Papers
Live trends in quantum computing research, updated daily from arXiv.
Total Papers
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
Backpropagation scaling in parameterised quantum circuits
Joseph Bowles, David Wierichs, Chae-Yeun Park·Jun 26, 2023
The discovery of the backpropagation algorithm ranks among one of the most important moments in the history of machine learning, and has made possible the training of large-scale neural networks through its ability to compute gradients at roughly the...
Deep Bayesian experimental design for quantum many-body systems
Leopoldo Sarra, F. Marquardt·Jun 26, 2023
Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a mo...
Variational quantum algorithms for machine learning: theory and applications
Stefano Mangini·Jun 16, 2023
This Ph.D. thesis provides a comprehensive review of the state-of-the-art in the field of Variational Quantum Algorithms and Quantum Machine Learning, including numerous original contributions. The first chapters are devoted to a brief summary of qua...
Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
Koki Chinzei, Q. Tran, Kazunori Maruyama +2 more·Jun 12, 2023
The quantum convolutional neural network (QCNN) is a promising quantum machine learning (QML) model that is expected to achieve quantum advantages in classically intractable problems. However, the QCNN requires a large number of measurements for data...
Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
Jonas Stein, Michael Poppel, P. Adamczyk +9 more·Jun 8, 2023
Surrogate models are ubiquitously used in industry and academia to efficiently approximate given black box functions. As state-of-the-art methods from classical machine learning frequently struggle to solve this problem accurately for the often scarc...
Validating an algebraic approach to characterizing resonator networks
V. R. Horowitz, B. Carter, Uriel Hernandez +2 more·Jun 2, 2023
Resonator networks are ubiquitous in natural and engineered systems, such as solid-state materials, electrical circuits, quantum processors, and even neural tissue. To understand and manipulate these networks it is essential to characterize their bui...
Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning
V. Skavysh, Sofia Priazhkina, Diego Guala +1 more·Jun 1, 2023
Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. We are the first to study whether Quantum Monte Carlo (QMC) algorithm can improve the runtime of economic applications and challenge...
A general-purpose single-photon-based quantum computing platform
N. Maring, A. Fyrillas, M. Pont +24 more·Jun 1, 2023
Quantum computing aims at exploiting quantum phenomena to efficiently perform computations that are unfeasible even for the most powerful classical supercomputers. Among the promising technological approaches, photonic quantum computing offers the ad...
Quantum convolutional neural networks for multi-channel supervised learning
Anthony M. Smaldone, Gregory W. Kyro, Victor S. Batista·May 30, 2023
As the rapidly evolving field of machine learning continues to produce incredibly useful tools and models, the potential for quantum computing to provide speed up for machine learning algorithms is becoming increasingly desirable. In particular, quan...
A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer
Hongyi Pan, Xin Zhu, S. Atici +1 more·May 27, 2023
In this paper, we propose a novel Hadamard Transform (HT)-based neural network layer for hybrid quantum-classical computing. It implements the regular convolutional layers in the Hadamard transform domain. The idea is based on the HT convolution theo...
A Scalable, Fast and Programmable Neural Decoder for Fault-Tolerant Quantum Computation Using Surface Codes
Mengyu Zhang, Xiangyu Ren, Guanglei Xi +6 more·May 25, 2023
Quantum error-correcting codes (QECCs) can eliminate the negative effects of quantum noise, the major obstacle to the execution of quantum algorithms. However, realizing practical quantum error correction (QEC) requires resolving many challenges to i...
Neural-network-designed three-qubit gates robust against charge noise and crosstalk in silicon
David W. Kanaar, J. Kestner·May 22, 2023
Spin qubits in semiconductor quantum dots are a promising platform for quantum computing, however, scaling to large systems is hampered by crosstalk and charge noise. Crosstalk here refers to the unwanted off-resonant rotation of idle qubits during t...
Deep Quantum Neural Networks are Gaussian Process
A. Rad·May 22, 2023
The overparameterization of variational quantum circuits, as a model of Quantum Neural Networks (QNN), not only improves their trainability but also serves as a method for evaluating the property of a given ansatz by investigating their kernel behavi...
Predictive Models from Quantum Computer Benchmarks
Daniel Hothem, Jordan Hines, Karthik Nataraj +2 more·May 15, 2023
Holistic benchmarks for quantum computers are essential for testing and summarizing the performance of quantum hardware. However, holistic benchmarks-such as algorithmic or randomized benchmarks-typically do not predict a processor's performance on c...
A Full Quantum Generative Adversarial Network Model for High Energy Physics Simulations
F. Rehm, S. Vallecorsa, M. Grossi +2 more·May 12, 2023
The prospect of quantum computing with a potential exponential speed-up compared to classical computing identifies it as a promising method in the search for alternative future High Energy Physics (HEP) simulation approaches. HEP simulations, such as...
Parallel hybrid quantum-classical machine learning for kernelized time-series classification
J. Baker, Gilchan Park, Kwangmin Yu +3 more·May 10, 2023
Supervised time-series classification garners widespread interest because of its applicability throughout a broad application domain including finance, astronomy, biosensors, and many others. In this work, we tackle this problem with hybrid quantum-c...
Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
Alireza Furutanpey, Johanna Barzen, Marvin Bechtold +4 more·May 9, 2023
Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs will soon be possible everywhere. Existing work has yet to draw from research in edge computing to explore systems...
The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs
Muhammad Kashif, S. Al-kuwari·May 8, 2023
Recent advances in quantum computing and machine learning have brought about a promising intersection of these two fields, leading to the emergence of quantum machine learning (QML). However, the integration of quantum computing and machine learning ...
Statistical learning on randomized data to verify quantum state approximate k-designs
Kaustav Mukherjee, Sarah Chehade, Lorenzo Versini +3 more·May 2, 2023
Random ensembles of pure states have proven to be extremely important in various aspects of quantum physics such as benchmarking the performance of quantum circuits, testing for quantum advantage, providing novel insights for many-body thermalization...
Quantum Fourier Iterative Amplitude Estimation
J. J. M. D. Lejarza, M. Grossi, L. Cieri +1 more·May 2, 2023
Monte Carlo integration is a widely used numerical method for approximating integrals, which is often computationally expensive. In recent years, quantum computing has shown promise for speeding up Monte Carlo integration, and several quantum algorit...