Transfer Learning Using Variational Quantum Circuit

Abstract

AbstractQuantum computing and particularly quantum machine learning has witnessed a surge in the direction of fruitful research being done in recent years. Variational quantum algorithms have gained popularity due to their resilience to error, which is a common occurrence in the near term intermediate scale quantum (NISQ) hardware. In such an algorithm, an optimal function is mapped by a quantum circuit which is designed using weighted parameterized gates to be trained based on a cost function that is optimized using gradient descent. A Variational Quantum Classifier (VQC) is such an algorithm and is used for supervised learning to perform data classification. But since the quantum hardware available today is not powerful enough for a VQC to come of any practical use in the current setting, we implement a transfer learning pipeline in which a pre-trained image processing model is fine tuned using a dressed VQC; for a specific, binary classification task. Our aim was to study the working of a VQC and test it’s behaviour with respect to multiple hyperparameters such as circuit depth, number of qubits, etc. We trained our transfer learning-VQC model for 4 different datasets and for different hyperparameters and were able to get close to state of the art performance in most of our tests, solidifying the prospect of VQC as an efficient quantum-classical hybrid supervised learning algorithm. It should be noted that the study of VQC was chosen over that of other algorithms available for classification because recent advancements in the understanding of VQCs that show promise in favour of the algorithm.KeywordsQuantum computingMachine learningTransfer learningBinary classification