Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework

• URL: http://arxiv.org/abs/2202.08024v1
• Date: Wed, 16 Feb 2022 12:37:10 GMT
• Title: Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
• Authors: Ra\'ul Berganza G\'omez, Corey O'Meara, Giorgio Cortiana, Christian B. Mendl and Juan Bernab\'e-Moreno
• Abstract summary: We take the first steps towards Automated Quantum Machine Learning (AutoQML) We propose a concrete description of the problem, and then develop a classical-quantum hybrid cloud architecture. As an application use-case, we train a quantum Geneversarative Adrial neural Network (qGAN) to generate energy prices that follow a known historic data distribution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The learning process of classical machine learning algorithms is tuned by hyperparameters that need to be customized to best learn and generalize from an input dataset. In recent years, Quantum Machine Learning (QML) has been gaining traction as a possible application of quantum computing which may provide quantum advantage in the future. However, quantum versions of classical machine learning algorithms introduce a plethora of additional parameters and circuit variations that have their own intricacies in being tuned. In this work, we take the first steps towards Automated Quantum Machine Learning (AutoQML). We propose a concrete description of the problem, and then develop a classical-quantum hybrid cloud architecture that allows for parallelized hyperparameter exploration and model training. As an application use-case, we train a quantum Generative Adversarial neural Network (qGAN) to generate energy prices that follow a known historic data distribution. Such a QML model can be used for various applications in the energy economics sector.

Related papers

• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective [7.7063925534143705]
  We introduce the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with machine learning algorithms. QT achieves remarkable results by employing a quantum neural network alongside a classical mapping model.
  arXiv  Detail & Related papers  (2024-05-18T14:35:57Z)
• 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)
• 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)
• 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)
• 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)
• Entangled Datasets for Quantum Machine Learning [0.0]
  We argue that one should instead employ quantum datasets composed of quantum states. We show how a quantum neural network can be trained to generate the states in the NTangled dataset. We also consider an alternative entanglement-based dataset, which is scalable and is composed of states prepared by quantum circuits.
  arXiv  Detail & Related papers  (2021-09-08T02:20:13Z)
• Quantum Machine Learning with HQC Architectures using non-Classically Simulable Feature Maps [0.30723404270319693]
  We present an application of QSVM (Quantum Support Vector Machines) to predict if a person will require mental health treatment in the future. We prove that NISQ HQC Architectures for Quantum Machine Learning can be used alternatively to create good performance models in near-term real-world applications.
  arXiv  Detail & Related papers  (2021-03-21T12:28:46Z)
• 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)
• Machine learning transfer efficiencies for noisy quantum walks [62.997667081978825]
  We show that the process of finding requirements on both a graph type and a quantum system coherence can be automated. The automation is done by using a convolutional neural network of a particular type that learns to understand with which network and under which coherence requirements quantum advantage is possible. Our results are of importance for demonstration of advantage in quantum experiments and pave the way towards automating scientific research and discoveries.
  arXiv  Detail & Related papers  (2020-01-15T18:36:53Z)

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.