Quantum Noise Bias Correction in Deep Learning
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Solution Overview
Problem
Deep learning systems face challenges in bias correction, particularly in machine learning algorithms where inherent bias in training data sets leads to inaccurate predictions, and existing techniques struggle to effectively address this issue.
Innovation Solution
A method utilizing a hybrid classical-quantum computing system that generates and tunes quantum noise to adjust the weights of neural networks, allowing for reclassification and sensitivity analysis to correct bias, leveraging the unique properties of quantum processors to introduce noise and improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum noise is introduced to correct bias in neural networks, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces quantum noise as an intermediary mechanism to correct bias in neural networks. The quantum processor generates controlled noise that is injected into the training data, acting as a mediator between the biased training data and the neural network, thereby improving prediction accuracy while managing system complexity through the use of quantum computational capabilities.
2Reliability
If iterative reclassification with noise adjustment is performed to reduce bias, then bias correction effectiveness is improved, but computational time increases
Solution Approach 1:
The patent employs periodic action through iterative reclassification cycles where noise parameters are adjusted and reapplied in successive training iterations. This periodic process allows the system to progressively reduce bias by cycling through noise generation, reclassification, and weight adjustment phases, improving bias correction effectiveness while the iterative nature enables convergence over time.
Data Source
AI summary
In an embodiment, a method includes classifying, using a neural network including quantum components, a data set to generate a first set of classified data. In the embodiment, the method includes generating noise in the quantum components. In the embodiment, the method includes reclassifying, using the neural network, the data set with the generated noise to generate a second set of classified data. In the embodiment, the method includes determining, responsive to comparing the first set of classified data and the second set of classified data, a sensitivity of the quantum components.