Exploring Quantum Active Learning for Materials Design and Discovery

• URL: http://arxiv.org/abs/2407.18731v1
• Date: Fri, 26 Jul 2024 13:34:26 GMT
• Title: Exploring Quantum Active Learning for Materials Design and Discovery
• Authors: Maicon Pierre Lourenço, Hadi Zadeh-Haghighi, Jiří Hostaš, Mosayeb Naseri, Daya Gaur, Christoph Simon, Dennis R. Salahub,
• Abstract summary: We extend our previous studies of materials discovery using classical active learning (AL) to explore the use of quantum algorithms within the AL framework (QAL) The proposed QAL uses quantum support vector regressor (QSVR) or a quantum Gaussian process regressor (QGPR) with various quantum kernels and different feature maps. Our results revealed that the QAL method improved the searches in most cases, but not all, seemingly correlated with the roughness of the data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our previous studies of materials discovery using classical active learning (AL), which showed remarkable economy of data, to explore the use of quantum algorithms within the AL framework (QAL) as implemented in the MLChem4D and QMLMaterials codes. The proposed QAL uses quantum support vector regressor (QSVR) or a quantum Gaussian process regressor (QGPR) with various quantum kernels and different feature maps. Data sets include perovskite properties (piezoelectric coefficient, band gap, energy storage) and the structure optimization of a doped nanoparticle (3Al@Si11) chosen to compare with classical AL results. Our results revealed that the QAL method improved the searches in most cases, but not all, seemingly correlated with the roughness of the data. QAL has the potential of finding optimum solutions, within chemical space, in materials science and elsewhere in chemistry.

Related papers

• Quantum Active Learning [3.3202982522589934]
  Training a quantum neural network typically demands a substantial labeled training set for supervised learning. QAL effectively trains the model, achieving performance comparable to that on fully labeled datasets. We elucidate the negative result of QAL being overtaken by random sampling baseline through miscellaneous numerical experiments.
  arXiv  Detail & Related papers  (2024-05-28T14:39:54Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Unifying (Quantum) Statistical and Parametrized (Quantum) Algorithms [65.268245109828]
  We take inspiration from Kearns' SQ oracle and Valiant's weak evaluation oracle. We introduce an extensive yet intuitive framework that yields unconditional lower bounds for learning from evaluation queries.
  arXiv  Detail & Related papers  (2023-10-26T18:23:21Z)
• QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
  A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques. Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
  arXiv  Detail & Related papers  (2023-08-25T15:08:19Z)
• Efficient VQE Approach for Accurate Simulations on the Kagome Lattice [0.0]
  This study focuses on using multiple ansatz models to create an effective Variational Quantum Eigensolver (VQE) on the Kagome lattice. By comparing various optimisation methods and optimising the VQE ansatz models, the main goal is to estimate ground state attributes with high accuracy.
  arXiv  Detail & Related papers  (2023-06-01T09:14:34Z)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• 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)
• 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)
• QKSA: Quantum Knowledge Seeking Agent -- resource-optimized reinforcement learning using quantum process tomography [1.3946983517871423]
  We extend the universal reinforcement learning (URL) agent models of artificial general intelligence to quantum environments. The utility function of a classical exploratory Knowledge Seeking Agent, KL-KSA, is generalized to distance measures from quantum information theory. QKSA is the first proposal for a framework that resembles the classical URL models.
  arXiv  Detail & Related papers  (2021-12-07T11:36:54Z)
• Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning [0.0]
  We introduce a training method for parametrized quantum circuits (PQCs) that can be used to solve RL tasks for discrete and continuous state spaces. We investigate which architectural choices for quantum Q-learning agents are most important for successfully solving certain types of environments.
  arXiv  Detail & Related papers  (2021-03-28T08:57:22Z)
• Hybrid Quantum-Classical Graph Convolutional Network [7.0132255816377445]
  This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data. The proposed framework demonstrates an advantage over classical multilayer perceptron and convolutional neural networks in the aspect of number of parameters. In terms of testing accuracy, the QGCNN shows comparable performance to a quantum convolutional neural network on the same HEP dataset.
  arXiv  Detail & Related papers  (2021-01-15T16:02:52Z)

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.