Quantum Neural Network Training With Augmentation Functions
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Solution Overview
Problem
The inefficiency and prolonged calculation time in adjusting parameters for quantum machine learning due to barren plateaus and increased resource demands as quantum circuits deepen, particularly in noisy intermediate-scale quantum computers, hinder effective training of quantum neural networks.
Innovation Solution
A training device and method that utilizes augmentation functions to connect shallow quantum circuits across multiple layers, optimizing weights without updating parameters directly in the quantum circuits, thereby reducing the need for extensive quantum computations and minimizing calculation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If quantum circuits are deepened to improve model capacity and representational power, then the ability to solve complex problems is enhanced, but calculation time increases significantly and training becomes inefficient due to barren plateaus
Solution Approach 1:
The patent segments the quantum neural network into multiple layers with alternating quantum and classical processing stages. Each layer processes part of the computation, allowing the model to achieve deep structure capabilities while breaking down the overall calculation into manageable segments that can be executed more efficiently.
Solution Approach 2:
Instead of directly optimizing quantum circuit parameters which leads to barren plateaus, the patent inverts the approach by optimizing classical parameters in the augmentation functions that connect quantum layers. This indirect optimization approach avoids the barren plateau problem while still achieving effective model training.
2Adaptability or versatility
If quantum circuits are deepened to process complex information, then problem-solving capability is improved, but resource demands increase and training efficiency decreases
Solution Approach 1:
The patent introduces classical augmentation functions as intermediary components between quantum layers. These augmentation functions act as mediators that process quantum outputs and generate inputs for subsequent quantum layers, reducing the direct computational burden on quantum circuits while maintaining problem-solving capability.
Solution Approach 2:
The computation is segmented into discrete quantum and classical stages across multiple layers. Each layer handles a specific portion of the computation, allowing for more efficient resource utilization and faster training compared to deeply nested quantum circuits alone.
3Reliability
If parameters in quantum circuits are adjusted through quantum machine learning, then model performance is improved, but the number of quantum computations required increases
Solution Approach 1:
The patent inverts the parameter optimization strategy by keeping quantum circuit parameters fixed and instead optimizing classical parameters in the augmentation functions. This approach achieves model performance improvement while significantly reducing the number of quantum computations required for training.
Solution Approach 2:
The patent creates a hybrid architecture where classical functions copy and extend the capabilities of quantum circuits. The classical augmentation functions replicate and process information from quantum layers, allowing model performance to improve without proportionally increasing quantum computation requirements.
Data Source
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AI summary
A computer acquires a training data set of a machine learning model. The machine learning model includes a quantum circuit in each of a plurality of layers and a generating function that generates, from an output of a first quantum circuit in a preceding layer in two consecutive layers, an input of a second quantum circuit in a subsequent layer. The computer determines a value of a parameter included in the generating function by training the machine learning model using the quantum computer that executes calculation of the quantum circuit in each of the plurality of layers and the training data set. The computer generates the trained machine learning model by setting the value of the parameter in the generating function.