Papers
Live trends in quantum computing research, updated daily from arXiv.
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Hardware platform mentions in abstracts — Photonic leads
wpScalable Quantum Neural Networks for Classification
Jindi Wu, Zeyi Tao, Qun Li·Aug 4, 2022
Many recent machine learning tasks resort to quantum computing to improve classification accuracy and training efficiency by taking advantage of quantum mechanics, known as quantum machine learning (QML). The variational quantum circuit (VQC) is freq...
Neural network accelerator for quantum control
David Xu, A. B. Özgüler, G. D. Guglielmo +4 more·Aug 4, 2022
Efficient quantum control is necessary for practical quantum computing implementations with current technologies. Conventional algorithms for determining optimal control parameters are computationally expensive, largely excluding them from use outsid...
Techniques for combining fast local decoders with global decoders under circuit-level noise
C. Chamberland, L. Gonçalves, P. Sivarajah +2 more·Aug 2, 2022
Implementing algorithms on a fault-tolerant quantum computer will require fast decoding throughput and latency times to prevent an exponential increase in buffer times between the applications of gates. In this work we begin by quantifying these requ...
NAPA: Intermediate-Level Variational Native-Pulse Ansatz for Variational Quantum Algorithms
Zhiding Liang, Jinglei Cheng, Hang Ren +8 more·Aug 2, 2022
Variational quantum algorithms (VQAs) have demonstrated great potentials in the noisy intermediate scale quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are iteratively updated to approximate the desired quantum states. We have s...
Domain-Specific Quantum Architecture Optimization
Wan-Hsuan Lin, Daniel Bochen Tan, M. Niu +2 more·Jul 29, 2022
With the steady progress in quantum computing over recent years, roadmaps for upscaling quantum processors have relied heavily on the targeted qubit architectures. So far, similarly to the early age of classical computing, these designs have been cra...
Quantum-inspired variational algorithms for partial differential equations: application to financial derivative pricing
Tianchen Zhao, Chuhao Sun, A. Cohen +2 more·Jul 22, 2022
Variational quantum Monte Carlo (VMC) combined with neural-network quantum states offers a novel angle of attack on the curse-of-dimensionality encountered in a particular class of partial differential equations (PDEs); namely, the real- and imaginar...
Machine Learning assisted excess noise suppression for continuous-variable quantum key distribution
Kexin Liang, Geng Chai, Zhengwen Cao +3 more·Jul 21, 2022
Excess noise is a major obstacle to high-performance continuous-variable quantum key distribution (CVQKD), which is mainly derived from the amplitude attenuation and phase fluctuation of quantum signals caused by channel instability. Here, an excess n...
Quantum neuron selection: finding high performing subnetworks with quantum algorithms
T. Whitaker·Jul 9, 2022
Gradient descent methods have long been the de facto standard for training deep neural networks. Millions of training samples are fed into models with billions of parameters, which are slowly updated over hundreds of epochs. Recently, it's been shown...
Knowledge Distillation in Quantum Neural Network using Approximate Synthesis
M. Alam, Satwik Kundu, Swaroop Ghosh·Jul 5, 2022
Recent assertions of a potential advantage of Quantum Neural Network (QNN) for specific Machine Learning (ML) tasks have sparked the curiosity of a sizable number of application researchers. The parameterized quantum circuit (PQC), a major building b...
Quantum Neural Network Compression
Zhirui Hu, Peiyan Dong, Zhepeng Wang +3 more·Jul 4, 2022
Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices. Recently, there are growing interest in variational quantum circuits (VQC), that is, a type of neural netw...
Rapid training of quantum recurrent neural networks
M. Siemaszko, A. Buraczewski, B. L. Saux +1 more·Jul 1, 2022
Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness recurrent neural networks (RNNs). However, while their predictions are quite accurate, their learning process is complex and, thus...
Learning quantum systems
Valentin Gebhart, R. Santagati, A. Gentile +7 more·Jul 1, 2022
The complexity of quantum systems increases exponentially with their size, but in many practical contexts there are assumptions (such as low rank, sparsity, or a specific type of expected dynamics) that enable classical algorithms to be efficient in ...
Neural network enhanced measurement efficiency for molecular groundstates
D. Iouchtchenko, J. Gonthier, A. Perdomo-Ortiz +1 more·Jun 30, 2022
It is believed that one of the first useful applications for a quantum computer will be the preparation of groundstates of molecular Hamiltonians. A crucial task involving state preparation and readout is obtaining physical observables of such states...
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization
Trong Duong, Sang T. Truong, Minh Tam +3 more·Jun 28, 2022
Quantum neural networks are promising for a wide range of applications in the Noisy Intermediate-Scale Quantum era. As such, there is an increasing demand for automatic quantum neural architecture search. We tackle this challenge by designing a quant...
Meta-learning digitized-counterdiabatic quantum optimization
P. Chandarana, Pablo Suárez Vieites, N. N. Hegade +3 more·Jun 20, 2022
The use of variational quantum algorithms for optimization tasks has emerged as a crucial application for the current noisy intermediate-scale quantum computers. However, these algorithms face significant difficulties in finding suitable ansatz and a...
Laziness, barren plateau, and noises in machine learning
Junyu Liu, Zexi Lin, Liang Jiang·Jun 19, 2022
We define laziness to describe a large suppression of variational parameter updates for neural networks, classical or quantum. In the quantum case, the suppression is exponential in the number of qubits for randomized variational quantum circuits. We...
Concentration of Data Encoding in Parameterized Quantum Circuits
Guangxi Li, Ruilin Ye, Xuanqiang Zhao +1 more·Jun 16, 2022
Variational quantum algorithms have been acknowledged as a leading strategy to realize near-term quantum advantages in meaningful tasks, including machine learning and combinatorial optimization. When applied to tasks involving classical data, such a...
Physics-Informed Neural Networks for Quantum Control.
A. Norambuena, M. Mattheakis, F. Gonz'alez +1 more·Jun 13, 2022
Quantum control is a ubiquitous research field that has enabled physicists to delve into the dynamics and features of quantum systems, delivering powerful applications for various atomic, optical, mechanical, and solid-state systems. In recent years,...
Exploring Accurate Potential Energy Surfaces via Integrating Variational Quantum Eigensolver with Machine Learning.
Yanxian Tao, Xiongzhi Zeng, Yi Fan +3 more·Jun 8, 2022
The potential energy surface (PES) is crucial for interpreting a variety of chemical reaction processes. However, predicting accurate PESs with high-level electronic structure methods is a challenging task due to the high computational cost. As an ap...
Predict better with less training data using a QNN
Barry D. Reese, M. Kowalik, Christian Metzl +2 more·Jun 8, 2022
Over the past decade, machine learning revolutionized vision-based quality assessment for which convolutional neural networks (CNNs) have now become the standard. In this paper, we consider a potential next step in this development and describe a qua...