Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs: (Invited Paper)
Zhepeng Wang, Zhiding Liang, Shangli Zhou +4 more·Sep 8, 2021
With the constant increase of the number of quantum bits (qubits) in the actual quantum computers, implementing and accelerating the prevalent deep learning on quantum computers are becoming possible. Along with this trend, there emerge quantum neura...
Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow (Invited Paper)
Zhiding Liang, Zhepeng Wang, Junhuan Yang +4 more·Sep 8, 2021
In the noisy intermediate-scale quantum (NISQ) era, one of the key questions is how to deal with the high noise level existing in physical quantum bits (qubits). Quantum error correction is promising but requires an extensive number (e.g., over 1,000...
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)
M. Alam, Satwik Kundu, R. Topaloglu +1 more·Sep 7, 2021
Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural ...
Medical image classification via quantum neural networks
Natansh Mathur, Jonas Landman, Yun Li +4 more·Sep 4, 2021
Machine Learning provides powerful tools for a variety of applications, including disease diagnosis through medical image classification. In recent years, quantum machine learning techniques have been put forward as a way to potentially enhance perfo...
A review of Quantum Neural Networks: Methods, Models, Dilemma
Ren-Xin Zhao, Shi Wang·Sep 4, 2021
The rapid development of quantum computer hardware has laid the hardware foundation for the realization of QNN. Due to quantum properties, QNN shows higher storage capacity and computational efficiency compared to its classical counterparts. This art...
On the effects of biased quantum random numbers on the initialization of artificial neural networks
R. Heese, M. Wolter, Sascha Mucke +2 more·Aug 30, 2021
Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum devices. A common property of quantum computers is...
Quantum Alphatron: quantum advantage for learning with kernels and noise
Siyi Yang, N. Guo, M. Santha +1 more·Aug 26, 2021
At the interface of machine learning and quantum computing, an important question is what distributions can be learned provably with optimal sample complexities and with quantum-accelerated time complexities. In the classical case, Klivans and Goel d...
Comparing concepts of quantum and classical neural network models for image classification task
R. Potempa, Sebastian Porębski·Aug 18, 2021
While quantum architectures are still under development, when available, they will only be able to process quantum data when machine learning algorithms can only process numerical data. Therefore, in the issues of classification or regression, it is ...
Neural Networks as Universal Probes of Many-Body Localization in Quantum Graphs
Cameron Beetar, Jeff Murugan, Dario Rosa·Aug 12, 2021
We show that a neural network, trained on the entanglement spectra of a nearest neighbor Heisenberg chain in a random transverse magnetic field, can be used to efficiently study the ergodic/many-body localized properties of a number of other quantum ...
Quantum reinforcement learning: the maze problem
Nicola Dalla Pozza, L. Buffoni, Stefano Martina +1 more·Aug 10, 2021
Quantum machine learning (QML) is a young but rapidly growing field where quantum information meets machine learning. Here, we will introduce a new QML model generalising the classical concept of reinforcement learning to the quantum domain, i.e. qua...
Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time‐Series
Annie E. Paine, V. Elfving, O. Kyriienko·Aug 6, 2021
A quantum algorithm is proposed for sampling from a solution of stochastic differential equations (SDEs). Using differentiable quantum circuits (DQCs) with a feature map encoding of latent variables, the quantile function is represented for an underl...
Hybrid Quantum-Classical Neural Network for Incident Detection
Zadid Khan, S. Khan, J. Tine +7 more·Aug 2, 2021
The effectiveness and dependability of real-time incident detection models directly impact the safety and operational conditions of the affected traffic routes. Recent advancements in cloud-based quantum computing infrastructure and developments in n...
Quantum convolutional neural network for classical data classification
Tak Hur, L. Kim, D. Park·Aug 2, 2021
With the rapid advance of quantum machine learning, several proposals for the quantum-analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark fully parameterized quantum convolutional neural networks (QCNNs) for classi...
RGB image classification with quantum convolutional ansatz
Yu Jing, Yang Yang, Chonghang Wu +4 more·Jul 23, 2021
With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of interest. Many quantum (convolutio...
Nonlinear transformation of complex amplitudes via quantum singular value transformation
N. Guo, K. Mitarai, K. Fujii·Jul 22, 2021
Due to the linearity of quantum operations, it is not straightforward to implement nonlinear transformations on a quantum computer, making some practical tasks like a neural network hard to achieve. In this paper, we define a task called and provide ...
Qsun: an open-source platform towards practical quantum machine learning applications
Quoc Chuong Nguyen, Le Bin Ho, Lan Nguyen Tran +1 more·Jul 22, 2021
Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine (QVM) that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before d...
Quantum Bayesian Neural Networks
N J Berner, Vincent Fortuin, Jonas Landman·Jul 20, 2021
Quantum machine learning promises great speedups over classical algorithms, but it often requires repeated computations to achieve a desired level of accuracy for its point estimates. Bayesian learning focuses more on sampling from posterior distribu...
A quantum algorithm for training wide and deep classical neural networks
Alexander Zlokapa, H. Neven, S. Lloyd·Jul 19, 2021
Given the success of deep learning in classical machine learning, quantum algorithms for traditional neural network architectures may provide one of the most promising settings for quantum machine learning. Considering a fully-connected feedforward n...
Fast suppression of classification error in variational quantum circuits
Bingzhi Zhang, Quntao Zhuang·Jul 16, 2021
Variational quantum circuits (VQCs) have shown great potential in near-term applications. However, the discriminative power of a VQC, in connection to its circuit architecture and depth, is not understood. To unleash the genuine discriminative power ...
Simulation of the five-qubit quantum error correction code on superconducting qubits
I. A. Simakov, I. S. Besedin, A. V. Ustinov·Jul 14, 2021
Experimental realization of stabilizer-based quantum error correction (QEC) codes that would yield superior logical qubit performance is one of the formidable task for state-of-the-art quantum processors. A major obstacle towards realizing this goal ...