Classical Quantum Ensemble AI Model for Classification
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Solution Overview
Problem
Current quantum artificial intelligence models face limitations in scaling due to data loading time, the number of qubits, and circuit depth, making it challenging to effectively process large datasets and complex classification tasks.
Innovation Solution
A system and method that generates and computes probability scores using a classical and quantum ensemble artificial intelligence model, combining classical AI models with quantum AI models through boosting techniques and kernel methods to enhance processing capabilities and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum AI models are used to process large datasets, then computational performance and accuracy are improved, but data loading time and circuit depth increase
Solution Approach 1:
The system divides the AI model into separate classical and quantum components, where the classical model handles data loading and preprocessing while the quantum model performs classification. This segmentation allows each component to operate optimally without the bottleneck of loading large datasets into quantum memory.
Solution Approach 2:
A classical computer acts as an intermediary between data storage and the quantum computer. The classical system loads data, performs preliminary processing, and sends only essential features to the quantum model, thereby reducing the data loading time and computational burden on the quantum system.
2Productivity
If quantum AI models process large datasets, then computational performance is improved, but the number of qubits required increases
Solution Approach 1:
The system extracts the heavy computational burden of data loading and feature engineering from the quantum system and places it in the classical system. Only the essential classification computation remains in the quantum system with a reduced number of qubits.
Solution Approach 2:
Instead of loading entire large datasets into the quantum system, the system performs partial action by loading only processed features or using quantum algorithms that operate on compressed representations, thereby reducing the qubit requirement while maintaining processing efficiency.
3Measurement precision
If quantum AI models are used for classification tasks, then accuracy is improved, but circuit depth increases
Solution Approach 1:
The classification task is segmented into classical preprocessing steps (feature extraction, dimensionality reduction) and quantum classification steps. This reduces the circuit depth required for the quantum portion while maintaining overall accuracy.
Solution Approach 2:
The system performs preliminary action by preprocessing data in the classical system before it enters the quantum circuit. This includes feature selection, normalization, and transformation, which reduces the complexity and depth of the subsequent quantum circuit.
Data Source
AI summary
Systems, computer-implemented methods, and computer program products that can facilitate a classical and quantum ensemble artificial intelligence model are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an ensemble component that generates an ensemble artificial intelligence model comprising a classical artificial intelligence model and a quantum artificial intelligence model. The computer executable components can further comprise a score component that computes probability scores of a dataset based on the ensemble artificial intelligence model.