Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
MAQA: a quantum framework for supervised learning
A. Macaluso, M. Klusch, Stefano Lodi +1 more·Mar 1, 2023
Quantum machine learning has the potential to improve traditional machine learning methods and overcome some of the main limitations imposed by the classical computing paradigm. However, the practical advantages of using quantum resources to solve pa...
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent
Jun Qi, Xiao-Lei Zhang, Javier Tejedor·Feb 27, 2023
Quantum federated learning (QFL) is a quantum extension of the classical federated learning model across multiple local quantum devices. An efficient optimization algorithm is always expected to minimize the communication overhead among different qua...
On the Behaviour of Pulsed Qubits and their Application to Feed Forward Networks
Matheus Hammes, A. Robles-Kelly·Feb 21, 2023
In the last two decades, the combination of machine learning and quantum computing has been an ever-growing topic of interest but, to this date, the limitations of quantum computing hardware have somewhat restricted the use of complex multi-qubit ope...
Long-Lived Particles Anomaly Detection with Parametrized Quantum Circuits
Simone Bordoni, Denis Stanev, T. Santantonio +1 more·Feb 13, 2023
We investigate the possibility to apply quantum machine learning techniques for data analysis, with particular regard to an interesting use-case in high-energy physics. We propose an anomaly detection algorithm based on a parametrized quantum circuit...
Thermodynamic AI and the Fluctuation Frontier
Patrick J. Coles·Feb 9, 2023
Many Artificial Intelligence (AI) algorithms are inspired by physics and employ stochastic fluctuations. We connect these physics-inspired AI algorithms by unifying them under a single mathematical framework that we call Thermodynamic AI, including: ...
Formalising and Learning a Quantum Model of Concepts
S. Tull, R. A. Shaikh, Sara Sabrina Zemljič +1 more·Feb 7, 2023
In this report we present a new modelling framework for concepts based on quantum theory, and demonstrate how the conceptual representations can be learned automatically from data. A contribution of the work is a thorough category-theoretic formalisa...
A Quantum Neural Network Regression for Modeling Lithium-ion Battery Capacity Degradation
Anh Phuong Ngo, Nhat Le, Hieu Nguyen +2 more·Feb 6, 2023
Given their high power density, low discharge rate, and decreasing cost, rechargeable lithium-ion batteries (LiBs) have found a wide range of applications such as power grid-level storage systems, electric vehicles (EVs), and mobile devices. Developi...
Towards interpretable quantum machine learning via single-photon quantum walks
Fulvio Flamini, Marius Krumm, Lukas J. Fiderer +2 more·Jan 31, 2023
Variational quantum algorithms represent a promising approach to quantum machine learning where classical neural networks are replaced by parametrized quantum circuits. However, both approaches suffer from a clear limitation, that is a lack of interp...
Implementing a Hybrid Quantum-Classical Neural Network by Utilizing a Variational Quantum Circuit for Detection of Dementia
R. Kim·Jan 29, 2023
Magnetic resonance imaging (MRI) is a common technique to scan brains for strokes, tumors, and other abnormalities that cause forms of dementia. However, correctly diagnosing forms of dementia from MRIs is difficult, as nearly 1 in 3 patients with Al...
Machine-Guided Design of Oxidation-Resistant Superconductors for Quantum Information Applications
Carson Koppel, Brandon Wilfong, Allana G. Iwanicki +3 more·Jan 27, 2023
Decoherence in superconducting qubits has long been attributed to two-level systems arising from the surfaces and interfaces present in real devices. A recent significant step in reducing decoherence was the replacement of superconducting niobium by ...
Deep Quantum Error Correction
Yoni Choukroun, Lior Wolf·Jan 27, 2023
Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. QECC, as its classical counterpart (ECC), enables the reduction of error rates, by distributing quantum logical information across redundant p...
Secure Synchronization of Artificial Neural Networks Used to Correct Errors in Quantum Cryptography
Marcin Niemiec, Tymoteusz Widlarz, Miralem Mehic·Jan 26, 2023
Quantum cryptography can provide a very high level of data security. However, a big challenge of this technique is errors in quantum channels. Therefore, error correction methods must be applied in real implementations. An example is error correction...
User Trajectory Prediction in Mobile Wireless Networks Using Quantum Reservoir Computing
Zoubeir Mlika, S. Cherkaoui, J. Laprade +1 more·Jan 20, 2023
This paper applies a quantum machine learning technique to predict mobile users' trajectories in mobile wireless networks using an approach called quantum reservoir computing (QRC). Mobile users' trajectories prediction belongs to the task of tempora...
Quantum Neural Network Inspired Hardware Adaptable Ansatz for Efficient Quantum Simulation of Chemical Systems.
Xiongzhi Zeng, Yi Fan, Jie Liu +2 more·Jan 18, 2023
The variational quantum eigensolver is a promising way to solve the Schrödinger equation on a noisy intermediate-scale quantum (NISQ) computer, while its success relies on a well-designed wave function ansatz. Inspired by the quantum neural network, ...
Hybrid quantum-classical convolutional neural networks to improve molecular protein binding affinity predictions
L. Domingo, M. Djukic, C. Johnson +1 more·Jan 16, 2023
One of the main challenges in drug discovery is to find molecules that bind specifically and strongly to their target protein while having minimal binding to other proteins. By predicting binding affinity, it is possible to identify the most promisin...
Restricting to the chip architecture maintains the quantum neural network accuracy
Lucas Friedrich, J. Maziero·Dec 29, 2022
In the era of noisy intermediate-scale quantum devices, variational quantum algorithms (VQAs) stand as a prominent strategy for constructing quantum machine learning models. These models comprise both a quantum and a classical component. The quantum ...
Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping
Michael Kölle, Alessandro Giovagnoli, Jonas Stein +3 more·Dec 22, 2022
In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs). VQCs are inspired by artificial neural networks, which achieve extraordinary performance in a wide range of AI tasks as massi...
An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes
Alexander Impertro, Julian F. Wienand, Sophie Häfele +6 more·Dec 22, 2022
In quantum gas microscopy experiments, reconstructing the site-resolved lattice occupation with high fidelity is essential for the accurate extraction of physical observables. For short interatomic separations and limited signal-to-noise ratio, this ...
Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement Learning
Zhikang Wang·Dec 21, 2022
With advances in digital technology in recent years, parallel computation utilizing GPUs has achieved remarkable efficiency, which has made it possible to use large-scale machine learning algorithms with large datasets, and deep learning, which is ma...
Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor
M. Moreira, G. Guerreschi, W. Vlothuizen +14 more·Dec 21, 2022
Artificial neural networks are becoming an integral part of digital solutions to complex problems. However, employing neural networks on quantum processors faces challenges related to the implementation of non-linear functions using quantum circuits....