Quantum Few-Shot Image Classification.

Area of Science:

• Quantum Computing
• Machine Learning
• Computer Vision

Background:

• Few-shot learning (FSL) algorithms struggle with limited labeled data, leading to suboptimal performance.
• Existing FSL methods often require extensive datasets or complex parameter tuning.
• Overfitting is a significant challenge in FSL, especially with limited training examples.

Purpose of the Study:

• To propose a novel quantum-based methodology for few-shot image classification.
• To enhance FSL performance at both the data and parameter levels.
• To reduce computational resource requirements for FSL tasks.

Main Methods:

• Introduced a quantum augmentation image representation technique utilizing the local phase of quantum states.
• Developed a parameterized quantum circuit for classification model construction.
• Employed a reduced number of trainable parameters in the quantum circuit to mitigate overfitting.

Main Results:

• The quantum approach demonstrated superior performance compared to classical methods in few-shot learning scenarios.
• Experimental validation on three datasets confirmed the efficacy of the proposed quantum methodology.
• The method achieved improved results while requiring fewer computational resources.

Conclusions:

• The proposed quantum few-shot image classification methodology effectively addresses the limitations of classical FSL.
• Quantum augmentation and parameterized quantum circuits offer significant advantages for data and parameter level enhancements.
• This quantum approach presents a computationally efficient and high-performing solution for image classification with limited data.