Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
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...
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...
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...
Quantum reservoir computing implementation on coherently coupled quantum oscillators
Julien Dudas, Baptiste Carles, E. Plouet +3 more·Sep 7, 2022
Quantum reservoir computing is a promising approach for quantum neural networks, capable of solving hard learning tasks on both classical and quantum input data. However, current approaches with qubits suffer from limited connectivity. We propose an ...
Deterministic and random features for large-scale quantum kernel machine
Kouhei Nakaji, Hiroyuki Tezuka, Naoki Yamamoto·Sep 5, 2022
Quantum machine learning (QML) is the spearhead of quantum computer applications. In particular, quantum neural networks (QNN) are actively studied as the method that works both in near-term quantum computers and fault-tolerant quantum computers. Rec...
Alternating Layered Variational Quantum Circuits Can Be Classically Optimized Efficiently Using Classical Shadows
Afrad Basheer, Yuan Feng, C. Ferrie +1 more·Aug 24, 2022
Variational quantum algorithms (VQAs) are the quantum analog of classical neural networks (NNs). A VQA consists of a parameterized quantum circuit (PQC) which is composed of multiple layers of ansatzes (simpler PQCs, which are an analogy of NN layers...
Exponential concentration in quantum kernel methods
Supanut Thanasilp, Samson Wang, M. Cerezo +1 more·Aug 23, 2022
Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a kernel-based model one is guar...
Quantum Multi-Agent Meta Reinforcement Learning
Won Joon Yun, Jihong Park, Joongheon Kim·Aug 22, 2022
Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by this, in this article we re-desig...
Detection and evaluation of abnormal user behavior based on quantum generation adversarial network
Minghua Pan, Bin Wang, Xiaoling Tao +3 more·Aug 21, 2022
Quantum computing holds tremendous potential for processing high-dimensional data, capitalizing on the unique capabilities of superposition and parallelism within quantum states. As we navigate the noisy intermediate-scale quantum (NISQ) era, the exp...
Iterative-Free Quantum Approximate Optimization Algorithm Using Neural Networks
Ohad Amosy, Tamuz Danzig, E. Porat +2 more·Aug 21, 2022
The quantum approximate optimization algorithm (QAOA) is a leading iterative variational quantum algorithm for heuris-tically solving combinatorial optimization problems. A large portion of the computational effort in QAOA is spent by the optimization...
Heart Disease Detection using Quantum Computing and Partitioned Random Forest Methods
Hanif Heidari, Gerhard Hellstern, Murugappan Murugappan·Aug 17, 2022
Heart disease morbidity and mortality rates are increasing, which has a negative impact on public health and the global economy. Early detection of heart disease reduces the incidence of heart mortality and morbidity. Recent research has utilized qua...
Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian Optimization with Neural Network Kernel
cSener Ozonder, H. K. Kuccukkartal·Aug 16, 2022
Density functional theory (DFT) is a powerful computational method used to obtain physical and chemical properties of materials. In the materials discovery framework, it is often necessary to virtually screen a large and high-dimensional chemical spa...
Federated Quantum Natural Gradient Descent for Quantum Federated Learning
Jun Qi·Aug 15, 2022
The heart of Quantum Federated Learning (QFL) is associated with a distributed learning architecture across several local quantum devices and a more efficient training algorithm for the QFL is expected to minimize the communication overhead among diff...
Imperfect Quantum Photonic Neural Networks
Jacob Ewaniuk, J. Carolan, B. Shastri +1 more·Aug 13, 2022
Quantum photonic neural networks are variational photonic circuits that can be trained to implement high‐fidelity quantum operations. However, work‐to‐date has assumed idealized components, including a perfect π Kerr nonlinearity. This work investiga...
Scalable neural quantum states architecture for quantum chemistry
Tianchen Zhao, J. Stokes, S. Veerapaneni·Aug 11, 2022
Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems. Despite rapid developments, significant scalability challenges arise when considering molecules of lar...
NEO-QEC: Neural Network Enhanced Online Superconducting Decoder for Surface Codes
Yosuke Ueno, M. Kondo, Masamitsu Tanaka +2 more·Aug 11, 2022
Quantum error correction (QEC) is essential for quantum computing to mitigate the effect of errors on qubits, and surface code (SC) is one of the most promising QEC methods. Decoding SCs is the most computational expensive task in the control device ...
An Example of Use of Variational Methods in Quantum Machine Learning
Marco Simonetti, Damiano Perri, O. Gervasi·Aug 7, 2022
This paper introduces a deep learning system based on a quantum neural network for the binary classification of points of a specific geometric pattern (Two-Moons Classification problem) on a plane. We believe that the use of hybrid deep learning syst...