Papers
Live trends in quantum computing research, updated daily from arXiv.
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Hardware platform mentions in abstracts — Photonic leads
Identification of quantum entanglement with Siamese convolutional neural networks and semisupervised learning
J. Pawłowski, Mateusz Krawczyk·Oct 13, 2022
Quantum entanglement is a fundamental property commonly used in various quantum information protocols and algorithms. Nonetheless, the problem of identifying entanglement has still not reached a general solution for systems larger than $2\times3$. In...
Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation
S. Humeniuk, Y. Wan, Lei Wang·Oct 12, 2022
Direct sampling from a Slater determinant is combined with an autoregressive deep neural network as a Jastrow factor into a fully autoregressive Slater-Jastrow ansatz for variational quantum Monte Carlo, which allows for uncorrelated sampling. The el...
QuCNN: A Quantum Convolutional Neural Network with Entanglement Based Backpropagation
S. Stein, Y. Mao, James Ang +1 more·Oct 11, 2022
Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push forward this trend, and demonstr...
Investigation of Early-Stage Breast Cancer Detection using Quantum Neural Network
Amjad Y. Sahib, Muazez Al Ali, Musaddiq Al Ali·Oct 8, 2022
aided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and ...
Automated Synthesis of Quantum Circuits using Neural Network
Kentaro Murakami, Jianjun Zhao·Oct 6, 2022
While the ability to build quantum computers is improving dramatically, developing quantum algorithms is very limited and relies on human insight and ingenuity. Although several quantum programming languages have been developed, it is challenging for...
Probabilistic partition of unity networks for high‐dimensional regression problems
Tiffany Fan, N. Trask, M. D'Elia +1 more·Oct 6, 2022
We explore the probabilistic partition of unity network (PPOU‐Net) model in the context of high‐dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction. With the proposed framework, the target fun...
Interpreting convolutional neural networks' low-dimensional approximation to quantum spin systems
Yilong Ju, S. Alam, Jonathan Minoff +3 more·Oct 3, 2022
Convolutional neural networks (CNNs) have been employed along with variational Monte Carlo methods for finding the ground state of quantum many-body spin systems with great success. However, it remains uncertain how CNNs, with a model complexity that...
Efficient solutions of fermionic systems using artificial neural networks
E. Nordhagen, Jane M. Kim, Bryce Fore +2 more·Oct 1, 2022
In this study, we explore the similarities and differences between variational Monte Carlo techniques that employ conventional and artificial neural network representations of the ground-state wave function for fermionic systems. Our primary focus is...
Accelerating the Training of Single Layer Binary Neural Networks using the HHL Quantum Algorithm
S. L. Alarcón, Cory E. Merkel, Martin Hoffnagle +2 more·Oct 1, 2022
Binary Neural Networks are a promising technique for implementing efficient deep models with reduced storage and computational requirements. The training of these is however, still a compute-intensive problem that grows drastically with the layer siz...
On physics-informed neural networks for quantum computers
S. Markidis·Sep 28, 2022
Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks. One of the advantages of using PINN is to leverage t...
quEEGNet: Quantum AI for Biosignal Processing
T. Koike-Akino, Ye Wang·Sep 27, 2022
In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specifically, we propose a hybrid quantum-classical neural network model that integrates...
Scalable Quantum Convolutional Neural Networks
Hankyul Baek, Won Joon Yun, Joongheon Kim·Sep 26, 2022
With the beginning of the noisy intermediate-scale quantum (NISQ) era, quantum neural network (QNN) has recently emerged as a solution for the problems that classical neural networks cannot solve. Moreover, QCNN is attracting attention as the next ge...
Automatic and Effective Discovery of Quantum Kernels
Massimiliano Incudini, Daniele Lizzio Bosco, F. Martini +3 more·Sep 22, 2022
Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data. Quantum kernels are able to capture relationships in the data that are not efficiently co...
Learning Fourier series with parametrized quantum circuits
D. Heimann, Hans Hohenfeld, Gunnar Schönhoff +2 more·Sep 21, 2022
Variational quantum algorithms (VQAs) and their applications in the field of quantum machine learning through parametrized quantum circuits (PQCs) are thought to be one major way of leveraging noisy intermediate-scale quantum computing devices. Howev...
Parametric Synthesis of Quantum Circuits for Training Perceptron Neural Networks
C. B. Pronin, A. Ostroukh·Sep 20, 2022
This work contains the analysis of results received after running synthesized quantum circuits for training perceptron neural networks. The training is performed by creating a Grover’s algorithm with a custom oracle function. The concept of synthesiz...
Cryogenic in-memory computing using magnetic topological insulators
Yuting Liu, Albert Lee, Kun Qian +14 more·Sep 20, 2022
Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here w...
Parametric Synthesis of Computational Circuits for Complex Quantum Algorithms
C. B. Pronin, A. Ostroukh·Sep 20, 2022
At the moment, quantum circuits are created mainly by manually placing logic elements on lines that symbolize quantum bits. The purpose of creating Quantum Circuit Synthesizer"Naginata"was due to the fact that even with a slight increase in the numbe...
FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features (Student Abstract)
Hankyul Baek, Won Joon Yun, Joongheon Kim·Sep 19, 2022
Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansa...
Quantum Vision Transformers
El Amine Cherrat, Iordanis Kerenidis, Natansh Mathur +3 more·Sep 16, 2022
In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis. Building upon t...
A self-similar sine–cosine fractal architecture for multiport interferometers
J. Basani, S. Vadlamani, S. Bandyopadhyay +2 more·Sep 7, 2022
Abstract Multiport interferometers based on integrated beamsplitter meshes have recently captured interest as a platform for many emerging technologies. In this paper, we present a novel architecture for multiport interferometers based on the sine–co...