Feedback-Based Quantum Optimization for Discrete Problems
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Solution Overview
Problem
Classical computers face challenges in solving discrete optimization problems efficiently, particularly as the number of variable parameters increases, often becoming trapped in local minima rather than finding the global minimum.
Innovation Solution
A quantum computing system is configured with a quantum device and a classical device, where the quantum device iteratively evaluates a quantum function representing a cost function of a discrete optimization problem, and the classical device provides control signals to optimize the quantum device's parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a quantum computing system is used to solve discrete optimization problems, then solution quality and ability to avoid local minima is improved, but device complexity increases
Solution Approach 1:
The system is divided into two distinct components: a quantum computing device that evaluates the quantum function and a classical computing device that optimizes parameters and provides control signals. This segmentation allows each device to perform its specialized function while working together to solve the optimization problem, thereby improving solution quality without requiring a single complex device to handle all tasks.
Solution Approach 2:
The patent introduces a hybrid classical-quantum architecture where the classical computing device acts as an intermediary between the problem definition and the quantum computing device. The classical device prepares the optimization problem, optimizes parameters based on measurement results, and generates control signals for the quantum device, enabling the quantum system to achieve high solution quality while managing complexity through coordinated interaction.
2Adaptability or versatility
If the number of variable parameters in a discrete optimization problem increases, then the problem becomes more complex to solve, but classical computers become intractable
Solution Approach 1:
The patent replaces the purely classical computational mechanism with a hybrid quantum-classical system. The quantum computing device leverages quantum mechanical effects (superposition, entanglement, interference) to evaluate the quantum function representing the cost function, enabling the system to handle problems with many variable parameters more efficiently than classical computers alone, thus maintaining productivity as problem complexity increases.
Solution Approach 2:
The system dynamically optimizes parameters of the quantum algorithm through iterative feedback. The classical computing device adjusts parameters based on measurement results from the quantum device, allowing the system to adapt to problems with varying numbers of parameters and maintain solving efficiency across different problem scales.
Data Source
AI summary
A system for identifying approximate solutions to discrete optimization problems includes a quantum computing device. The quantum computing device iteratively configures a layered quantum circuit to evaluate a Hamiltonian representation of a cost function over a set of parameter values. After each iteration, the quantum computing device identifies a new parameter value based upon an estimate of an expectation value under the quantum state output by execution of the layered quantum circuit. The quantum computing device updates a configuration of the layered quantum circuit based upon the new parameter value. After a final iteration, the output quantum state of the layered quantum circuit is measured to identify an approximate solution vector. This solution vector is output as optimization results that are indicative of an approximate solution to the discrete optimization problem.