Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
The curse of random quantum data
Kaining Zhang, Junyu Liu, Liu Liu +3 more·Aug 19, 2024
Quantum machine learning, which involves running machine learning algorithms on quantum devices, may be one of the most significant flagship applications for these devices. Unlike its classical counterparts, the role of data in quantum machine learni...
Bee-yond the Plateau: Training QNNs with Swarm Algorithms
Rubén Darío Guerrero·Aug 16, 2024
In the quest to harness the power of quantum computing, training quantum neural networks (QNNs) presents a formidable challenge. This study introduces an innovative approach, integrating the Bees Optimization Algorithm (BOA) to overcome one of the mo...
Neural Quantum States and Peaked Molecular Wave Functions: Curse or Blessing?
Aleksei Malyshev, Markus Schmitt, A. Lvovsky·Aug 14, 2024
The field of neural quantum states has recently experienced a tremendous progress, making them a competitive tool of computational quantum many-body physics. However, their largest achievements to date mostly concern interacting spin systems, while t...
From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks
Andrea Ceschini, Francesco Mauro, Francesca De Falco +7 more·Aug 12, 2024
Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challenges inherent in classical GNNs that are powerful tools for analyzing da...
SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation
Yilun Zhao, Bingmeng Wang, Wenle Jiang +4 more·Aug 10, 2024
Numerous quantum algorithms operate under the assumption that classical data has already been converted into quantum states, a process termed Quantum State Preparation (QSP). However, achieving precise QSP requires a circuit depth that scales exponen...
Quantum Neural Network Training of a Repeater Node
Diego Fuentealba, Jack Dahn, J. Steck +1 more·Aug 8, 2024
The construction of robust and scalable quantum gates is a uniquely hard problem in the field of quantum computing. Real-world quantum computers suffer from many forms of noise, characterized by the decoherence and relaxation times of a quantum circu...
Quantum Transfer Learning for MNIST Classification Using a Hybrid Quantum-Classical Approach
Soumyadip Sarkar·Aug 5, 2024
We implement a hybrid quantum-classical model for image classification that compresses MNIST digit images into a low-dimensional feature space and then maps these features onto a 5-qubit quantum state. First, an autoencoder compresses each $28\times2...
Understanding Deep Learning via Notions of Rank
Noam Razin·Aug 4, 2024
Despite the extreme popularity of deep learning in science and industry, its formal understanding is limited. This thesis puts forth notions of rank as key for developing a theory of deep learning, focusing on the fundamental aspects of generalizatio...
Experimental quantum-enhanced kernels on a photonic processor
Zhenghao Yin, Iris Agresti, G. Felice +9 more·Jul 29, 2024
Recently, machine learning had a remarkable impact, from scientific to everyday-life applications. However, complex tasks often imply unfeasible energy and computational power consumption. Quantum computation might lower such requirements, although i...
A Scalable Quantum Non-local Neural Network for Image Classification
Sparsh Gupta, Debanjan Konar, Vaneet Aggarwal·Jul 26, 2024
Non-local operations play a crucial role in computer vision enabling the capture of long-range dependencies through weighted sums of features across the input, surpassing the constraints of traditional convolution operations that focus solely on loca...
Systematic study of High $E_J/E_C$ transmon qudits up to $d = 12$
Z. Wang, R. Parker, E. Champion +1 more·Jul 24, 2024
Qudits provide a resource-efficient alternative to qubits for quantum information processing. The multilevel nature of the transmon, with its individually resolvable transition frequencies, makes it an attractive platform for superconducting circuit-...
Quanv4EO: Empowering Earth Observation by Means of Quanvolutional Neural Networks
A. Sebastianelli, Francesco Mauro, Giulia Ciabatti +4 more·Jul 24, 2024
A significant amount of remotely sensed data is generated daily by many Earth observation (EO) spaceborne and airborne sensors over different countries of our planet. Different applications use those data, such as natural hazard monitoring, global cl...
A quantum leaky integrate-and-fire spiking neuron and network
D. Brand, Francesco Petruccione·Jul 23, 2024
Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme ha...
Backdoor Attacks against Hybrid Classical-Quantum Neural Networks
Ji Guo, Wenbo Jiang, Rui Zhang +3 more·Jul 23, 2024
Hybrid Quantum Neural Networks (HQNNs) represent a promising advancement in Quantum Machine Learning (QML), yet their security has been rarely explored. In this paper, we present the first systematic study of backdoor attacks on HQNNs. We begin by pr...
Sparks of Quantum Advantage and Rapid Retraining in Machine Learning
William Troy·Jul 22, 2024
The advent of quantum computing holds the potential to revolutionize various fields by solving complex problems more efficiently than classical computers. Despite this promise, practical quantum advantage is hindered by current hardware limitations, ...
L2O-$g^{\dagger}$: Learning to Optimize Parameterized Quantum Circuits with Fubini-Study Metric Tensor
Yu-Chao Huang, H. Goan·Jul 20, 2024
Before the advent of fault-tolerant quantum computers, variational quantum algorithms (VQAs) play a crucial role in noisy intermediate-scale quantum (NISQ) machines. Conventionally, the optimization of VQAs predominantly relies on manually designed o...
Quantum Hamiltonian embedding of images for data reuploading classifiers
Peiyong Wang, Casey R. Myers, Lloyd C. L. Hollenberg +1 more·Jul 19, 2024
When applying quantum computing to machine learning tasks, one of the first considerations is the design of the quantum machine learning model itself. Conventionally, the design of quantum machine learning algorithms relies on the “quantisation” of c...
Classification and reconstruction for single-pixel imaging with classical and quantum neural networks
Sofya Manko, Dmitry Frolovtsev·Jul 17, 2024
Single-pixel cameras are an effective solution for imaging outside the visible spectrum, where traditional CMOS/CCD cameras have challenges. When combined with machine learning, they can analyze images quickly enough for practical applications. Solvi...
Conditional diffusion-based parameter generation for quantum approximate optimization algorithm
Fanxu Meng, Xiang-Yu Zhou, Pengcheng Zhu +1 more·Jul 17, 2024
The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm that shows promise in efficiently solving the Max-Cut problem, a representative example of combinatorial optimization. However, its effectiveness heavily de...
A Brief Review of Quantum Machine Learning for Financial Services
Mina Doosti, P. Wallden, C. Hamill +3 more·Jul 17, 2024
This review paper examines state-of-the-art algorithms and techniques in quantum machine learning with potential applications in finance. We discuss QML techniques in supervised learning tasks, such as Quantum Variational Classifiers, Quantum Kernel ...