Papers
Live trends in quantum computing research, updated daily from arXiv.
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Quaternary Neural Belief Propagation Decoding of Quantum LDPC Codes With Overcomplete Check Matrices
Sisi Miao, A. Schnerring, Haizheng Li +1 more·Aug 16, 2023
Quantum low-density parity-check (QLDPC) codes are promising candidates for error correction in quantum computers. One of the major challenges in implementing QLDPC codes in quantum computers is the lack of a universal decoder. In this work, we first...
Randomness-Enhanced Expressivity of Quantum Neural Networks.
Yadong Wu, Juan Yao, P. Zhang +1 more·Aug 9, 2023
As a hybrid of artificial intelligence and quantum computing, quantum neural networks (QNNs) have gained significant attention as a promising application on near-term, noisy intermediate-scale quantum devices. Conventional QNNs are described by param...
Neural-network-encoded variational quantum algorithms
Jiaqi Miao, Chang-Yu Hsieh, Shenmin Zhang·Aug 2, 2023
We introduce a general framework called neural network (NN) encoded variational quantum algorithms (VQAs), or NN-VQA for short, to address the challenges of implementing VQAs on noisy intermediate-scale quantum (NISQ) computers. Specifically, NN-VQA ...
Scalable quantum measurement error mitigation via conditional independence and transfer learning
Chang-Woo Lee, D. Park·Aug 1, 2023
Mitigating measurement errors in quantum systems without relying on quantum error correction is of critical importance for the practical development of quantum technology. Deep learning-based quantum measurement error mitigation (QMEM) has exhibited ...
Toward Quantum Machine Translation of Syntactically Distinct Languages
M. Abbaszade, Mariam Zomorodi, V. Salari +1 more·Jul 31, 2023
The present study aims to explore the feasibility of language translation using quantum natural language processing algorithms on noisy intermediate-scale quantum (NISQ) devices. Classical methods in natural language processing (NLP) struggle with ha...
Enhanced quantum state preparation via stochastic predictions of neural networks
C. Li, Runhong He, Zhao-Ming Wang·Jul 27, 2023
In pursuit of enhancing the predication capabilities of the neural network, it has been a longstanding objective to create dataset encompassing a diverse array of samples. The purpose is to broaden the horizons of neural network and continually striv...
Dissipative learning of a quantum classifier
Ufuk Korkmaz, Denz Türkpençe·Jul 23, 2023
The expectation that quantum computation might bring performance advantages in machine learning algorithms motivates the work on the quantum versions of artificial neural networks. In this study, we analyse the learning dynamics of a quantum classifi...
MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-Classification
Jindi Wu, Tianjie Hu, Qun Li·Jul 21, 2023
Quantum computing has shown considerable promise for compute-intensive tasks in recent years. For instance, classification tasks based on quantum neural networks (QNN) have garnered significant interest from researchers and have been evaluated in var...
A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction
Fr'ed'eric Marcotte, Pierre-Antoine Mouny, Victor Yon +6 more·Jul 18, 2023
Neural decoders for quantum error correction (QEC) rely on neural networks to classify syndromes extracted from error correction codes and find appropriate recovery operators to protect logical information against errors. Its ability to adapt to hard...
qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers
Han-Yu Cao, Feng Pan, Yijia Wang +1 more·Jul 18, 2023
We propose a general framework for decoding quantum error-correcting codes with generative modeling. The model utilizes autoregressive neural networks, specifically Transformers, to learn the joint probability of logical operators and syndromes. This...
Information-driven Nonlinear Quantum Neuron
Ufuk Korkmaz, Deniz Turkpencce·Jul 18, 2023
The promising performance increase offered by quantum computing has led to the idea of applying it to neural networks. Studies in this regard can be divided into two main categories: simulating quantum neural networks with the standard quantum circui...
Quantum circuit autoencoder
Jun-Yi Wu, Hao Fu, Mingzheng Zhu +3 more·Jul 17, 2023
Quantum autoencoder is a quantum neural network model for compressing information stored in quantum states. However, one needs to process information stored in quantum circuits for many tasks in the emerging quantum information technology. In this wo...
A Quantum Convolutional Neural Network Approach for Object Detection and Classification
Gowri Namratha Meedinti, Kandukuri Sai Srirekha, R. Delhibabu·Jul 17, 2023
This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasi...
QDoor: Exploiting Approximate Synthesis for Backdoor Attacks in Quantum Neural Networks
Cheng Chu, Fan Chen, P. Richerme +1 more·Jul 13, 2023
Quantum neural networks (QNNs) succeed in object recognition, natural language processing, and financial analysis. To maximize the accuracy of a QNN on a Noisy Intermediate Scale Quantum (NISQ) computer, approximate synthesis modifies the QNN circuit...
Error-tolerant quantum convolutional neural networks for symmetry-protected topological phases
Petr Zapletal, Nathan A. McMahon, M. Hartmann·Jul 7, 2023
The analysis of noisy quantum states prepared on current quantum computers is getting beyond the capabilities of classical computing. Quantum neural networks based on parametrized quantum circuits, measurements and feed-forward can process large amou...
A Hybrid Quantum-Classical Generative Adversarial Network for Near-Term Quantum Processors
Albha O’Dwyer Boyle, R. Nikandish·Jul 6, 2023
In this article, we present a hybrid quantum-classical generative adversarial network (GAN) for near-term quantum processors. The hybrid GAN comprises a variational generator and a discriminator quantum neural network, which are trained using a class...
Data-driven decoding of quantum error correcting codes using graph neural networks
Moritz Lange, Pontus Havström, Basudha Srivastava +6 more·Jul 3, 2023
To leverage the full potential of quantum error-correcting stabilizer codes it is crucial to have an efficient and accurate decoder. Accurate, maximum likelihood, decoders are computationally very expensive whereas decoders based on more efficient al...
Quantum data learning for quantum simulations in high-energy physics
Lento Nagano, Alexander Miessen, Tamiya Onodera +3 more·Jun 29, 2023
Quantum machine learning with parametrised quantum circuits has attracted significant attention over the past years as an early application for the era of noisy quantum processors. However, the possibility of achieving concrete advantages over classi...
Quantum Fourier networks for solving parametric PDEs
Nishant Jain, Jonas Landman, Natansh Mathur +1 more·Jun 27, 2023
Many real-world problems, like modelling environment dynamics, physical processes, time series etc involve solving partial differential equations (PDEs) parameterised by problem-specific conditions. Recently, a deep learning architecture called Fouri...
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...