A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification

• URL: http://arxiv.org/abs/2303.11705v1
• Date: Tue, 21 Mar 2023 09:51:19 GMT
• Title: A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification
• Authors: Amer Delilbasic, Bertrand Le Saux, Morris Riedel, Kristel Michielsen, Gabriele Cavallaro
• Abstract summary: This work proposes a novel quantum SVM for direct multiclass classification based on quantum annealing, called Quantum Multiclass SVM (QMSVM) The main objective of this work is to evaluate the feasibility, accuracy, and time performance of this approach. Experiments have been performed on the D-Wave Advantage quantum annealer for a classification problem on remote sensing data.
• Score: 26.80167258721593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in quantum machine learning and in particular for the popular quantum SVM. Several versions of the quantum SVM have been proposed, and quantum annealing has been shown to be effective in them. Extensions to multiclass problems have also been made, which consist of an ensemble of multiple binary classifiers. This work proposes a novel quantum SVM formulation for direct multiclass classification based on quantum annealing, called Quantum Multiclass SVM (QMSVM). The multiclass classification problem is formulated as a single Quadratic Unconstrained Binary Optimization (QUBO) problem solved with quantum annealing. The main objective of this work is to evaluate the feasibility, accuracy, and time performance of this approach. Experiments have been performed on the D-Wave Advantage quantum annealer for a classification problem on remote sensing data. The results indicate that, despite the memory demands of the quantum annealer, QMSVM can achieve accuracy that is comparable to standard SVM methods and, more importantly, it scales much more efficiently with the number of training examples, resulting in nearly constant time. This work shows an approach for bringing together classical and quantum computation, solving practical problems in remote sensing with current hardware.

Related papers

• Quantum Machine Learning with Application to Progressive Supranuclear Palsy Network Classification [0.0]
  We present a quantum machine learning model for the diagnosis of Progressive Supranuclear Palsy (PSP) disorder. The results suggest that quantum machine learning has led to noticeable advancement and outperforms classical frameworks. In particular, we have demonstrated the successful application of the present model on both a quantum simulator and real chips of the IBM quantum platform.
  arXiv  Detail & Related papers  (2024-07-06T14:16:31Z)
• Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
  We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
  arXiv  Detail & Related papers  (2024-06-05T17:16:07Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines [0.0]
  This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. We scrutinize the classification prowess of classical SVM and Quantum Support Vector Machines operational on quantum hardware over the Iris dataset.
  arXiv  Detail & Related papers  (2023-10-17T01:06:59Z)
• MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification [10.969833959443495]
  MORE stands for measurement and correlation based variational quantum multi-classifier. We implement MORE using the Qiskit Python library and evaluate it through extensive experiments on both noise-free and noisy quantum systems.
  arXiv  Detail & Related papers  (2023-07-21T19:33:10Z)
• Anticipative measurements in hybrid quantum-classical computation [68.8204255655161]
  We present an approach where the quantum computation is supplemented by a classical result. Taking advantage of its anticipation also leads to a new type of quantum measurements, which we call anticipative. In an anticipative quantum measurement the combination of the results from classical and quantum computations happens only in the end.
  arXiv  Detail & Related papers  (2022-09-12T15:47:44Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• Higgs analysis with quantum classifiers [0.0]
  We have developed two quantum classifier models for the $tbartH(bbarb)$ classification problem. Our results serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance.
  arXiv  Detail & Related papers  (2021-04-15T18:01:51Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• Efficient Discrete Feature Encoding for Variational Quantum Classifier [3.7576442570677253]
  Variational quantum classification (VQC) is one of such methods with possible quantum advantage. We introduce the use of quantum random-access coding (QRAC) to map discrete features efficiently into limited number of qubits for VQC. We experimentally show that QRAC can help speeding up the training of VQC by reducing its parameters via saving on the number of qubits for the mapping.
  arXiv  Detail & Related papers  (2020-05-29T04:43:14Z)

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.