Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?

• URL: http://arxiv.org/abs/2204.02784v1
• Date: Mon, 4 Apr 2022 21:16:06 GMT
• Title: Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?
• Authors: Mohammad Masum, Mohammad Nazim, Md Jobair Hossain Faruk, Hossain Shahriar, Maria Valero, Md Abdullah Hafiz Khan, Gias Uddin, Shabir Barzanjeh, Erhan Saglamyurek, Akond Rahman, Sheikh Iqbal Ahamed
• Abstract summary: This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML) Due to limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as Qiskit and IBM Quantum. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks.
• Score: 5.655023007686363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks.

Related papers

• 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)
• Quantum-Assisted Simulation: A Framework for Developing Machine Learning Models in Quantum Computing [0.0]
  We investigate the history of quantum computing, examine existing QML algorithms, and present a simplified procedure for setting up simulations of QML algorithms. We conduct simulations on a dataset using both traditional machine learning and quantum machine learning approaches.
  arXiv  Detail & Related papers  (2023-11-17T07:33:42Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [59.07246314484875]
  TeD-Q is an open-source software framework for quantum machine learning. It seamlessly integrates classical machine learning libraries with quantum simulators. It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
  arXiv  Detail & Related papers  (2023-01-13T09:35:05Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Open Source Variational Quantum Eigensolver Extension of the Quantum Learning Machine (QLM) for Quantum Chemistry [0.0]
  We introduce a novel open-source QC package, denoted Open-VQE, providing tools for using and developing chemically-inspired adaptive methods. It is able to use the Atos Quantum Learning Machine (QLM), a general programming framework enabling to write, optimize simulate computing programs. Along with OpenVQE, we introduce myQLMFermion, a new open-source module (that includes the key QLM ressources that are important for QC developments)
  arXiv  Detail & Related papers  (2022-06-17T14:24:22Z)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Cutting Quantum Circuits to Run on Quantum and Classical Platforms [25.18520278107402]
  CutQC is a scalable hybrid computing approach that distributes a large quantum circuit onto quantum (QPU) and classical platforms ( CPU or GPU) for co-processing. It achieves much higher quantum circuit evaluation fidelity than the large NISQ devices achieve in real-system runs.
  arXiv  Detail & Related papers  (2022-05-12T02:09:38Z)
• Investigation of Quantum Support Vector Machine for Classification in NISQ era [0.0]
  We investigate quantum support vector machine (QSVM) algorithm and its circuit version on present quantum computers. We compute the efficiency of the QSVM circuit implementation method by encoding training and testing data sample in quantum circuits. We highlight the technical difficulties one would face while applying the QSVM algorithm on current NISQ era devices.
  arXiv  Detail & Related papers  (2021-12-13T18:59:39Z)
• 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)
• Classification with Quantum Machine Learning: A Survey [17.55390082094971]
  We combine classical machine learning (ML) with Quantum Information Processing (QIP) to build a new field in the quantum world is called Quantum Machine Learning (QML) This paper presents and summarizes a comprehensive survey of the state-of-the-art advances in Quantum Machine Learning (QML)
  arXiv  Detail & Related papers  (2020-06-22T14:05:31Z)

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.