Transfer Learning in QML — A Beginner’s Overview
Day 26 — Quantum30 Challenge 2.0
Transfer learning in the context of neural networks is a powerful technique that can be likened to leveraging knowledge and skills acquired from one domain to excel in a related but distinct domain. To put it simply, it’s analogous to learning how to cook one type of dish and then using that culinary expertise to prepare a different dish.
In the realm of neural networks, transfer learning entails starting with a pre-trained model. This model, typically a neural network, has already been trained on a substantial dataset for a specific task.
Instead of crafting a new model from scratch for each distinct task, transfer learning allows us to adapt and fine-tune the pre-trained model for the new task at hand. This adaptation involves modifying the pre-trained model (A modified to A’), often by removing some of its final layers, and then connecting it to a new trainable network (B) that aligns with the characteristics of the new dataset.
The different implementations and the selected approach
Depending on the nature of A and B, one can have different implementations of Transfer Learning:
Let us focus on the CQ model. Here, we use a pre-trained classical model to help solve a new task using a quantum model.
Let’s imagine we have a pre-trained model (A) that has become exceptionally proficient at recognizing diverse objects in images. It has learned this skill from a vast dataset like ImageNet, more specifically the ResNet18 program. To make it suitable for a different but related task, we surgically remove the part of the model responsible for making the ultimate classification decisions. What remains is a pre-processing block capable of transforming high-quality images into abstract features or characteristics.
Now, to bridge classical and quantum computing approaches, we link this pre-processing block with a quantum circuit. The pre-processing block takes care of feature extraction from the images, while the quantum circuit plays a crucial role in making the final decision based on these extracted features. To ensure the system can effectively recognize new data, it undergoes training using a dataset specific to the new task. For example, if the objective is insect classification, the Hymenoptera dataset, containing images of ants and bees, could be employed for training purposes.
This entire process can be visualized as a data processing pipeline, where each step in the pipeline plays a distinct role in the transformation of raw images into precise classifications. Transfer learning, as a whole, enables us to harness knowledge acquired in one domain, adapt it using both classical and quantum components, and efficiently tackle fresh challenges that may emerge in related domains.