Quantum Kernel Ensemble Generation for Automated Feature Maps
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Solution Overview
Problem
The choice of initial feature map in quantum machine learning models can yield unique decision boundaries, requiring physics expertise and complicating feature space discovery, and existing methods lack efficient automation for generating feature spaces in quantum machine learning models.
Innovation Solution
A boosting procedure is employed to generate an ensemble of quantum kernel-based learners by iteratively selecting and modifying quantum kernels, exploring a range of feature maps and adjusting error weights to automate the feature space generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If quantum machine learning models use different initial feature maps, then unique decision boundaries are achieved, but the complexity of navigating feature space increases and requires physics expertise
Solution Approach 1:
The system performs self-service by automatically generating and evaluating quantum feature maps through quantum circuit execution, eliminating the need for manual physics-based feature engineering. The quantum computer autonomously explores feature spaces by executing parameterized circuits and measuring outcomes, thereby reducing reliance on expert physics knowledge while maintaining adaptability across different feature map configurations
Solution Approach 2:
The system applies parameter changes by varying circuit parameters such as rotation angles and gate configurations to generate diverse feature maps. By systematically modifying these parameters in quantum circuits, the system explores different feature spaces and generates unique decision boundaries without requiring deep physics expertise, thus resolving the contradiction between adaptability and complexity
2Power
If quantum computers are used to leverage data processing advantages, then high-dimensional quantum Hilbert space is exploited, but automated development of quantum kernels is lacking
Solution Approach 1:
The system implements feedback by measuring quantum circuit outcomes and using these results to guide subsequent feature map generation and kernel development. The measured data from quantum executions feeds back into the process of selecting and refining quantum kernels, creating an automated loop that leverages the full power of quantum data processing while systematically developing kernels without manual intervention
Solution Approach 2:
The system introduces an intermediary classical computing layer that bridges quantum data processing power and automated kernel development. This intermediary layer processes quantum measurement outcomes, evaluates feature map performance, and automatically selects optimal quantum kernels, thereby translating raw quantum computational power into automated kernel development capabilities
3Measurement precision
If feature space discovery requires physics expertise, then navigation of complex architectures is possible, but the ease of operation decreases
Solution Approach 1:
The quantum computing system performs self-service by autonomously exploring feature spaces through automated circuit execution and measurement. The system independently evaluates different feature maps and identifies optimal configurations without requiring external physics expertise, thereby maintaining measurement precision while dramatically improving ease of operation
Solution Approach 2:
The system replaces manual physics-based feature engineering with automated quantum circuit-based feature map generation. By substituting the mechanical process of expert-driven feature selection with automated quantum computation and measurement, the system achieves both precise feature space navigation and operational simplicity
Data Source
AI summary
Techniques regarding generating an ensemble of quantum kernel-based learners for one or more quantum machine learning models 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, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an ensemble component that can generate an ensemble of quantum kernel-based learners by selecting a quantum kernel at multiple iterations of a boosting procedure that analyzes a range of feature maps employable by a quantum machine learning model.