Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC), an important primitive to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. We show that the proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances in classifying real-world datasets, and thus its possibility to leverage near-term quantum computers for quantum machine learning.
Impact of Circuit Depth versus Qubit Count on Variational Quantum Classifiers for Higgs Boson Signal Detection
School of Computing, Universiti Utara Malaysia（乌拉尔大学计算机学院）；Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung（Sunan Gunung Djati Bandung大学科学与技术学院）
Special-Unitary Parameterization for Trainable Variational Quantum Circuits
Department of Electrical and Electronic Engineering, Imperial College London, London, UK（电气与电子工程系，帝国理工学院伦敦校区，伦敦，英国）；Centre for Quantum Engineering, Science and Technology (QuEST), Imperial College London, London, UK（量子工程、科学与技术中心（QuEST），帝国理工学院伦敦校区，伦敦，英国）；Brookhaven National Laboratory, New York, USA（布鲁克海文国家实验室，纽约，美国）；National Taiwan University, Taipei, Taiwan（台湾大学，台北，台湾）
Variational Quantum Classifiers Through the Lens of the Hessian
National Institute of Technology, Agartala（阿加尔塔拉国家理工学院）；Chitkara University Institute of Engineering & Technology, Chitkara University（奇特卡拉大学工程与技术学院）；Mansoura University（曼苏拉大学）
IT4Innovations, VSB - Technical University of Ostrava（IT4Innovations奥斯特拉瓦技术大学）；Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava（电气工程与计算机科学学院，奥斯特拉瓦技术大学）
A hybrid variational quantum circuit approach for stabilizer states classifiers
Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS, University of Technology of Belfort-Montbéliard（贝尔福-蒙贝利亚尔技术大学）；Mathematics and Statistics Department, Auburn University（奥本大学）
Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification
Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Selangor, Malaysia（计算机科学与多媒体学院，林肯大学学院，Petaling Jaya，Selangor，马来西亚）
Hybrid quantum tensor networks for aeroelastic applications
Institute for AI Safety and Security（人工智能安全与保安研究所）；German Aerospace Center (DLR)（德国航空航天中心）；Institute of Aeroelasticity（气动弹性研究所）；Institute of Neural Information Processing（神经信息处理研究所）；University of Ulm（乌尔姆大学）
Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）；The Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程学系）；Graduate School of China Academy of Engineering Physics（中国工程物理研究院研究生院）
A Variational Dissipative Framework for Quantum Algorithms
Center on Frontiers of Computing Studies, Peking University（北京大学前沿计算研究中心）；School of Computer Science, Peking University（北京大学计算机学院）；Faculty of Information Science and Engineering, Ocean University of China（中国海洋大学信息科学与工程学院）；Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所）；China Center of Advanced Science and Technology（中国科学院科技政策与管理科学研究所）
Physics-Informed Variational Quantum Classifier for Phase Detection in Strongly Correlated Matter
Instituto de Física Corpuscular, Universitat de València – Consejo Superior de Investigaciones Científicas（瓦伦西亚大学-西班牙国家研究委员会粒子物理研究所）；Escuela de Ciencias, Ingeniería y Diseño, Universidad Europea de Valencia（瓦伦西亚欧洲大学科学与工程设计学院）；Facultat de Física, Universitat de València（瓦伦西亚大学物理学院）
Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning
Quemix Inc.（Quemix公司）；Department of Physics, The University of Tokyo（东京大学物理学系）；Quantum Materials and Applications Research Center, National Institutes for Quantum Science and Technology（国立量子科学技术研究院量子材料与应用研究中心）；Physics Division, Sophia University（上智大学物理学科）
An Application of Quantum Machine Learning on Quantum Correlated Systems: Quantum Convolutional Neural Network as a Classifier for Many-Body Wavefunctions from the Quantum Variational Eigensolver
Trainable Quantum Channels as Computational Primitives for Quantum Learning
China Mobile (Suzhou) Software Technology Company Limited（中国移动（苏州）软件技术有限公司）；QuSpect Technology Company Limited（QuSpect科技有限公司）；School of Physical Science and Technology, Beijing University of Posts and Telecommunications（北京邮电大学物理科学与技术学院）；Beijing Academy of Quantum Information Sciences（北京量子信息科学研究院）；State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University（清华大学物理系低维量子物理国家重点实验室）；Frontier Science Center for Quantum Information（量子信息前沿科学中心）
