Hybrid Classical-Quantum Decision Tree Ensembles for Large-Scale Retraining
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Solution Overview
Problem
Existing machine learning models struggle with retraining on large datasets due to memory constraints and computational complexity, leading to decreased accuracy when using techniques like sampling or incremental batch learning.
Innovation Solution
A hybrid classical-quantum algorithm for constructing and retraining decision tree ensembles, utilizing quantum-supervised clustering to efficiently process large datasets with polylogarithmic complexity, supporting both numerical and categorical features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sampling or incremental batch learning is used to handle large datasets, then memory constraints are relieved and computational complexity is reduced, but accuracy decreases
Solution Approach 1:
The patent segments the large dataset into manageable quantum states that can be processed in parallel, allowing the system to handle large datasets without loading everything into classical memory while maintaining accuracy through quantum superposition processing
Solution Approach 2:
The patent introduces quantum computers as an intermediary between the large dataset and the decision tree training process, using quantum random sampling with replacement to efficiently select training examples without sacrificing the statistical properties needed for accurate model training
2Measurement precision
If all data is loaded into memory for retraining, then accuracy is maximized, but memory constraints are violated and computational complexity increases
Solution Approach 1:
The patent transitions from classical memory storage to quantum state representation, moving the data into a different dimensional space where large datasets can be represented and processed with polylogarithmic memory complexity while preserving the ability to compute accurate models
Solution Approach 2:
The patent creates quantum copies of the dataset that can be processed through quantum random sampling, allowing multiple training iterations on large datasets without requiring proportional increases in classical memory resources
3Adaptability or versatility
If retraining is performed frequently with large datasets, then model adaptability improves, but computational time increases rapidly
Solution Approach 1:
The patent enables periodic retraining of decision tree ensembles by implementing an iterative process where quantum random sampling efficiently processes new data batches, allowing frequent model updates with large datasets without proportional increases in computational time
Solution Approach 2:
The patent changes the computational parameters by using quantum computing resources to achieve polylogarithmic complexity scaling, fundamentally altering the time complexity characteristics of retraining operations to enable frequent updates with large datasets
Data Source
AI summary
Systems and methods for construction and retraining of decision trees ensemble using hybrid classical-quantum algorithms are disclosed. A method may include a classical computer program: receiving a dataset; calculating feature-weights for original examples using a feature-weight calculation method; updating the feature-weights for the original examples with feature-weights for the original examples and the new examples; loading the feature-weights for the dataset and new data into a first quantum-accessible data structure and loading overwritten values into a second quantum-accessible data structure; instructing a quantum computer to query quantum states for the first and second quantum-accessible data structures using random sampling with replacement, to execute quantum-supervised clustering with the quantum states and the feature-weights, to grow a depth for the tree, and to calculate labels for a regression task and/or a classification task; and receiving the labels from the quantum computer.