Quantum Circuit Selection Optimization for Feature Generation
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Solution Overview
Problem
The optimization of quantum circuit selection for quantum-enhanced feature generation in machine learning is hindered by high costs and constraints of real quantum hardware, as well as the need for efficient use of resources, where not all quantum circuits are suitable or achievable due to physical or engineering limitations, and heuristic approaches may not apply universally across problem types.
Innovation Solution
A system comprising a processor that executes computer-executable components for selecting and optimizing quantum circuits, assessing their performance, and iteratively selecting new circuits to map classical features to a high-dimensional quantum feature space, using a combination of classical and quantum computing components to optimize resource usage and improve model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real quantum hardware is used for quantum circuit execution, then quantum-enhanced feature generation can be achieved, but high costs and hardware constraints limit resource availability and accessibility
Solution Approach 1:
The patent creates virtual copies of quantum hardware through simulated quantum computers that replicate quantum circuit execution behavior. These virtual quantum systems allow multiple users and applications to access quantum computing capabilities without requiring physical quantum hardware, thereby reducing hardware constraints and improving accessibility while maintaining the core functionality of quantum-enhanced feature generation.
Solution Approach 2:
The patent introduces a cloud-based quantum computing platform as an intermediary between users and physical quantum hardware. This intermediary layer provides virtual quantum computers that mediate access to quantum resources, managing hardware allocation and providing quantum circuit execution capabilities to users who would otherwise face hardware accessibility barriers.
2Measurement precision
If more quantum circuits are tested to find optimal circuits, then feature generation accuracy improves, but resource consumption and execution time increase
Solution Approach 1:
The patent performs preliminary actions by pre-selecting and pre-testing quantum circuits using virtual quantum computers before deploying them to real hardware. This preliminary filtering process identifies high-performing circuits that are then prioritized for execution on physical quantum systems, reducing the number of circuits that need to be tested on expensive hardware while maintaining feature generation accuracy.
Solution Approach 2:
The patent applies partial action by testing a large number of quantum circuits in virtual environments to identify the most promising candidates, then performing excessive action by executing selected circuits multiple times on real quantum hardware to gather robust performance data. This two-stage approach balances comprehensive circuit exploration with efficient resource utilization.
3Measurement precision
If quantum circuits are optimized for specific problem types, then performance improves, but adaptability to different problem types decreases
Solution Approach 1:
The patent develops universal quantum circuit templates and feature generation pipelines that can be adapted to multiple problem types including classification, regression, and clustering tasks. The virtual quantum computing platform provides a unified interface that allows the same quantum circuit infrastructure to serve different machine learning applications, maintaining high performance across diverse problem types while preserving adaptability.
Data Source
AI summary
Systems, computer-implemented methods, and computer program products to facilitate optimization of quantum-enhanced feature generation are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a selection component that selects a quantum circuit for mapping a set of classical features to a quantum feature space. The computer executable components further comprise an execution component that provides the quantum circuit for execution by a quantum computer or a quantum simulator to map the set of classical features and to produce quantum-enhanced features. The computer executable components further comprise a training component that assesses selection of quantum circuits and signals the selection component to select a new quantum circuit based on the assessment.