Binary Classifier Inspired by Quantum Theory

Machine Learning (ML) helps us to recognize patterns from raw data. ML is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current ML models are based on classical theories of probability and statistics, which can now be replaced by Quantum Theory (QT) with the aim of improving the effectiveness of ML. In this paper, we propose the Binary Classifier Inspired by Quantum Theory (BCIQT) model, which outperforms the state of the art classification in terms of recall for every category.Comment: AAAI 201

Quantum-inspired algorithm for direct multi-class classification

Over the last few decades, quantum machine learning has emerged as a groundbreaking discipline. Harnessing the peculiarities of quantum computation for machine learning tasks offers promising advantages. Quantum-inspired machine learning has revealed how relevant benefits for machine learning problems can be obtained using the quantum information theory even without employing quantum computers. In the recent past, experiments have demonstrated how to design an algorithm for binary classification inspired by the method of quantum state discrimination, which exhibits high performance with respect to several standard classifiers. However, a generalization of this quantuminspired binary classifier to a multi-class scenario remains nontrivial. Typically, a simple solution in machine learning decomposes multi-class classification into a combinatorial number of binary classifications, with a concomitant increase in computational resources. In this study, we introduce a quantum-inspired classifier that avoids this problem. Inspired by quantum state discrimination, our classifier performs multi-class classification directly without using binary classifiers. We first compared the performance of the quantum-inspired multi-class classifier with eleven standard classifiers. The comparison revealed an excellent performance of the quantum-inspired classifier. Comparing these results with those obtained using the decomposition in binary classifiers shows that our method improves the accuracy and reduces the time complexity. Therefore, the quantum-inspired machine learning algorithm proposed in this work is an effective and efficient framework for multi-class classification. Finally, although these advantages can be attained without employing any quantum component in the hardware, we discuss how it is possible to implement the model in quantum hardware

A quantum-inspired classifier for clonogenic assay evaluations.

Recent advances in Quantum Machine Learning (QML) have provided benefits to several computational processes, drastically reducing the time complexity. Another approach of combining quantum information theory with machine learning-without involving quantum computers-is known as Quantum-inspired Machine Learning (QiML), which exploits the expressive power of the quantum language to increase the accuracy of the process (rather than reducing the time complexity). In this work, we propose a large-scale experiment based on the application of a binary classifier inspired by quantum information theory to the biomedical imaging context in clonogenic assay evaluation to identify the most discriminative feature, allowing us to enhance cell colony segmentation. This innovative approach offers a two-fold result: (1) among the extracted and analyzed image features, homogeneity is shown to be a relevant feature in detecting challenging cell colonies; and (2) the proposed quantum-inspired classifier is a novel and outstanding methodology, compared to conventional machine learning classifiers, for the evaluation of clonogenic assays

Towards a Quantum-Inspired Binary Classifier

Machine Learning classification models learn the relation between input as features and output as a class in order to predict the class for the new given input. Several research works have demonstrated the effectiveness of machine learning algorithms but the state-of-the-art algorithms are based on the classical theories of probability and logic. Quantum Mechanics (QM) has already shown its effectiveness in many fields and researchers have proposed several interesting results which cannot be obtained through classical theory. In recent years, researchers have been trying to investigate whether the QM can help to improve the classical machine learning algorithms. It is believed that the theory of QM may also inspire an effective algorithm if it is implemented properly. From this inspiration, we propose the quantum-inspired binary classifier, which is based on quantum detection theory. We used text corpora and image corpora to explore the effect of our proposed model. Our proposed model outperforms the state-of-the-art models in terms of precision, recall, and F-measure for several topics (categories) in the 20 newsgroup text corpora. Our proposed model outperformed all the baselines in terms of recall when the MNIST handwritten image dataset was used; F-measure is also higher for most of the categories and precision is also higher for some categories. Our proposed model suggests that binary classification effectiveness can be achieved by using quantum detection theory. In particular, we found that our Quantum-Inspired Binary Classifier can increase the precision, recall, and F-measure of classification where the state-of-the-art methods cannot

Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence

Quantum learning: optimal classification of qubit states

Pattern recognition is a central topic in Learning Theory with numerous applications such as voice and text recognition, image analysis, computer diagnosis. The statistical set-up in classification is the following: we are given an i.i.d. training set $(X_{1},Y_{1}),... (X_{n},Y_{n})$ where $X_{i}$ represents a feature and $Y_{i}\in \{0,1\}$ is a label attached to that feature. The underlying joint distribution of $(X,Y)$ is unknown, but we can learn about it from the training set and we aim at devising low error classifiers $f:X\to Y$ used to predict the label of new incoming features. Here we solve a quantum analogue of this problem, namely the classification of two arbitrary unknown qubit states. Given a number of `training' copies from each of the states, we would like to `learn' about them by performing a measurement on the training set. The outcome is then used to design mesurements for the classification of future systems with unknown labels. We find the asymptotically optimal classification strategy and show that typically, it performs strictly better than a plug-in strategy based on state estimation. The figure of merit is the excess risk which is the difference between the probability of error and the probability of error of the optimal measurement when the states are known, that is the Helstrom measurement. We show that the excess risk has rate $n^{-1}$ and compute the exact constant of the rate.Comment: 24 pages, 4 figure