Quantum Solver for Multi-Objective Function Optimization
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Solution Overview
Problem
Current classical computing methods are inefficient in solving complex multi-objective function problems, particularly those with conflicting objectives and binary decision variables, which are common in various industries.
Innovation Solution
A quantum solver system that initializes circuit parameters for an ansatz quantum circuit, measures bitstrings from the quantum circuit's state, determines Pareto-efficient elements, calculates the hypervolume, and updates circuit parameters to increase the hypervolume indices, thereby generating Pareto fronts more efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If classical computing methods are used to solve multi-objective function problems, then the problems can be solved, but the efficiency is low and the computational workload is high
Solution Approach 1:
The patent replaces classical computing systems with a quantum computing system to solve multi-objective optimization problems. The quantum computer executes quantum circuits that leverage quantum mechanical properties (superposition, entanglement, interference) to explore the solution space more efficiently than classical mechanical computing methods, thereby improving productivity and reducing computation time.
Solution Approach 2:
The patent changes the fundamental computational parameters by transitioning from classical bits to quantum bits (qubits). This parameter change enables the system to represent and process multiple potential solutions simultaneously through quantum superposition, and to evaluate multiple objectives concurrently, significantly enhancing optimization efficiency and reducing the time required to find Pareto-optimal solutions.
2Measurement precision
If the quantum processor runs more cycles to generate accurate Pareto fronts, then the solution accuracy improves, but the workload increases
Solution Approach 1:
The patent implements a feedback mechanism where the quantum processor generates candidate solutions, the classical processor evaluates their Pareto optimality and calculates hypervolume metrics, and then feeds this information back to guide subsequent quantum circuit executions. This feedback loop allows the system to focus computational resources on promising regions of the solution space, achieving accurate Pareto fronts with fewer quantum processor cycles and reduced workload.
Data Source
AI summary
Systems and techniques that facilitate multi-objective function optimization are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise an initialization component that initializes circuit parameters for an ansatz quantum circuit in a quantum computer; a measurement component that measures a plurality of bitstrings from a state of the quantum circuit; and an optimization component that determines a subset of bitstrings comprising feasible Pareto-efficient elements of the plurality of bitstrings and a hypervolume based on the subset of bitstrings and updates the circuit parameters to increase indices of the hypervolume.