Quantum Annealing for Network Asset Distribution Optimization
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Solution Overview
Problem
Existing prediction systems, such as machine learning systems, face challenges in determining an optimum distribution of network assets for wireless communication services, especially when managing a large number of assets, as classical optimization approaches are impractical for a large number of variables and may take too long or be unachievable.
Innovation Solution
A quantum annealing process is used to find a global minimum for a Hamiltonian representation of inducement bundles, optimizing the distribution of network assets by treating them as a portfolio to maximize return on investment under cost constraints, employing a quantum annealer with a special purpose integrated circuit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If classical optimization approaches are used to determine the optimum distribution of network assets, then the method is simple and easy to implement, but the calculation time becomes impractically long or unachievable when managing a large number of assets
Solution Approach 1:
The patent replaces classical mechanical optimization algorithms with a quantum annealing system that uses quantum mechanical phenomena (quantum tunneling, superposition) to solve optimization problems. The quantum annealer formulates the network asset distribution problem as a Hamiltonian minimization problem, where quantum fluctuations enable the system to escape local minima and find global optimum solutions much faster than classical methods for large-scale problems.
Solution Approach 2:
The patent changes the fundamental parameter of optimization from classical iterative algorithms to quantum annealing parameters (Hamiltonian formulation, quantum fluctuation strength). By transforming the optimization problem into a quantum mechanical problem with specific Hamiltonian parameters, the system achieves exponential speedup in solving large-scale optimization problems compared to classical approaches.
2Productivity
If quantum annealing is used to find the global minimum of the Hamiltonian representation, then the calculation becomes practical and efficient for large numbers of network assets, but the system complexity increases
Solution Approach 1:
The patent introduces a quantum annealer as an intermediary system between the network management problem and the optimization solution. The quantum annealer serves as a specialized device that translates network asset distribution problems into quantum mechanical Hamiltonian problems, processes them using quantum annealing, and returns optimized solutions. This intermediary approach encapsulates the complexity in a dedicated hardware system while keeping the overall network management architecture relatively simple.
3Measurement precision
If network assets are treated as a portfolio to maximize return on investment, then the optimization becomes more precise and targeted, but the formulation and implementation become more complex
Solution Approach 1:
The patent changes the optimization formulation by introducing financial portfolio theory parameters (return on investment, cost constraints) into the network asset distribution problem. The Hamiltonian is formulated to represent the portfolio optimization problem, where quantum annealing minimizes the energy function corresponding to the portfolio return. This parameter transformation enables precise optimization of network assets as investment portfolios, achieving targeted allocation that maximizes ROI while managing complexity through the quantum annealing framework.
Data Source
AI summary
A device may include a processor configured to select a plurality of customers; select a plurality of network assets; and generate a Hamiltonian function representation of optimizing the plurality of network assets with respect to the plurality of customers. The processor may be further configured to determine a global minimum for the Hamiltonian function representation using a quantum annealer; select a distribution of the plurality of network assets based on the determined global minimum of the Hamiltonian function representation; and apply the selected distribution of the plurality of network assets to improve network performance.