Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
The Quantum Imitation Game: Reverse Engineering of Quantum Machine Learning Models
Archisman Ghosh, Swaroop Ghosh·Jul 9, 2024
Quantum Machine Learning (QML) is an amalgamation of quantum computing paradigms with machine learning models, providing significant prospects for solving complex problems. However, with the expansion of numerous third-party vendors in the Noisy Inte...
Exploiting the equivalence between quantum neural networks and perceptrons
Chris Mingard, Jessica Pointing, Charles London +2 more·Jul 5, 2024
Quantum machine learning models based on parametrized quantum circuits, also called quantum neural networks (QNNs), are considered to be among the most promising candidates for applications on near-term quantum devices. Here we explore the expressivi...
Low-latency machine learning FPGA accelerator for multi-qubit state discrimination
P. Gautam, Shantharam Kalipatnapu, Ujjawal Singhal +5 more·Jul 4, 2024
Measuring a qubit state is a fundamental yet error-prone operation in quantum computing. These errors can arise from various sources, such as crosstalk, spontaneous state transitions, and excitations caused by the readout pulse. Here, we utilize an i...
Programming universal unitary transformations on a general-purpose silicon photonics platform
José Roberto Rausell-Campo, D. P'erez, L'opez +1 more·Jul 3, 2024
General-purpose programmable photonic processors provide a versatile platform for integrating diverse functionalities on a single chip. Leveraging a two-dimensional hexagonal waveguide mesh of Mach-Zehnder interferometers, these systems have demonstr...
ML-Powered FPGA-based Real-Time Quantum State Discrimination Enabling Mid-circuit Measurements
Neel R. Vora, Yilun Xu, A. Hashim +11 more·Jun 27, 2024
Similar to reading the transistor state in classical computers, identifying the quantum bit (qubit) state is a fundamental operation to translate quantum information. However, identifying quantum state has been the slowest and most susceptible to err...
Trade-off between gradient measurement efficiency and expressivity in deep quantum neural networks
Koki Chinzei, Shin-ichi Yamano, Quoc-Hoan Tran +2 more·Jun 26, 2024
Quantum neural networks (QNNs) require an efficient training algorithm to achieve practical quantum advantages. A promising approach is gradient-based optimization, where gradients are estimated by quantum measurements. However, QNNs currently lack g...
Quantum extreme learning of molecular potential energy surfaces and force fields
Gabriele Lo Monaco, Marco Bertini, Salvatore Lorenzo +1 more·Jun 20, 2024
Quantum machine learning algorithms are expected to play a pivotal role in quantum chemistry simulations in the immediate future. One such key application is the training of a quantum neural network to learn the potential energy surface and force fie...
A Survey of Methods for Mitigating Barren Plateaus for Parameterized Quantum Circuits
Michelle Gelman·Jun 20, 2024
Barren Plateaus are a formidable challenge for hybrid quantum-classical algorithms that lead to flat plateaus in the loss function landscape making it difficult to take advantage of the expressive power of parameterized quantum circuits with gradient...
New Reservoir Computing Kernel Based on Chaotic Chua Circuit and Investigating Application to Post-Quantum Cryptography
Matthew John Cossins, Sendy Phang·Jun 18, 2024
The aim of this project was to develop a new Reservoir Computer implementation, based on a chaotic Chua circuit. In addition to suitable classification and regression benchmarks, the Reservoir Computer was applied to Post-Quantum Cryptography, with i...
Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures
Enrico Russo, M. Palesi, Davide Patti +2 more·Jun 17, 2024
Modular, distributed and multi-core architectures are currently considered a promising approach for scalability of quantum computing systems. The integration of multiple Quantum Processing Units necessitates classical and quantum-coherent communicati...
Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning
Abid Khan, Prateek Vaish, Y. Pang +8 more·Jun 12, 2024
The ability to perform ab initio molecular dynamics simulations using potential energies calculated on quantum computers would allow virtually exact dynamics for chemical and biochemical systems, with substantial impacts on the fields of catalysis an...
Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy
Byoungjoon Ahn, Hyun-Sik Jeong, Keun-Young Kim +1 more·Jun 11, 2024
We investigate the bulk reconstruction of AdS black hole spacetime emergent from quantum entanglement within a machine learning framework. Utilizing neural ordinary differential equations alongside Monte-Carlo integration, we develop a method tailore...
CHARME: A Chain-based Reinforcement Learning Approach for the Minor Embedding Problem
Hoang M. Ngo, Nguyen Do, Minh N. Vu +3 more·Jun 11, 2024
Quantum annealing (QA) has great potential to solve combinatorial optimization problems efficiently. However, the effectiveness of QA algorithms is heavily based on the embedding of problem instances, represented as logical graphs, into the quantum p...
Buildung Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset
Alona Sakhnenko, Julian Sikora, J. Lorenz·Jun 10, 2024
In this work, we are introducing a Quantum-Classical Bayesian Neural Network (QCBNN) that is capable to perform uncertainty-aware classification of classical medical dataset. This model is a symbiosis of a classical Convolutional NN that performs ult...
Quantum sparse coding and decoding based on quantum network
Xun Ji, Qin Liu, Shan Huang +2 more·Jun 10, 2024
Sparse coding provides a versatile framework for efficiently capturing and representing crucial data (information) concisely, which plays an essential role in various computer science fields, including data compression, feature extraction, and genera...
A quantum neural network-based approach to power quality disturbances detection and recognition
Guo-Dong Li, Haibin He, Yue Li +4 more·Jun 5, 2024
As an emerging technology force, quantum algorithms have shown great potential and unique advantages in many fields of application. Power quality disturbances (PQDs) affect the security and stability of the power system, which may lead to equipment d...
Hybrid Quantum-Classical Convolutional Neural Networks for Image Classification in Multiple Color Spaces
Kwok-Ho Ng, Tingting Song, Zhiquan Liu·Jun 4, 2024
The growing complexity and scale of image processing tasks challenge classical convolutional neural networks (CNNs) with high computational costs. Hybrid quantum-classical convolutional neural networks (HQCNNs) show potential to improve performance b...
Classification analysis of transition-metal chalcogenides and oxides using quantum machine learning
Kurudi V Vedavyasa, Ashok Kumar·May 29, 2024
Quantum machine learning (QML) leverages the potential from machine learning to explore the subtle patterns in huge datasets of complex nature with quantum advantages. This exponentially reduces the time and resources necessary for computations. QML ...
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
Marc Wanner, Laura Lewis, Chiranjib Bhattacharyya +2 more·May 28, 2024
A fundamental problem in quantum many-body physics is that of finding ground states of local Hamiltonians. A number of recent works gave provably efficient machine learning (ML) algorithms for learning ground states. Specifically, [Huang et al. Scien...
Graph Neural Networks on Quantum Computers
Yidong Liao, Xiao-Ming Zhang, Chris Ferrie·May 27, 2024
Graph Neural Networks (GNNs) are powerful machine learning models that excel at analyzing structured data represented as graphs, demonstrating remarkable performance in applications like social network analysis and recommendation systems. However, cl...