Papers
Live trends in quantum computing research, updated daily from arXiv.
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QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Kaixiong Zhou, Zhenyu (Allen) Zhang, Sheng-Wei Chen +4 more·Nov 9, 2022
Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advantages. Despite lots of efforts developed in computer vision domain, on...
A Quantum-Powered Photorealistic Rendering
Yuanfu Yang, Min Sun·Nov 7, 2022
Achieving photorealistic rendering of real-world scenes poses a significant challenge with diverse applications, including mixed reality and virtual reality. Neural networks, extensively explored in solving differential equations, have previously bee...
Low-overhead quantum error-correction codes with a cyclic topology
I. A. Simakov, I. Besedin·Nov 6, 2022
Quantum error correction is an important ingredient for scalable quantum computing. Stabilizer codes are one of the most promising and straightforward ways to correct quantum errors, are convenient for logical operations, and improve performance with...
Toward Neural Network Simulation of Variational Quantum Algorithms
Oliver Knitter, J. Stokes, S. Veerapaneni·Nov 5, 2022
Variational quantum algorithms (VQAs) utilize a hybrid quantum–classical architecture to recast problems of high-dimensional linear algebra as ones of stochastic optimization. Despite the promise of leveraging near- to intermediate-term quantum resou...
Quantum Deep Dreaming: A Novel Approach for Quantum Circuit Design
Romi Lifshitz·Nov 5, 2022
One of the challenges currently facing the quantum computing community is the design of quantum circuits which can efficiently run on near-term quantum computers, known as the quantum compiling problem. Algorithms such as the Variational Quantum Eigens...
Reservoir Computing via Quantum Recurrent Neural Networks
Samuel Yen-Chi Chen, D. Fry, Amol Deshmukh +2 more·Nov 4, 2022
Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with high scientific and commercial value. Existing VQC or QNN-based method...
Analog Quantum Variational Embedding Classifier
Rui Yang, Samuel Bosch, B. Kiani +2 more·Nov 4, 2022
Quantum machine learning has the potential to provide powerful algorithms for artificial intelligence. The pursuit of quantum advantage in quantum machine learning is an active area of research. For current noisy, intermediate-scale quantum (NISQ) co...
Quantum Similarity Testing with Convolutional Neural Networks.
Yadong Wu, Yan Zhu, Ge Bai +2 more·Nov 3, 2022
The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous variable quantum systems. In this Lette...
Pulse-efficient quantum machine learning
A. Melo, Nathan Earnest-Noble, F. Tacchino·Nov 2, 2022
Quantum machine learning algorithms based on parameterized quantum circuits are promising candidates for near-term quantum advantage. Although these algorithms are compatible with the current generation of quantum processors, device noise limits thei...
Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
K. Reuer, Jonas Landgraf, T. Fosel +10 more·Oct 30, 2022
To realize the full potential of quantum technologies, finding good strategies to control quantum information processing devices in real time becomes increasingly important. Usually these strategies require a precise understanding of the device itsel...
TorchQuantum Case Study for Robust Quantum Circuits (Invited Paper)
Hanrui Wang, Pengyu Liu, Jinglei Cheng +10 more·Oct 29, 2022
Quantum Computing has attracted much research attention because of its potential to achieve fundamental speed and efficiency improvements in various domains. Among different quantum algorithms, Parameterized Quantum Circuits (PQC) for Quantum Machine...
Implementing arbitrary quantum operations via quantum walks on a cycle graph
Jia-Yi Lin, Xinyou Li, Yusheng Shao +2 more·Oct 26, 2022
The quantum circuit model is the most commonly used model for implementing quantum computers and quantum neural networks whose essential tasks are to realize certain unitary operations. Here we propose an alternative approach; we use a simple discret...
Hierarchical quantum circuit representations for neural architecture search
Matt Lourens, I. Sinayskiy, D. Park +2 more·Oct 26, 2022
Quantum circuit algorithms often require architectural design choices analogous to those made in constructing neural and tensor networks. These tend to be hierarchical, modular and exhibit repeating patterns. Neural Architecture Search (NAS) attempts...
Quantum Deep Recurrent Reinforcement Learning
Samuel Yen-Chi Chen·Oct 26, 2022
Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of quantum machine learning (QML). Reinforcement learning (RL) is one of the ML paradigms which can be used to solve complex seque...
Deep Neural Networks as the Semi-classical Limit of Topological Quantum Neural Networks: The problem of generalisation
A. Marcianò, De-Wei Chen, Filippo Fabrocini +3 more·Oct 25, 2022
Deep Neural Networks miss a principled model of their operation. A novel framework for supervised learning based on Topological Quantum Field Theory that looks particularly well suited for implementation on quantum processors has been recently explor...
Track reconstruction at the LUXE experiment using quantum algorithms
Arianna Crippa, L. Funcke, T. Hartung +8 more·Oct 24, 2022
LUXE (Laser Und XFEL Experiment) is a proposed experiment at DESY which will study Quantum Electrodynamics (QED) in the strong-field regime, where QED becomes non-perturbative. Measuring the rate of created electron-positron pairs using a silicon pix...
Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning
Giuseppe Scriva, Emanuele Costa, B. McNaughton +1 more·Oct 20, 2022
Adiabatic quantum computers, such as the quantum annealers commercialized by D-Wave Systems Inc., are routinely used to tackle combinatorial optimization problems. In this article, we show how to exploit them to accelerate equilibrium Markov chain Mo...
Extending Graph Transformers with Quantum Computed Aggregation
Slimane Thabet, Romain Fouilland, L. Henriet·Oct 19, 2022
Recently, efforts have been made in the community to design new Graph Neural Networks (GNN), as limitations of Message Passing Neural Networks became more apparent. This led to the appearance of Graph Transformers using global graph features such as ...
3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications
Hankyul Baek, Won Joon Yun, Joongheon Kim·Oct 18, 2022
With the beginning of the noisy intermediate-scale quantum (NISQ) era, a quantum neural network (QNN) has recently emerged as a solution for several specific problems that classical neural networks cannot solve. Moreover, a quantum convolutional neur...
TopGen: Topology-Aware Bottom-Up Generator for Variational Quantum Circuits
Jinglei Cheng, Hanrui Wang, Zhiding Liang +3 more·Oct 15, 2022
Variational Quantum Algorithms (VQA) are promising to demonstrate quantum advantages on near-term devices. Designing ansatz, a variational circuit with parameterized gates, is of paramount importance for VQA as it lays the foundation for parameter op...