Binary classifiers for noisy datasets: a comparative study of existing quantum machine learning frameworks and some new approaches

• URL: http://arxiv.org/abs/2111.03372v1
• Date: Fri, 5 Nov 2021 10:29:05 GMT
• Title: Binary classifiers for noisy datasets: a comparative study of existing quantum machine learning frameworks and some new approaches
• Authors: N. Schetakis, D. Aghamalyan, M. Boguslavsky, P. Griffin
• Abstract summary: We apply Quantum Machine Learning frameworks to improve binary classification. noisy datasets are in financial datasets. New models exhibit better learning characteristics to asymmetrical noise in the dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the most promising areas of research to obtain practical advantage is Quantum Machine Learning which was born as a result of cross-fertilisation of ideas between Quantum Computing and Classical Machine Learning. In this paper, we apply Quantum Machine Learning (QML) frameworks to improve binary classification models for noisy datasets which are prevalent in financial datasets. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operating characteristic curve (ROC/AUC). By combining such approaches as hybrid-neural networks, parametric circuits, and data re-uploading we create QML inspired architectures and utilise them for the classification of non-convex 2 and 3-dimensional figures. An extensive benchmarking of our new FULL HYBRID classifiers against existing quantum and classical classifier models, reveals that our novel models exhibit better learning characteristics to asymmetrical Gaussian noise in the dataset compared to known quantum classifiers and performs equally well for existing classical classifiers, with a slight improvement over classical results in the region of the high noise.

Related papers

• Benchmarking quantum machine learning kernel training for classification tasks [0.0]
  This work performs a benchmark study of Quantum Kernel Estimation (QKE) and Quantum Kernel Training (QKT) with a focus on classification tasks. Two quantum feature mappings, namely ZZFeatureMap and CovariantFeatureMap, are analyzed in this context. Experimental results indicate that quantum methods exhibit varying performance across different datasets.
  arXiv  Detail & Related papers  (2024-08-17T10:53:06Z)
• Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset [0.0]
  We introduce a Quantum-Classical Bayesian Neural Network (QCBNN) that is capable to perform uncertainty-aware classification of medical dataset. We track multiple behavioral metrics that capture both predictive performance as well as model's uncertainty. It is our ambition to create a hybrid model that is capable to classify samples in a more uncertainty aware fashion.
  arXiv  Detail & Related papers  (2024-06-10T14:23:25Z)
• Strategic Data Re-Uploads: A Pathway to Improved Quantum Classification Data Re-Uploading Strategies for Improved Quantum Classifier Performance [0.0]
  Re-uploading classical information into quantum states multiple times can enhance the accuracy of quantum classifiers. We demonstrate our approach to two classification patterns: a linear classification pattern (LCP) and a non-linear classification pattern (NLCP)
  arXiv  Detail & Related papers  (2024-05-15T14:28:00Z)
• Ensembles of Quantum Classifiers [0.0]
  A viable approach for the execution of quantum classification algorithms is the introduction of the ensemble methods. In this work, we present an implementation and an empirical evaluation of ensembles of quantum classifiers for binary classification.
  arXiv  Detail & Related papers  (2023-11-16T10:27:25Z)
• Classical-to-Quantum Transfer Learning Facilitates Machine Learning with Variational Quantum Circuit [62.55763504085508]
  We prove that a classical-to-quantum transfer learning architecture using a Variational Quantum Circuit (VQC) improves the representation and generalization (estimation error) capabilities of the VQC model. We show that the architecture of classical-to-quantum transfer learning leverages pre-trained classical generative AI models, making it easier to find the optimal parameters for the VQC in the training stage.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing [75.75419308975746]
  This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification. Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
  arXiv  Detail & Related papers  (2022-02-17T09:55:21Z)
• Quantum Machine Learning with SQUID [64.53556573827525]
  We present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. We provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset.
  arXiv  Detail & Related papers  (2021-04-30T21:34:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.