Papers
Live trends in quantum computing research, updated daily from arXiv.
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12,931 papers in 12 months (-5% vs prior quarter)
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Enhancing the expressivity of quantum neural networks with residual connections
Jingwei Wen, Zhiguo Huang, Dunbo Cai +1 more·Jan 29, 2024
The authors introduce a quantum circuit-based algorithm to implement quantum residual neural networks by incorporating auxiliary qubits in the data-encoding and trainable blocks, which leads to an improved expressivity of parameterized quantum circui...
Non-parametric Greedy Optimization of Parametric Quantum Circuits
Koustubh Phalak, Swaroop Ghosh·Jan 27, 2024
The use of Quantum Neural Networks (QNN) that are analogous to classical neural networks, has greatly increased in the past decade owing to the growing interest in the field of Quantum Machine Learning (QML). A QNN consists of three major components:...
Evaluation of QCNN-LSTM for Disability Forecasting in Multiple Sclerosis Using Sequential Multisequence MRI
John D. Mayfield, I. E. Naqa·Jan 22, 2024
Introduction Quantum Convolutional Neural Network (QCNN)-Long Short-Term Memory (LSTM) models were studied to provide sequential relationships for each timepoint in MRIs of patients with Multiple Sclerosis (MS). In this pilot study, we compared three...
Quantum Architecture Search with Unsupervised Representation Learning
Yize Sun, Zixin Wu, Volker Tresp +1 more·Jan 21, 2024
Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS) on Noisy Intermediate-Scale Quantum (NISQ) devices. QAS is designed to optimize quantum circuits for Variational Quantum Algorithms (VQAs)...
VQC-based reinforcement learning with data re-uploading: performance and trainability
Rodrigo Coelho, A. Sequeira, Luís Paulo Santos·Jan 21, 2024
Reinforcement learning (RL) consists of designing agents that make intelligent decisions without human supervision. When used alongside function approximators such as Neural Networks (NNs), RL is capable of solving extremely complex problems. Deep Q-...
Novel techniques for efficient quantum state tomography and quantum process tomography and their experimental implementation
Akshay Gaikwad·Jan 18, 2024
This thesis actively focuses on designing, analyzing, and experimentally implementing various QST and QPT protocols using an NMR ensemble quantum processor and superconducting qubit-based IBM cloud quantum processor. Part of the thesis also includes ...
Elivagar: Efficient Quantum Circuit Search for Classification
Sashwat Anagolum, Narges Alavisamani, Poulami Das +2 more·Jan 17, 2024
Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is challenging --- the design space scales exponentially with circuit size, and there are few well-supported guiding principles for QML circuit design. Although recent ...
Symmetry breaking in geometric quantum machine learning in the presence of noise
Cenk Tüysüz, Su Yeon Chang, Maria Demidik +3 more·Jan 17, 2024
Geometric quantum machine learning based on equivariant quantum neural networks (EQNNs) recently appeared as a promising direction in quantum machine learning. Despite encouraging progress, studies are still limited to theory, and the role of hardwar...
Effective detection of quantum discord by using convolutional neural networks
Narjes Taghadomi, A. Mani, Ali Fahim +2 more·Jan 15, 2024
Quantum discord represents a type of correlation defined as the difference between quantum and classical mutual information of two parties. Due to the optimization involved in the definition of classical mutual information of quantum systems, calcula...
Advantage of Quantum Neural Networks as Quantum Information Decoders
Weishun Zhong, O. Shtanko, R. Movassagh·Jan 11, 2024
A promising strategy to protect quantum information from noise-induced errors is to encode it into the low-energy states of a topological quantum memory device. However, readout errors from such memory under realistic settings is less understood. We ...
Photonics for Sustainable Computing
Farbin Fayza, S. P. Rao, Darius Bunandar +2 more·Jan 10, 2024
Photonic integrated circuits are finding use in a variety of applications including optical transceivers, LIDAR, bio-sensing, photonic quantum computing, and Machine Learning (ML). In particular, with the exponentially increasing sizes of ML models, ...
Loop Feynman integration on a quantum computer
Jorge J. Martínez de Lejarza, Leandro Cieri, M. Grossi +2 more·Jan 5, 2024
This work investigates in detail the performance and advantages of a new quantum Monte Carlo integrator, dubbed quantum Fourier iterative amplitude estimation (QFIAE), to numerically evaluate for the first time loop Feynman integrals in a near-term q...
Ultrahigh-fidelity spatial mode quantum gates in high-dimensional space by diffractive deep neural networks
Qianke Wang, Jun Liu, Dawei Lyu +1 more·Jan 5, 2024
While the spatial mode of photons is widely used in quantum cryptography, its potential for quantum computation remains largely unexplored. Here, we showcase the use of the multi-dimensional spatial mode of photons to construct a series of high-dimen...
Microwave signal processing using an analog quantum reservoir computer
Alen Senanian, Sridhar Prabhu, Vladimir Kremenetski +8 more·Dec 26, 2023
Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum n...
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Shaolun Ruan, Zhiding Liang, Qian-Guo Guan +4 more·Dec 23, 2023
With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks....
Distributed quantum neural networks via partitioned features encoding
Yoshiaki Kawase·Dec 21, 2023
Quantum neural networks are expected to be a promising application in near-term quantum computing, but face challenges such as vanishing gradients during optimization and limited expressibility by a limited number of qubits and shallow circuits. To m...
Distributed Quantum Learning with co-Management in a Multi-tenant Quantum System
Anthony D'Onofrio, A. Hossain, Lesther Santana +5 more·Dec 13, 2023
The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications Given its immense potential, quantum-based computing systems have attra...
Radio Signal Classification by Adversarially Robust Quantum Machine Learning
Yanqiu Wu, Eromanga Adermann, Chandra Thapa +3 more·Dec 13, 2023
Radio signal classification plays a pivotal role in identifying the modulation scheme used in received radio signals, which is essential for demodulation and proper interpretation of the transmitted information. Researchers have underscored the high ...
Non-Markovian Feedback for Optimized Quantum Error Correction.
Matteo Puviani, Sangkha Borah, Remmy Zen +2 more·Dec 12, 2023
Bosonic codes allow the encoding of a logical qubit in a single component device, utilizing the infinitely large Hilbert space of a harmonic oscillator. In particular, the Gottesman-Kitaev-Preskill code has recently been demonstrated to be correctabl...
Evaluating the Convergence Limit of Quantum Neural Tangent Kernel
Trong Duong·Dec 5, 2023
Quantum variational algorithms have been one of major applications of quantum computing with current quantum devices. There are recent attempts to establish the foundation for these algorithms. A possible approach is to characterize the training dyna...