Hybrid Quantum Neural Network Architecture for Diverse Classification
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Solution Overview
Problem
Conventional classification systems using classical neural networks lack diversity and explainability, failing to address all features of large data sets and provide real-time output diversity.
Innovation Solution
Incorporation of quantum network layers into neural network architectures, adjusting model architecture by adding quantum layers optimized for layer depth, accuracy, and diversity, and evaluating multiple hybrid architectures for optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If quantum network layers are incorporated into neural network architectures, then diversity and explainability of classification outputs are improved, but device complexity increases
Solution Approach 1:
The patent divides the neural network into separate quantum and classical components, with quantum network layers handling specific feature transformations and classical network layers handling other processing tasks. This segmentation allows each component to specialize in what it does best while maintaining overall system manageability through modular architecture.
Solution Approach 2:
The patent introduces quantum network layers as intermediary components between classical neural network layers. These quantum layers act as mediators that transform classical data representations into quantum states for processing, then convert results back to classical representations, enabling quantum-enhanced classification without requiring a complete quantum system.
2Measurement precision
If multiple hybrid quantum-classical architectures are evaluated and selected, then model accuracy and diversity are improved, but training time and computational resources increase
Solution Approach 1:
The patent evaluates multiple hybrid architectures with varying degrees of quantum layer integration, training only the necessary number of models to achieve sufficient diversity and accuracy. Rather than exhaustively testing all possible configurations, the system selects an appropriate subset that provides adequate performance improvement without excessive time investment.
Solution Approach 2:
The patent systematically varies parameters such as the number of quantum layers, their positions in the network, and their configuration settings across different hybrid architectures. By changing these parameters across multiple candidate models, the system explores the design space efficiently to identify optimal configurations that balance accuracy, diversity, and training time.
Data Source
AI summary
Providing a hybrid neural network architecture by training a plurality of models using a set of training data, the plurality comprising quantum models and classical models, evaluating each model using a common test data set, assigning one or more evaluation metrics to each model according to the evaluation, generating a plurality of networks, each network comprising a combination of the models, evaluating a flow of each network, selecting a network according to the flow, and providing the selected network to a user.