to solve combinatorial decision-making tasks. The suggested architecture will consist of a layered execution, hybrid execution, cost-model encoding approach, and adaptive fallback mechanisms to make them robust to quantum hardware variability. This has been experimentally tested with simulated quantum annealing and variational quantum algorithms that exhibit a quantifiable improvement in the quality and scalability of optimization and heuristics based on entirely classical algorithms, especially in high-dimensional query plans. The findings confirm the practicability of quantum query optimization and put down a feasible route to implement quantum algorithms in the serving machine of the next-generation data management systems.
Wireless communication systems face increasing challenges in resource allocation, latency reduction, scalability, and adaptation because modern networks generate high-dimensional, nonlinear, and rapidly changing data. This work presents a hybrid Quantum Neural Network (QNN)-driven framework for intelligent resource optimization in dynamic wireless communication environments. The proposed framework integrates variational quantum circuits with classical optimization techniques to support adaptive learning and efficient decision-making. A modular architecture consisting of User, Database, and Operator modules enables secure data acquisition, storage, preprocessing, continuous model training, and operator-driven optimization. Quantum Neural Networks are employed as the primary learning mechanism, while hybrid quantum-classical optimization supports stable parameter training. Quantum annealing is incorporated for complex tasks such as channel allocation, bandwidth management, and resource scheduling. The framework is designed to improve allocation accuracy, reduce latency, enhance scalability, reliability, security, and resource utilization. Continuous learning further enables adaptation to changing traffic patterns, user requirements, workloads, and network conditions. Keywords: Quantum Neural Network, Wireless Communication, Resource Optimization, Quantum Computing, Hybrid Quantum-Classical Learning, Resource Allocation.
A systematic review has been conducted on the prerequisites for applying quantum computing in high-precision control systems, taking into account the limitations of traditional architectures associated with computational complexity, latency, and limited adaptability in real-time scenarios. Classes of problems for which the implementation of quantum computing solutions is potentially appropriate have been identified, including trajectory optimization, state prediction, and dynamic stabilization. Leading architectures of quantum computing devices have been considered in terms of their compatibility with the requirements of control systems. The algorithmic foundations relevant to optimization and decision-making tasks have been analyzed, in particular Grover’s algorithm, the Quantum Approximate Optimization Algorithm (QAOA), the Variational Quantum Eigensolver (VQE), and quantum linear algebra algorithms. Key characteristics of quantum platforms have been highlighted—such as quantum advantage, decoherence, noise sensitivity, and qubit count limitations—as critical factors for practical implementation. Technical requirements for computational cores to be considered in the design of hybrid architectures have been summarized. Examples of the use of predictive and adaptive control incorporating quantum accelerators are provided. Models of coexistence between classical and quantum components in online control systems are examined. The potential of quantum algorithms for modeling and control tasks has been outlined. A generalized concept of a hybrid control system incorporating a classical decision-making module and a quantum optimizer has been developed. An analytical comparison of the performance of classical and quantum approaches is presented using standard control tasks, based on metrics such as speed, accuracy, and noise robustness. An architectural solution involving a quantum optimizer for managing a multi-agent system with incomplete information about the environment is proposed. A conditionally formal description of a hybrid control algorithm with a quantum component in the optimization loop is provided.
This paper investigates the application of quantum-inspired neural networks to constraint optimization, aiming to address the limitations of traditional optimization methods when faced with complex, high-dimensional problems. We propose a novel hybrid approach that integrates quantum-inspired neural networks into the optimization process, leveraging their ability to capture subtle patterns and relationships within the problem space. The goal is to significantly improve the performance of constraint optimization, particularly in scenarios where conventional methods fall short. The core mechanism focuses on utilizing the neural network to generate a surrogate model that facilitates faster and more efficient search, enabling the solution of challenging problems. The results demonstrate substantial improvements in optimization speed and accuracy compared to existing methods, especially when dealing with intricate constraint formulations.
This paper has a full-scale reference architecture for hybrid quantum-classical learning to include the integration of classical representation models with quantum neural networks (QNN), quantum support vector machines (QSVM), quantum principal component analysis (qPCA), variational quantum circuits (VQC), and QAOA-based combinatorial optimization to create next-generation predictive business intelligence and complex decision optimization subject to all real world operating constraints. The proposed structure has a layered system consisting of; (1) Quantum aware data encoding techniques through Amplitude, Basis and Angle Mapping; (2) Differentiable Hybrid Learning Pipelines optimized using Parameter Shift Gradient Methods across Simulators and Managed Quantum Processing Units; (3) Prescriptive Workflows that reformulate Multi-Objective Business Problems such as Supply Chain Coordination, Routing with Time Windows and Portfolio Construction into QUBO Representations Supported by Convex Warm Starts and Classical Post-Processing to Guarantee Feasibility and Explainability. An End-To-End Orchestration Layer will manage Circuit Templates, Data Hashes, Model Versions and Backend Execution Metadata to Provide Reproducibility, Traceability, Governance Compliance Across All Enterprise Pipelines. A Systematic Evaluation Protocol Will be Defined to Isolate Quantum Contribution Through Systematic Ablation Analysis of Classical-Only, Quantum-Only, and Hybrid Configurations Combined With Rolling Window Validation, Latency Profiling, Cost Efficiency Analysis, and Carbon Impact Accounting. Industry Standard Time Series and Probabilistic Accuracy Measures Including sMAPE, MASE, CRPS and Calibration Consistency Will Be Used to Evaluate the Performance of Predictive Models. Optimization Quality Will Be Evaluated Using Optimality Gap, Constraint Violation Rates, Time to Feasible Solution and Downstream Business Impact on Profit Margins and Service Levels. An Interpretability Layer Will Provide Transparency by Mapping Quantum Feature Spaces, Circuit Observables, and Kernel Alignment Structures to Domain-Specific Reasoning to Build Stakeholder Trust, Auditability and Regulatory Alignment. In Addition, The Framework Will Incorporate Adaptive Decision-Making Via Quantum Reinforcement Learning to Allow Continuous Optimization Under Dynamic and Uncertain Business Environments. An Energy Aware Execution Layer Will Dynamically Route Workloads Between Classical Simulators and Quantum Hardware According to Predefined Energy Budgets, Carbon Thresholds, and Performance Demands. The Overall Framework Provides a Scalable, Auditable and Ethically Aligned Blueprint for Deploying Hybrid Quantum-Classical Intelligence Within Existing Enterprise Ecosystems for Applications Such as Financial Forecasting, Retail Analytics, Dynamic Pricing, Market Risk Modeling, and Supply Chain Optimization.