Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Machine Learning Assisted Cognitive Construction of a Shallow Depth Dynamic Ansatz for Noisy Quantum Hardware
Sonaldeep Halder, Anish Dey, Chinmay Shrikhande +1 more·Oct 12, 2023
The development of various dynamic ansatz-constructing techniques has ushered in a new era, rendering the practical exploitation of Noisy Intermediate-Scale Quantum (NISQ) hardware for molecular simulations increasingly viable. However, they exhibit ...
Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits
Zhan Yu, Qiuhao Chen, Yuling Jiao +4 more·Oct 11, 2023
Parameterized quantum circuits (PQCs) have emerged as a promising approach for quantum neural networks. However, understanding their expressive power in accomplishing machine learning tasks remains a crucial question. This paper investigates the expr...
Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions
Zhengmeng Xu, Hai Lin·Oct 11, 2023
We propose a time series forecasting method named Quantum Gramian Angular Field (QGAF). This approach merges the advantages of quantum computing technology with deep learning, aiming to enhance the precision of time series classification and forecast...
Quantum sequential scattering model for quantum state learning
M. Jing, Geng-yang Liu, Hongbin Ren +1 more·Oct 11, 2023
Learning probability distribution is an essential framework in classical learning theory. As a counterpart, quantum state learning has spurred the exploration of quantum machine learning theory. However, as dimensionality increases, learning a high-d...
Quantum Shadow Gradient Descent for Variational Quantum Algorithms
Mohsen Heidari, M. Naved, Wenbo Xie +2 more·Oct 10, 2023
Gradient-based optimizers have been proposed for training variational quantum circuits in settings such as quantum neural networks (QNNs). The task of gradient estimation, however, has proven to be challenging, primarily due to distinctive quantum fe...
Learning high-accuracy error decoding for quantum processors
Johannes Bausch, A. W. Senior, F. Heras +15 more·Oct 9, 2023
Building a large-scale quantum computer requires effective strategies to correct errors that inevitably arise in physical quantum systems1. Quantum error-correction codes2 present a way to reach this goal by encoding logical information redundantly i...
SU(d)-symmetric random unitaries: quantum scrambling, error correction, and machine learning
Zimu Li, Han Zheng, Yunfei Wang +3 more·Sep 28, 2023
Quantum information processing in the presence of continuous symmetry is of wide importance and exhibits many novel physical and mathematical phenomena. SU(d) is a continuous symmetry group of particular interest since it represents a fundamental typ...
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Hao Zhang, Chenghong Zhu, M. Jing +1 more·Sep 26, 2023
Quantum neural networks (QNNs) have been a promising framework in pursuing near-term quantum advantage in various fields, where many applications can be viewed as learning a quantum state that encodes useful data. As a quantum analog of probability d...
Quantum Superpositions of Conscious States in a Minimal Integrated Information Model
Kelvin J. McQueen, Ian T. Durham, Markus P. Mueller·Sep 25, 2023
Could there be quantum superpositions of conscious states, as suggested by the Wigner's friend thought experiment? Mathematical theories of consciousness, notably Integrated Information Theory (IIT), make this question more precise by associating phy...
Quantum algorithms for state preparation and data classification based on stabilizer codes
P. Jouzdani, H. A. Hashim, E. Mucciolo·Sep 18, 2023
Quantum error correction (QEC) is a way to protect quantum information against noise. It consists of encoding input information into entangled quantum states known as the code space. Furthermore, to classify if the encoded information is corrupted or...
Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images
Su Yeon Chang, M. Grossi, B. L. Saux +1 more·Sep 17, 2023
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which can be efficiently simulated with a low depth on near-term quantum hardware in the presence of noises. However, their performance highly relies on choosing the most s...
Predicting Expressibility of Parameterized Quantum Circuits Using Graph Neural Network
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +2 more·Sep 13, 2023
Parameterized Quantum Circuits (PQCs) are essential to quantum machine learning and optimization algorithms. The expressibility of PQCs, which measures their ability to represent a wide range of quantum states, is a critical factor influencing their ...
Physics-informed neural networks for an optimal counterdiabatic quantum computation
Antonio Ferrer-S'anchez, Carlos Flores-Garrigos, C. Hernani-Morales +7 more·Sep 8, 2023
A novel methodology that leverages physics-informed neural networks to optimize quantum circuits in systems with NQ qubits by addressing the counterdiabatic (CD) protocol is introduced. The primary purpose is to employ physics-inspired deep learning ...
Neural Crystals
S. Karamintziou, Thanassis Mavropoulos, Dimos Ntioudis +3 more·Sep 5, 2023
We face up to the challenge of explainability in Multimodal Artificial Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum computing, interpretable and transparent spin-geometrical neural architectures for early fusion of large-sca...
Financial fraud detection using quantum graph neural networks
Nouhaila Innan, Abhishek Sawaika, Ashim Dhor +7 more·Sep 3, 2023
Financial fraud detection is essential for preventing significant financial losses and maintaining the reputation of financial institutions. However, conventional methods of detecting financial fraud have limited effectiveness, necessitating the need...
What can we Learn from Quantum Convolutional Neural Networks?
Chukwudubem Umeano, Annie E. Paine, V. Elfving +1 more·Aug 31, 2023
Quantum machine learning (QML) shows promise for analyzing quantum data. A notable example is the use of quantum convolutional neural networks (QCNNs), implemented as specific types of quantum circuits, to recognize phases of matter. In this approach...
Thermodynamic computing via autonomous quantum thermal machines
Patryk Lipka-Bartosik, M. Perarnau-Llobet, N. Brunner·Aug 30, 2023
We develop a physics-based model for classical computation based on autonomous quantum thermal machines. These machines consist of few interacting quantum bits (qubits) connected to several environments at different temperatures. Heat flows through t...
Hybrid quantum neural network structures for image multi-classification
Mingrui Shi, Haozhen Situ, Cai Zhang·Aug 30, 2023
Image classification is a fundamental problem in computer vision, and neural networks provide an effective solution. With the advancement of quantum technology, quantum neural networks have attracted a lot of attention. However, they are only suitabl...
Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Models
Farina Riaz, Shahab A. Abdulla, Hajime Suzuki +3 more·Aug 22, 2023
This paper proposes a novel quantum pre-processing filter (QPF) to improve the image classification accuracy of neural network (NN) models. A simple four qubit quantum circuit that uses Y rotation gates for encoding and two controlled NOT gates for c...
Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment
Soronzonbold Otgonbaatar, Dieter Kranzlmüller·Aug 18, 2023
This article examines the current status of quantum computing (QC) in Earth observation and satellite imagery. We analyze the potential limitations and applications of quantum learning models when dealing with satellite data, considering the persiste...