Hybrid Classical-Quantum Kernel Alignment for Classifier Training
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Solution Overview
Problem
Quantum circuit optimization becomes increasingly complex due to the exponential increase in matrix transformations with the number of qubits, making manual decomposition unmanageable, and existing methods struggle to efficiently train quantum classifiers and decision-making systems.
Innovation Solution
A hybrid classical-quantum computing system is employed for quantum feature kernel alignment, where a classical computer optimizes kernel alignment parameters and a quantum computer evaluates the quality of quantum kernels, co-evolving quantum and classical methodologies to improve the accuracy of machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If quantum circuit optimization is performed manually, then some level of optimization can be achieved, but the complexity becomes unmanageable due to exponential increase in matrix transformations with the number of qubits
Solution Approach 1:
The patent segments the quantum circuit optimization process into distinct components: feature map decomposition into sequential quantum gates, kernel matrix computation separated from classifier training, and iterative optimization steps. This segmentation makes the exponentially complex problem manageable by breaking it into smaller, programmatically处理的 parts that can be automated.
Solution Approach 2:
The patent introduces a classical computer as an intermediary between the quantum processor and the optimization objective. The classical computer computes kernel matrices from quantum measurements, performs kernel alignment calculations, and guides the optimization of quantum circuit parameters. This intermediary handles the computationally intensive classical processing, allowing the quantum processor to focus on generating quantum states and measurements.
2Productivity
If existing quantum classifier training methods are used, then classification can be performed, but training efficiency and accuracy are insufficient for intractable optimization problems
Solution Approach 1:
The patent implements a feedback loop where the classical computer computes kernel alignment between the quantum kernel matrix and the data, then uses this alignment metric to guide optimization of the quantum circuit parameters. This iterative feedback process continuously improves the quantum feature map to better capture the structure of the classification problem, enhancing both training efficiency and accuracy for difficult optimization problems.
Solution Approach 2:
The patent optimizes quantum classifier performance by systematically varying parameters of the quantum feature map circuit, including gate rotation angles and circuit depth. By changing these parameters and evaluating their impact on kernel alignment and classification accuracy, the system adapts the quantum circuit to the specific characteristics of the problem being solved, improving efficiency and reliability.
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AI summary
The illustrative embodiments provide a method, system, and computer program product for quantum feature kernel alignment using a hybrid classical-quantum computing system. An embodiment of a method for hybrid classical-quantum decision maker training includes receiving a training data set. In an embodiment, the method includes selecting, by a first processor, a sampling of objects from the training set, each object represented by at least one vector.In an embodiment, the method includes applying, by a quantum processor, a set of quantum feature maps to the selected objects, the set of quantum maps corresponding to a set of quantum kernels. In an embodiment, the method includes evaluating, by a quantum processor, a set of parameters for a quantum feature map circuit corresponding to at least one of the set of quantum feature maps.