Papers
Live trends in quantum computing research, updated daily from arXiv.
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27,548
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Research Volume
12,928 papers in 12 months (-5% vs prior quarter)
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Papers by research theme (12 months). Hover for details.
Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading
Maniraman Periyasamy, Marc Hölle, Marco Wiedmann +3 more·Apr 27, 2023
Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming. This challenge is further exacerbated in the case of batch RL, where the agent is trained solely on a pr...
Learning a Quantum Computer's Capability
Daniel Hothem, K. Young, Tommie A. Catanach +1 more·Apr 20, 2023
Accurately predicting a quantum computer's capability—which circuits it can run and how well it can run them—is a foundational goal of quantum characterization and benchmarking. As modern quantum computers become increasingly hard to simulate, we mus...
Sample-efficient Model-based Reinforcement Learning for Quantum Control
Irtaza Khalid, C. Weidner, E. Jonckheere +2 more·Apr 19, 2023
We propose a model-based reinforcement learning (RL) approach for noisy time-dependent gate optimization with improved sample complexity over model-free RL. Sample complexity is the number of controller interactions with the physical system. Leveragi...
Conditional generative models for learning stochastic processes
Salvatore Certo, Anh Pham, Nicolas Robles +1 more·Apr 19, 2023
A framework to learn a multi-modal distribution is proposed, denoted as the conditional quantum generative adversarial network (C-qGAN). The neural network structure is strictly within a quantum circuit and, as a consequence, is shown to represent a ...
Quantum machine learning for image classification
Arsenii Senokosov, Alexander Sedykh, A. Sagingalieva +2 more·Apr 18, 2023
Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the pri...
The END: An Equivariant Neural Decoder for Quantum Error Correction
E. Egorov, Roberto Bondesan, M. Welling·Apr 14, 2023
Quantum error correction is a critical component for scaling up quantum computing. Given a quantum code, an optimal decoder maps the measured code violations to the most likely error that occurred, but its cost scales exponentially with the system si...
Quantum Neural Network for Quantum Neural Computing
Min-Gang Zhou, Zhi-Ping Liu, Hua‐Lei Yin +3 more·Apr 14, 2023
Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quant...
Battle Against Fluctuating Quantum Noise: Compression-Aided Framework to Enable Robust Quantum Neural Network
Zhirui Hu, Youzuo Lin, Qiang Guan +1 more·Apr 10, 2023
Recently, we have been witnessing the scale-up of superconducting quantum computers; however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to leveraging the power of quantum computing. Although there exist error...
Neural-network-assisted quantum state and process tomography using limited data sets
Akshay Gaikwad, Omkar Bihani, Arvind +1 more·Apr 9, 2023
In this study we employ a feed-forward artificial neural network (FFNN) architecture to perform tomography of quantum states and processes obtained from noisy experimental data. To evaluate the performance of the FFNN, we use a heavily reduced data s...
Quantum Algorithms for Charged Particle Track Reconstruction in the LUXE Experiment
Arianna Crippa, L. Funcke, T. Hartung +8 more·Apr 4, 2023
The LUXE experiment is a new experiment in planning in Hamburg, which will study quantum electrodynamics at the strong-field frontier. LUXE intends to measure the positron production rate in this unprecedented regime using, among others, a silicon tr...
Variational Denoising for Variational Quantum Eigensolver
Quoc-Hoan Tran, Shinji Kikuchi, H. Oshima·Apr 2, 2023
The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems that are currently intractable on classical computers. VQE trains parameterized quantum circuits usi...
Quantum Deep Hedging
El Amine Cherrat, S. Raj, Iordanis Kerenidis +12 more·Mar 29, 2023
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets....
Stabilization and Dissipative Information Transfer of a Superconducting Kerr-Cat Qubit
Ufuk Korkmaz, Deniz Türkpençe·Mar 28, 2023
Today, the competition to build a quantum computer continues, and the number of qubits in hardware is increasing rapidly. However, the quantum noise that comes with this process reduces the performance of algorithmic applications, so alternative ways...
Trainable Variational Quantum-Multiblock ADMM Algorithm for Generation Scheduling
Reza Mahroo, A. Kargarian·Mar 28, 2023
The advent of quantum computing can potentially revolutionize how complex problems are solved. This paper proposes a two-loop quantum-classical solution algorithm for generation scheduling by infusing quantum computing, machine learning, and distribu...
A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models
Mohamed Hibat-Allah, M. Mauri, Juan Carrasquilla +1 more·Mar 27, 2023
Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in the near term. In this study, we build over an existing framework for ev...
Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC
Eric Wulff, M. Girone, D. Southwick +2 more·Mar 27, 2023
Training and Hyperparameter Optimization (HPO) of deep learning-based AI models are often compute resource intensive and calls for the use of large-scale distributed resources as well as scalable and resource efficient hyperparameter search algorithm...
Efficient site-resolved imaging and spin-state detection in dynamic two-dimensional ion crystals
R. Wolf, Joseph H. Pham, Julian Y. Z. Jee +2 more·Mar 20, 2023
Resolving the locations and discriminating the spin states of individual trapped ions with high fidelity is critical for a large class of applications in quantum computing, simulation, and sensing. We report on a method for high-fidelity state discri...
High-speed and energy-efficient non-volatile silicon photonic memory based on heterogeneously integrated memresonator
B. Tossoun, D. Liang, S. Cheung +4 more·Mar 10, 2023
Photonic integrated circuits have grown as potential hardware for neural networks and quantum computing, yet the tuning speed and large power consumption limited the application. Here, authors introduce the memresonator, a memristor heterogeneously i...
Variational Quantum Neural Networks (VQNNS) in Image Classification
Meghashrita Das, T. Bolisetti·Mar 10, 2023
Quantum machine learning has established as an interdisciplinary field to overcome limitations of classical machine learning and neural networks. This is a field of research which can prove that quantum computers are able to solve problems with compl...
Variational Quantum Eigensolver for Classification in Credit Sales Risk
Joanna Wi'sniewska, Marek Sawerwain·Mar 5, 2023
The data classification task is broadly utilized in numerous fields of science and it may be realized by different known approaches (e.g. neural networks). However, in this work, quantum computations were harnessed to solve the problem. We take into ...