aqora

Supervised learning with a quantum classifier using multi-level systems

Soumik Adhikary, Siddharth Dangwal, Debanjan Bhowmik

arXiv:1908.08385Quantum Information ProcessingJan 21, 202070 citations

Self-supervision with quantum classifiers

Minh-Hieu Tran, Nam-Hai Nguyen, Phuong-Nam Nguyen +1 more

Self-supervised learning (SSL) has emerged as a novel method for learning representations from inputs without data annotations. A key challenge in SSL is avoiding the collapse of representations, leading to poor predictive models. This research proposes an SSL framework that leverages quantum classifiers to address this issue. We achieve well-stratified, nontrivial representations by embedding data into the Hilbert space via quantum classifiers. The algorithm works by minimizing the Kullback-Leibler (KL) divergence to maximize the agreement of positive pairs and minimize the agreement of negative pairs. We demonstrate the proof-of-concept in image classification tasks using MNIST, KMNIST, and FashionMNIST datasets. Classifiers trained with our framework achieve higher predictive performance than classical classifiers, with a maximum accuracy of 65% on MNIST and more than 70% on KMNIST and FashionMNIST using unseen samples without fine-tuning. The code implementation is available at https://github.com/namhai03/QSSLSelf-supervised learning (SSL) has emerged as a novel method for learning representations from inputs without data annotations. A key challenge in SSL is avoiding the collapse of representations, leading to poor predictive models. This research proposes an SSL framework that leverages quantum classifiers to address this issue. We achieve well-stratified, nontrivial representations by embedding data into the Hilbert space via quantum classifiers. The algorithm works by minimizing the Kullback-Leibler (KL) divergence to maximize the agreement of positive pairs and minimize the agreement of negative pairs. We demonstrate the proof-of-concept in image classification tasks using MNIST, KMNIST, and FashionMNIST datasets. Classifiers trained with our framework achieve higher predictive performance than classical classifiers, with a maximum accuracy of 65% on MNIST and more than 70% on KMNIST and FashionMNIST using unseen samples without fine-tuning. The code implementation is available at https://github.com/namhai03/QSSL

Nonlinear Theory and Its Applications IEICEJan 1, 20250 citations

Quantum Entanglement Inspired Correlation Learning for Classification

Junwei Zhang, Zhao Li, Yinghui Wang +4 more

Lecture notes in computer scienceJan 1, 20227 citations

Quantum computing methods for supervised learning

Viraj Kulkarni, Milind Kulkarni, Aniruddha Pant

arXiv:2006.12025Quantum Machine IntelligenceSep 6, 202138 citations

An Algorithm for Fast Supervised Learning in Variational Circuits through Simultaneous Processing of Multiple Samples

Siddharth Dangwal, Ritvik Sharma, Debanjan Bhowmik

We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly. The algorithm can be adapted for any ansatz used in the variational circuit. The presented algorithm utilizes qRAM and other quantum circuits in the forward pass. Further, instead of the usual practice of computing the loss classically, we calculate the loss using a Swap-test circuit. The algorithm thus brings down the training cost of a variational classifier to O(logN)from the usual O(N)when training on a dataset of N samples. Although we discuss only binary classification in the paper, the algorithm can be easily generalized to multi-class classification.We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly. The algorithm can be adapted for any ansatz used in the variational circuit. The presented algorithm utilizes qRAM and other quantum circuits in the forward pass. Further, instead of the usual practice of computing the loss classically, we calculate the loss using a Swap-test circuit. The algorithm thus brings down the training cost of a variational classifier to O(logN)from the usual O(N)when training on a dataset of N samples. Although we discuss only binary classification in the paper, the algorithm can be easily generalized to multi-class classification.

arXiv:2011.14297Nov 29, 20200 citations

Polyadic Quantum Classifier

William Cappelletti, Rebecca Erbanni, Joaquın Keller

We introduce here a supervised quantum machine learning algorithm for multi-class classification on NISQ architectures. A parametric quantum circuit is trained to output a specific bit string corresponding to the class of the input datapoint. We train and test it on an IBMq 5-qubit quantum computer and the algorithm shows good accuracy - compared to a classical machine learning model - for ternary classification of the Iris dataset and an extension of the XOR problem. Furthermore, we evaluate with simulations how the algorithm fares for a binary and a quaternary classification on resp. a known binary dataset and a synthetic dataset.We introduce here a supervised quantum machine learning algorithm for multi-class classification on NISQ architectures. A parametric quantum circuit is trained to output a specific bit string corresponding to the class of the input datapoint. We train and test it on an IBMq 5-qubit quantum computer and the algorithm shows good accuracy - compared to a classical machine learning model - for ternary classification of the Iris dataset and an extension of the XOR problem. Furthermore, we evaluate with simulations how the algorithm fares for a binary and a quaternary classification on resp. a known binary dataset and a synthetic dataset.

arXiv:2007.14044IEEE International Conference on Quantum Computing and Engineering (QCE)Oct 1, 202026 citations

Entanglement assisted training algorithm for supervised quantum classifiers

Related papers

Entanglement assisted training algorithm for supervised quantum classifiers

Related papers