Papers
Live trends in quantum computing research, updated daily from arXiv.
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Hardware platform mentions in abstracts — Photonic leads
On Assessing the Quantum Advantage for MaxCut Provided by Quantum Neural Network Ans\"atze
Juneseo Lee·May 11, 2021
In this work we design a class of Ansätze to solve MaxCut on a parameterized quantum circuit (PQC). Gaining inspiration from properties of quantum optimal control landscapes, we consider the presence of optimization traps as a measure of complexity f...
Entangling Quantum Generative Adversarial Networks.
M. Niu, Alexander Zlokapa, M. Broughton +4 more·Apr 30, 2021
Generative adversarial networks (GANs) are one of the most widely adopted machine learning methods for data generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (an entangling quantum GAN, EQ-GAN...
Exploiting degeneracy in belief propagation decoding of quantum codes
Kao-Yueh Kuo, C. Lai·Apr 28, 2021
Quantum information needs to be protected by quantum error-correcting codes due to imperfect physical devices and operations. One would like to have an efficient and high-performance decoding procedure for the class of quantum stabilizer codes. A pot...
Efficient Measure for the Expressivity of Variational Quantum Algorithms.
Yuxuan Du, Zhuozhuo Tu, Xiao Yuan +1 more·Apr 20, 2021
The superiority of variational quantum algorithms (VQAs) such as quantum neural networks (QNNs) and variational quantum eigensolvers (VQEs) heavily depends on the expressivity of the employed Ansätze. Namely, a simple Ansatz is insufficient to captur...
A quantum convolutional neural network on NISQ devices
Shijie Wei, Yanhu Chen, Zeng-rong Zhou +1 more·Apr 14, 2021
Quantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks (CNN), which...
Training Quantum Neural Networks on NISQ Devices
Kerstin Beer, D. List, Gabriel Muller +2 more·Apr 13, 2021
The advent of noisy intermediate-scale quantum (NISQ) devices offers crucial opportunities for the development of quantum algorithms. Here we evaluate the noise tolerance of two quantum neural network (QNN) architectures on IBM's NISQ devices, namely...
On quantum neural networks
A.A. Ezhov·Apr 12, 2021
The early definition of a quantum neural network as a new field that combines the classical neurocomputing with quantum computing was rather vague and satisfactory in the 2000s. The widespread in 2020 modern definition of a quantum neural network as ...
Quantum Machine Learning for Power System Stability Assessment
Yifan Zhou, Peng Zhang·Apr 10, 2021
Transient stability assessment (TSA) is a cornerstone for resilient operations of today's interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA chall...
Quantum-enhanced filter: QFilter
Parfait Atchade-Adelomou, Guillermo Alonso-Linaje·Apr 7, 2021
Convolutional neural networks (CNN) are used mainly to treat problems with many images characteristic of deep learning. In this work, we propose a hybrid image classification model to take advantage of quantum and classical computing. The method will...
Avoiding local minima in Variational Quantum Algorithms with Neural Networks
Javier Rivera-Dean, Patrick Huembeli, A. Ac'in +1 more·Apr 7, 2021
Variational Quantum Algorithms have emerged as a leading paradigm for near-term quantum computation. In such algorithms, a parameterized quantum circuit is controlled via a classical optimization method that seeks to minimize a problem-dependent cost...
Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
Andrea Skolik, S. Jerbi, V. Dunjko·Mar 28, 2021
Quantum machine learning (QML) has been identified as one of the key fields that could reap advantages from near-term quantum devices, next to optimization and quantum chemistry. Research in this area has focused primarily on variational quantum algori...
QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity
S. Stein, Y. Mao, Betis Baheri +5 more·Mar 21, 2021
In the past decade, remarkable progress has been achieved in deep learning related systems and applications. In the post Moore's Law era, however, the limit of semiconductor fabrication technology along with the increasing data size have slowed down ...
Quantum machine learning of graph-structured data
Kerstin Beer, Megha Khosla, Julius Kohler +1 more·Mar 19, 2021
Graph structures are ubiquitous throughout the natural sciences. Here we consider graph-structured quantum data and describe how to carry out its quantum machine learning via quantum neural networks. In particular, we consider training data in the fo...
Tomography of time-dependent quantum spin networks with machine learning
Chen-Di Han, Bryan Glaz, M. Haile +1 more·Mar 15, 2021
Interacting quantum Hamiltonians are fundamental to quantum computing. Data-based tomography of timeindependent quantum Hamiltonians has been achieved, but an open challenge is to ascertain the structures of time-dependent quantum Hamiltonians using ...
Quantum circuit optimization with deep reinforcement learning
T. Fosel, M. Niu, F. Marquardt +1 more·Mar 13, 2021
A central aspect for operating future quantum computers is quantum circuit optimization, i.e., the search for efficient realizations of quantum algorithms given the device capabilities. In recent years, powerful approaches have been developed which f...
Neural predictor based quantum architecture search
Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang +1 more·Mar 11, 2021
Variational quantum algorithms (VQAs) are widely speculated to deliver quantum advantages for practical problems under the quantum–classical hybrid computational paradigm in the near term. Both theoretical and practical developments of VQAs share man...
Parametrized Quantum Policies for Reinforcement Learning
S. Jerbi, Casper Gyurik, Simon Marshall +2 more·Mar 9, 2021
With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system is gaining increasing traction. Such hybrid systems have already shown ...
Machine Learning the period finding algorithm
J. Francis, Anil Shaji·Mar 9, 2021
We use differentiable programming and gradient descent to find unitary matrices that can be used in the period finding algorithm to extract period information from the state of a quantum computer post application of the oracle. The standard procedure...
Hardware error correction for programmable photonics
S. Bandyopadhyay, R. Hamerly, D. Englund·Mar 8, 2021
Programmable photonic circuits of reconfigurable interferometers can be used to implement arbitrary operations on optical modes, facilitating a flexible platform for accelerating tasks in quantum simulation, signal processing, and artificial intellig...
Entangled q-convolutional neural nets
V. Anagiannis, Miranda C. N. Cheng·Mar 6, 2021
We introduce a machine learning model, the q-CNN model, sharing key features with convolutional neural networks and admitting a tensor network description. As examples, we apply q-CNN to the MNIST and Fashion MNIST classification tasks. We explain ho...