- 2023
- Medina Bandic, Carmen G. Almudéver, Sebastian Feld:
Interaction graph-based characterization of quantum benchmarks for improving quantum circuit mapping techniques. Quantum Mach. Intell. 5(2): 1-30 (2023) - M. Bilkis, Marco Cerezo, Guillaume Verdon, Patrick J. Coles, Lukasz Cincio:
A semi-agnostic ansatz with variable structure for variational quantum algorithms. Quantum Mach. Intell. 5(2): 43 (2023) - Jure Brence
, Dragan Mihailovic, Viktor V. Kabanov, Ljupco Todorovski, Saso Dzeroski, Jaka Vodeb:
Boosting the performance of quantum annealers using machine learning. Quantum Mach. Intell. 5(1): 1-11 (2023) - Roberto Campos, P. A. M. Casares, M. A. Martin-Delgado:
Quantum Metropolis Solver: a quantum walks approach to optimization problems. Quantum Mach. Intell. 5(2): 1-15 (2023) - Yuji Cao, Xiyuan Zhou, Xiang Fei, Huan Zhao, Wenxuan Liu, Junhua Zhao:
Linear-layer-enhanced quantum long short-term memory for carbon price forecasting. Quantum Mach. Intell. 5(2): 1-12 (2023) - Salvatore Certo, Anh Pham, Nicolas Robles, Andrew Vlasic:
Conditional generative models for learning stochastic processes. Quantum Mach. Intell. 5(2): 1-12 (2023) - Sagnik Chatterjee, Rohan Bhatia, Parmeet Singh Chani, Debajyoti Bera:
Quantum boosting using domain-partitioning hypotheses. Quantum Mach. Intell. 5(2): 1-20 (2023) - El Amine Cherrat, Iordanis Kerenidis, Anupam Prakash:
Quantum reinforcement learning via policy iteration. Quantum Mach. Intell. 5(2): 1-18 (2023) - Manuel P. Cuéllar, Carlos Cano Gutierrez, Luis G. Baca Ruíz, Lorenzo Servadei:
Time series quantum classifiers with amplitude embedding. Quantum Mach. Intell. 5(2): 45 (2023) - Davide Cugini, Dario Gerace, Pietro Govoni, Auro Michele Perego, Davide Valsecchi:
Comparing quantum and classical machine learning for Vector Boson Scattering background reduction at the Large Hadron Collider. Quantum Mach. Intell. 5(2): 1-11 (2023) - Sreetama Das, Jingfu Zhang, Stefano Martina, Dieter Suter, Filippo Caruso:
Quantum pattern recognition on real quantum processing units. Quantum Mach. Intell. 5(1): 1-17 (2023) - Hossein T. Dinani, Diego Tancara, Felipe F. Fanchini, Ariel Norambuena, Raul Coto:
Estimating the degree of non-Markovianity using variational quantum circuits. Quantum Mach. Intell. 5(2): 1-10 (2023) - Tulika Dutta, Siddhartha Bhattacharyya, Bijaya Ketan Panigrahi, Ivan Zelinka, Leo Mrsic:
Multi-level quantum inspired metaheuristics for automatic clustering of hyperspectral images. Quantum Mach. Intell. 5(1): 1-35 (2023) - E. Ghasemian:
Stationary states of a dissipative two-qubit quantum channel and their applications for quantum machine learning. Quantum Mach. Intell. 5(1): 1-18 (2023) - Aikaterini Gratsea, Patrick Huembeli:
The effect of the processing and measurement operators on the expressive power of quantum models. Quantum Mach. Intell. 5(2): 1-12 (2023) - Massimiliano Incudini, Michele Grossi, Andrea Ceschini, Antonio Mandarino, Massimo Panella, Sofia Vallecorsa, David Windridge:
Resource saving via ensemble techniques for quantum neural networks. Quantum Mach. Intell. 5(2): 1-24 (2023) - Martins Kalis, Andris Locans, Rolands Sikovs, Hassan Naseri, Andris Ambainis:
A hybrid quantum-classical approach for inference on restricted Boltzmann machines. Quantum Mach. Intell. 5(2): 44 (2023) - Nozomu Kobayashi, Yoshiyuki Suimon, Koichi Miyamoto, Kosuke Mitarai:
The cross-sectional stock return predictions via quantum neural network and tensor network. Quantum Mach. Intell. 5(2): 46 (2023) - Michal Koren, Oded Koren, Or Peretz:
A quantum "black box" for entropy calculation. Quantum Mach. Intell. 5(2): 37 (2023) - Haoran Liao, Ian Convy, Zhibo Yang, K. Birgitta Whaley:
Decohering tensor network quantum machine learning models. Quantum Mach. Intell. 5(1): 1-16 (2023) - Marina O. Lisnichenko, Stanislav I. Protasov:
Quantum image representation: a review. Quantum Mach. Intell. 5(1): 1-12 (2023) - Kai Liu, Yuxing Wei, Hai-Sheng Li:
The quantum realization of image linear gray enhancement. Quantum Mach. Intell. 5(1): 1-14 (2023) - Sanjaya Lohani, Sangita Regmi, Joseph M. Lukens, Ryan T. Glasser, Thomas A. Searles, Brian T. Kirby:
Dimension-adaptive machine learning-based quantum state reconstruction. Quantum Mach. Intell. 5(1): 1-10 (2023) - Duarte Magano, Lorenzo Buffoni, Yasser Omar:
Quantum density peak clustering. Quantum Mach. Intell. 5(1): 1-11 (2023) - Francesco Di Marcantonio, Massimiliano Incudini, Davide Tezza, Michele Grossi:
Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning. Quantum Mach. Intell. 5(1): 1-11 (2023) - Konstantinos Meichanetzidis, Alexis Toumi, Giovanni de Felice, Bob Coecke:
Grammar-aware sentence classification on quantum computers. Quantum Mach. Intell. 5(1): 1-16 (2023) - Péter Mernyei, Konstantinos Meichanetzidis, Ismail Ilkan Ceylan:
Equivariant quantum graph circuits: constructions for universal approximation over graphs. Quantum Mach. Intell. 5(1): 1-20 (2023) - Sascha Mücke, Raoul Heese, Sabine Müller, Moritz Wolter, Nico Piatkowski:
Feature selection on quantum computers. Quantum Mach. Intell. 5(1): 1-16 (2023) - Anupama Padha, Anita Sahoo:
MAQML: a Meta-approach to Quantum Machine Learning with Accentuated Sample Variations for Unobtrusive Mental Health Monitoring. Quantum Mach. Intell. 5(1): 1-17 (2023) - Joel E. Pion, Christian F. A. Negre, Susan M. Mniszewski:
Quantum computing for a profusion of postman problem variants. Quantum Mach. Intell. 5(2): 1-20 (2023)
