default search action
Quantum Machine Intelligence, Volume 5
Volume 5, Number 1, June 2023
- Marina O. Lisnichenko, Stanislav I. Protasov:
Quantum image representation: a review. 1-12 - Sanjaya Lohani
, Sangita Regmi
Dimension-adaptive machine learning-based quantum state reconstruction. 1-10 - Duarte Magano, Lorenzo Buffoni
Quantum density peak clustering. 1-11 - Massimo Pregnolato, Paola Zizzi:
SARS-CoV-2 spike and ACE2 entanglement-like binding. 1-13 - Haoran Liao
Decohering tensor network quantum machine learning models. 1-16 - Péter Mernyei
Equivariant quantum graph circuits: constructions for universal approximation over graphs. 1-20 - Tianchen Zhao, James Stokes, Shravan K. Veerapaneni
Meta-variational quantum Monte Carlo. 1-9 - Jure Brence
Boosting the performance of quantum annealers using machine learning. 1-11 - Haimeng Zhao
Non-IID quantum federated learning with one-shot communication complexity. 1-11 - E. Ghasemian:
Stationary states of a dissipative two-qubit quantum channel and their applications for quantum machine learning. 1-18 - James Stokes, Saibal De
Continuous-variable neural network quantum states and the quantum rotor model. 1-12 - Sascha Mücke
Feature selection on quantum computers. 1-16 - Konstantinos Meichanetzidis, Alexis Toumi, Giovanni de Felice, Bob Coecke:
Grammar-aware sentence classification on quantum computers. 1-16 - Anupama Padha
MAQML: a Meta-approach to Quantum Machine Learning with Accentuated Sample Variations for Unobtrusive Mental Health Monitoring. 1-17 - Sreetama Das
Quantum pattern recognition on real quantum processing units. 1-17 - Kai Liu, Yuxing Wei, Hai-Sheng Li:
The quantum realization of image linear gray enhancement. 1-14 - Wen-Ran Zhang:
If AI machine cannot think, can QI machine think? - from negative numbers to quantum intelligence for mind-light-matter unity. 1-18 - Francesco Di Marcantonio
Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning. 1-11 - Kai Schärer, Marco Comuzzi:
The quantum threat to blockchain: summary and timeline analysis. 1-12 - André Sequeira
Policy gradients using variational quantum circuits. 1-15 - Tulika Dutta
Multi-level quantum inspired metaheuristics for automatic clustering of hyperspectral images. 1-35 - Supanut Thanasilp, Samson Wang, Nhat A. Nghiem, Patrick J. Coles, Marco Cerezo:
Subtleties in the trainability of quantum machine learning models. 1-22
Volume 5, Number 2, December 2023
- Yan Zhu, Ge Bai, Yuexuan Wang, Tongyang Li, Giulio Chiribella:
Quantum autoencoders for communication-efficient cloud computing. 1-15 - Yuji Cao, Xiyuan Zhou, Xiang Fei, Huan Zhao
Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. 1-12 - Stanislav I. Protasov, Marina O. Lisnichenko
Faster quantum state decomposition with Tucker tensor approximation. 1-10 - Joel E. Pion, Christian F. A. Negre, Susan M. Mniszewski:
Quantum computing for a profusion of postman problem variants. 1-20 - Lirandë Pira, Chris Ferrie:
An invitation to distributed quantum neural networks. 1-24 - Noah L. Wach, Manuel S. Rudolph
Data re-uploading with a single qudit. 1-12 - Davide Cugini, Dario Gerace
Comparing quantum and classical machine learning for Vector Boson Scattering background reduction at the Large Hadron Collider. 1-11 - Cenk Tüysüz
Classical splitting of parametrized quantum circuits. 1-19 - Sagnik Chatterjee, Rohan Bhatia, Parmeet Singh Chani, Debajyoti Bera:
Quantum boosting using domain-partitioning hypotheses. 1-20 - Aikaterini Gratsea, Patrick Huembeli:
The effect of the processing and measurement operators on the expressive power of quantum models. 1-12 - Michal Siemaszko
Rapid training of quantum recurrent neural networks. 1-16 - El Amine Cherrat
Quantum reinforcement learning via policy iteration. 1-18 - Hossein T. Dinani
Estimating the degree of non-Markovianity using variational quantum circuits. 1-10 - Roberto Campos
Quantum Metropolis Solver: a quantum walks approach to optimization problems. 1-15 - Michal Koren
A quantum "black box" for entropy calculation. 37 - Salvatore Certo, Anh Pham, Nicolas Robles, Andrew Vlasic:
Conditional generative models for learning stochastic processes. 1-12 - Anthony M. Smaldone
Quantum convolutional neural networks for multi-channel supervised learning. 1-15 - Medina Bandic, Carmen G. Almudéver, Sebastian Feld:
Interaction graph-based characterization of quantum benchmarks for improving quantum circuit mapping techniques. 1-30 - Massimiliano Incudini
Resource saving via ensemble techniques for quantum neural networks. 1-24 - Asel Sagingalieva, Mohammad Kordzanganeh, Andrii Kurkin, Artem Melnikov, Daniil Kuhmistrov, Michael Perelshtein, Alexey Melnikov, Andrea Skolik, David Von Dollen:
Hybrid quantum ResNet for car classification and its hyperparameter optimization. 1-15 - M. Bilkis, Marco Cerezo, Guillaume Verdon, Patrick J. Coles, Lukasz Cincio:
A semi-agnostic ansatz with variable structure for variational quantum algorithms. 43 - Martins Kalis, Andris Locans, Rolands Sikovs, Hassan Naseri, Andris Ambainis:
A hybrid quantum-classical approach for inference on restricted Boltzmann machines. 44 - Manuel P. Cuéllar, Carlos Cano Gutierrez, Luis G. Baca Ruíz
Time series quantum classifiers with amplitude embedding. 45 - Nozomu Kobayashi, Yoshiyuki Suimon, Koichi Miyamoto, Kosuke Mitarai:
The cross-sectional stock return predictions via quantum neural network and tensor network. 46
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-12-16 22:17 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: