Papers
Live trends in quantum computing research, updated daily from arXiv.
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13,008 papers in 12 months (-3% vs prior quarter)
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Entanglement Forging with generative neural network models
Patrick Huembeli, Giuseppe Carleo, Antonio Mezzacapo·May 2, 2022
The optimal use of quantum and classical computational techniques together is important to address problems that cannot be easily solved by quantum computations alone. This is the case of the ground state problem for quantum many-body systems. We sho...
BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithms
Ankit Kulshrestha, Ilya Safro·Apr 28, 2022
Barren plateaus are a notorious problem in the optimization of variational quantum algorithms and pose a critical obstacle in the quest for more efficient quantum machine learning algorithms. Many potential reasons for barren plateaus have been ident...
Quantum-classical convolutional neural networks in radiological image classification
A. Matic, Maureen Monnet, J. Lorenz +2 more·Apr 26, 2022
Quantum machine learning is receiving significant attention currently, but its usefulness in comparison to classical machine learning techniques for practical applications remains unclear. However, there are indications that certain quantum machine l...
New Aspects of Optical Coherence and Their Potential for Quantum Technologies
N. Miller·Apr 19, 2022
Currently, optical technology impacts most of our lives, from light used in scientific measurement to the fiber optic cables that makeup the backbone of the internet. However, as our current optical infrastructure grows, we discover that these techno...
Optimizing Tensor Network Contraction Using Reinforcement Learning
E. Meirom, Haggai Maron, Shie Mannor +1 more·Apr 18, 2022
Quantum Computing (QC) stands to revolutionize computing, but is currently still limited. To develop and test quantum algorithms today, quantum circuits are often simulated on classical computers. Simulating a complex quantum circuit requires computi...
Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?
Mohammad Masum, Mohammad Nazim, Md Jobair Hossain Faruk +8 more·Apr 4, 2022
Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises-quadratic or exponential i...
Experimental quantum adversarial learning with programmable superconducting qubits
W. Ren, Weikang Li, Shibo Xu +21 more·Apr 4, 2022
Quantum computing promises to enhance machine learning and artificial intelligence. However, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from adversa...
Classification of NEQR Processed Classical Images using Quantum Neural Networks (QNN)
Santanu Ganguly·Mar 29, 2022
- A quantum neural network (QNN) is interpreted today as any quantum circuit with trainable continuous parameters. This work builds on previous works by the authors and addresses QNN for image classification with Novel Enhanced Quantum Representation...
Unentangled quantum reinforcement learning agents in the OpenAI Gym
Jeng-Yueh Hsiao, Yuxuan Du, Wei-Yin Chiang +2 more·Mar 27, 2022
Classical reinforcement learning (RL) has generated excellent results in different regions ; however, its sample inefficiency remains a critical issue. In this paper, we provide concrete numerical evidence that the sample efficiency (the speed of converge...
New quantum neural network designs
Felix Petitzon·Mar 12, 2022
Quantum computers promise improving machine learning. We investigated the performance of new quantum neural network designs. Quantum neural networks currently employed rely on a feature map to encode the input into a quantum state. This state is then...
Quantum neural networks force fields generation
Oriel Kiss, F. Tacchino, S. Vallecorsa +1 more·Mar 9, 2022
Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning (ML) methods have demonstrated impressive performances in predicting a...
Quantum algorithm for neural network enhanced multi-class parallel classification
Anqi Zhang, Xiaoyun He, Sheng-mei Zhao·Mar 8, 2022
Using the properties of quantum superposition, we propose a quantum classification algorithm to efficiently perform multi-class classification tasks, where the training data are loaded into parameterized operators which are applied to the basis of th...
QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning
Hanrui Wang, Zi-Chen Li, Jiaqi Gu +3 more·Feb 26, 2022
Parameterized Quantum Circuits (PQC) are drawing increasing research interest thanks to its potential to achieve quantum advantages on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable PQC learning, the training...
Quantum Deep Reinforcement Learning for Robot Navigation Tasks
Hans Hohenfeld, D. Heimann, Felix Wiebe +1 more·Feb 24, 2022
We utilize hybrid quantum deep reinforcement learning to learn navigation tasks for a simple, wheeled robot in simulated environments of increasing complexity. For this, we train parameterized quantum circuits (PQCs) with two different encoding strat...
Simple, Reliable, and Noise-Resilient Continuous-Variable Quantum State Tomography with Convex Optimization
Ingrid Strandberg·Feb 23, 2022
Precise reconstruction of unknown quantum states from measurement data, a process commonly called quantum state tomography, is a crucial component in the development of quantum information processing technologies. Many different tomography methods hav...
Completely Quantum Neural Networks
Steve Abel, J. C. Criado, M. Spannowsky·Feb 23, 2022
Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to embed and train a general neural network in a quantum annealer without introducing any classical el-ement in training. To implement the network on a sta...
Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
Ra'ul Berganza G'omez, Corey O’Meara, G. Cortiana +2 more·Feb 16, 2022
The learning process of classical machine learning algorithms is tuned by hyperparameters that need to be customized to best learn and generalize from an input dataset. In recent years, Quantum Machine Learning (QML) has been gaining traction as a po...
Quantum Lazy Training
E. Abedi, Salman Beigi, Leila Taghavi·Feb 16, 2022
In the training of over-parameterized model functions via gradient descent, sometimes the parameters do not change significantly and remain close to their initial values. This phenomenon is called lazy training and motivates consideration of the line...
Neural-Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs
Ramon W. J. Overwater, M. Babaie, F. Sebastiano·Feb 11, 2022
Quantum error correction (QEC) is required in quantum computers to mitigate the effect of errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most computationally expensive task in the classical electro...
Self-correcting quantum many-body control using reinforcement learning with tensor networks
F. Metz, M. Bukov·Jan 27, 2022
Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body syst...