Poster Sessions
Preparing your poster
- All posters should be based on the submitted abstract that has been accepted by the Programme Committee.
- Poster displays are limited to one side of a poster panel, 100cm (width) x 225cm tall from the floor.
- The recommended poster size is A0 (portrait layout): 84.1cm (width) x 118.9 cm (height).
- Be sure to include the title of the abstract, the names of the author and co-authors, and the affiliations.
- Please note that all posters must be displayed in English.
- It is recommended that you hand-carry your poster to the conference in a tube or portfolio case. Costs associated with the creation and shipping of the poster display are the responsibility of the authors. Poster printing will NOT be available on site.
- For first-time presenters, guides on preparing impactful poster are abundant online, and you can find some inspiration here. The most important feature of effective posters is that they do not contain a lot of text; good posters usually have 100-1000 words.
Displaying your poster
- The poster session is located next to the conference room.
- Posters are randomly assigned to session I or II. Please check the lists below to find out which session you are presenting in.
- Posters can be set up from 12:30 on the day of your session. Poster sessions are 16:30-18:00. Please be at your poster during this period. Posters should be removed after 18:00.
- There is no allocated space for each poster. Please put up your printed poster on any available poster boards, first come first served.
- Velcro will be provided on site. No glue, double-sided tape, tacks or staples are permitted on the poster boards.
- Please note that the Organising Committee is not responsible for any damage or loss of posters. Any posters remaining after the removal time will be removed and discarded after the event.
Poster Session I: Monday
| EasyChairID | Authors | Title |
|---|---|---|
| 181 | Marco Sciorilli, Lucas Borges, Giancarlo Camilo, Thiago O. Maciel, Askery Canabarro and Leandro Aolita | A competitive NISQ and qubit-efficient solver for the LABS problem |
| 111 | Tim Forrer, Matthew Wilson, Philip Taranto, Jisho Miyazaki and Mio Murao | A compositional framework for leveraging quantum learning to circumvent no-go theorems |
| 261 | Ian Joel David, Ilya Sinayskiy and Francesco Petruccione | A faster converging qDRIFT algorithm with application to Hamiltonian data encoding |
| 321 | Kieran McDowall, Theodoros Kapourniotis, Christopher Oliver, Phalgun Lolur and Konstantinos Georgopoulos | A Practical Cross-Platform, Multi-Algorithm Study of Quantum Optimisation for Configurational Analysis of Materials |
| 88 | Tara Kit, Leanghok Hour, Muyleang Ing and Youngsun Han | A Resource-Efficient Quantum-Classical Model for Protein–Ligand Binding Affinity Prediction |
| 291 | Vincenzo Lipardi, Domenica Dibenedetto, Georgios Stamoulis and Mark H.M. Winands | A Study on Stabilizer R\'enyi Entropy Estimation using Machine Learning |
| 108 | Rundi Lu, Ruiqi Zhang, Weikang Li, Dong-Ling Deng and Zhengwei Liu | A Unified Frequency Principle for Quantum and Classical Machine Learning |
| 33 | Mafalda Ramôa | ADAPT-VQE with Operator Removal |
| 11 | Mandadi Sindhu, M.Mohan Sai Reddy and Devireddy Sai Santhosh Reddy | Advanced Ensemble Smart Classifications for Nifty Smart Market Trends |
| 298 | Xinbiao Wang, Yuxuan Du, Zihan Lou, Yang Qian, Kaining Zhang, Yong Luo, Bo Du and Dacheng Tao | AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation |
| 4 | Matthew Ho, Jun Yong Khoo, Adrian Mak and Stefano Carrazza | AI-Powered Noisy Quantum Emulation: Generalized Gate-Based Protocols for Hardware-Agnostic Simulation |
| 148 | Li Xin and Zhang-Qi Yin | An Attention-Based Quantum Phase Transition Detection on NISQ Devices |
| 42 | Yuqi Huang, Vincent Y. F. Tan and Sharu Jose | Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization |
| 331 | Laura Henderson, Kerstin Beer, Salini Karuvade, Riddhi Gupta and Angela White | Barren plateau-free and noise-robust quantum advantage for learning data with group symmetries |
| 126 | Alexandra Ramôa, Raffaele Santagati and Nathan Wiebe | Bayesian Learning of Quantum Hardware Dynamics |
| 192 | Dominic Lowe, Myungshik Kim and Roberto Bondesan | Benchmarking Quantum Algorithms for Gaussian Process Regression |
| 334 | Charitha Pathipati, Tirupati Bolisetti, Girish Sankar, Sri Harsha Thota and Ram Balachandar | Bridging Remote Sensing and Quantum Computing: Snow Depth Estimation with LSTM and QLSTM |
| 150 | Dean Brand, Domenica Dibenedetto and Francesco Petruccione | Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit |
| 86 | Ji Guan | Certifying Adversarial Robustness in Quantum Machine Learning: From Theory to Physical Validation |
| 271 | Georgios Korpas, Wayne Lin, Iosif Sakos and Antonios Varvitsiotis | Certifying Optimality of VQA Solutions via Sparse SOS Hierarchies |
| 43 | Jan Schnabel, Roberto Flórez-Ablan and Marco Roth | Challenges and limitations of quantum kernel methods |
| 200 | Paolo Braccia, Pablo Bermejo, Antonio Anna Mele, Nahuel Diaz, Andrew Deneris, Martin Larocca and Marco Cerezo | Characterizing quantum resourcefulness via group-Fourier decompositions |
| 66 | Changjae Im, Hyeondo Oh and Daniel K. Park | Classical-quantum hybrid support vector data description for one-class classification |
| 125 | Giuseppe Sergioli, Roberto Giuntini, Andres Camillo Granda Arango, Carlo Cuccu and Carla Sophie Rieger | Classification of Quantum Correlations via Quantum-inspired Machine Learning |
| 78 | Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos, Antonio A. Gentile and Osvaldo Simeone | Conservative Quantum Offline Model-Based Optimization |
| Manual Submission | Xiaoting Gao, Mingsheng Tian, Yu Xiang, Mattia Walschaers, and Qiongyi He | Continuous Variable Entanglement Detection and Classification through Neural Networks |
| 37 | Prof. Dr. Gerhard Hellstern | Data Clustering as a Quantum Computing Use-Cas |
| 220 | Arthur Strauss, Lukas Voss, Aniket Chatterjee and Hui Khoon Ng | Deep Reinforcement Learning for real-time context-aware gate calibration |
| 77 | Hugo Thomas, Hela Mhiri, Leo Monbroussou, Zoë Holmes and Elham Kashefi | Dequantization and expressivity in photonic quantum Fourier models |
| 38 | Zhong-Xia Shang, Naixu Guo, Dong An and Qi Zhao | Design nearly optimal quantum algorithm for linear differential equations via Lindbladians |
| 324 | Nouhaila Innan and Muhammad Shafique | Designing Privacy-Preserving Architectures in Quantum Federated Learning |
| 13 | Jonathan Lu, Lucy Jiao, Kristina Wolinski, Milan Kornjača, Hong-Ye Hu, Sergio Cantu, Fangli Liu, Susanne Yelin and Sheng-Tao Wang | Digital–analog quantum learning on Rydberg atom arrays |
| 94 | Yuxuan Yan, Sitian Qian, Qi Zhao and Xingjian Zhang | Distilling the knowledge with quantum neural networks |
| 104 | Marie Kempkes, Aroosa Ijaz, Elies Gil-Fuster, Carlos Bravo-Prieto, Jakob Spiegelberg, Evert van Nieuwenburg and Vedran Dunjko | Double Descent in Quantum Kernel Methods |
| 187 | Yudai Suzuki, Bi Hong Tiang, Jeongrak Son, René Zander, Raphael Seidel, Nelly H. Y. Ng, Zoë Holmes and Marek Gluza | Double-bracket quantum algorithms for ground-state preparation via cooling |
| 197 | Bharathi Kannan Jeevanandam, Nisarg Vyas, Sreeram Pg and Santhanam M.S | Dynamical Regimes and Memory Performance in Quantum Reservoirs: Insights from Random Matrix Theory |
| 82 | Jan Balewski, Chris Pestano, Mercy G. Amankwah, E. Wes Bethel and Talita Perciano | EHands: Quantum Protocol for Polynomial Computation on Real-Valued Encoded States |
| 266 | Daniel Uzcátegui, Katherine Muñoz, Aldo Delgado and Dardo Goyeneche | Entanglement detection via machine learning techniques |
| 243 | Vladyslav Bohun, Illia Lukin, Mykola Lukhanko, Georgios Korpas, Philippe J.S. De Brouwer, Mykola Maksymenko and Maciej Koch-Janusz | Entanglement scaling in matrix product state representation of smooth functions and their shallow quantum circuit approximations |
| 15 | Haimeng Zhao and Dong-Ling Deng | Entanglement-induced provable and robust quantum learning advantages |
| 71 | Iris Agresti, Mirela Selimovic, Michal Siemaszko, Joshua Morris, Borivoje Dakic, Riccardo Albiero, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame, Magdalena Stobinska and Philip Walther | Experimental quantum memristor-based reservoir computing |
| 260 | Felix Paul, Björn Minneker and Peter Jung | Exploring Trainability of Quantum Fourier Models for Different Data Re-uploading Schemes |
| 72 | Maria Demidik, Cenk Tüysüz, Nico Piatkowski, Michele Grossi and Karl Jansen | Expressive equivalence of classical and quantum restricted Boltzmann machines |
| 50 | Nils-Erik Schütte, Niclas Götting, Hauke Müntinga, Meike List, Daniel Brunner and Christopher Gies | Expressivity Limits of Quantum Reservoir Computing |
| 335 | Mitali Nanda and Tirupati Bolisetti | Fake News Detection using Hybrid Classical-Quantum Transfer Learning Approach |
| 62 | Adam Wesolowski, Ronin Wu and Karim Essafi | Fast, Accurate and Interpretable Graph Classification with Topological Kernels: An Even More Scalable Alternative to Weisfeiler-Lehman Kernels |
| 209 | Javier González Otero, Adrián Pérez Salinas and Miguel Ángel González Ballester | Flexible quantum Kolmogorov-Arnold networks via generalized fractional-order Chebyshev functions and trainable QSVT basis |
| 139 | Jiabin You, Jian Feng Kong and Jun Ye | Forecasting the Lorenz System Using a Hierarchical Tensor Network Model |
| 248 | Mansoor Ali Khan, Muhammad Naveed Aman and Biplab Sikdar | From Bits to Qubits: Comparative Insights into Embedding Strategies for Machine Learning |
| 154 | Naqueeb Ahmad Warsi, Ayanava Dasgupta and Masahito Hayashi | Generalization Bounds for Quantum Learning via Rényi Divergences |
| 175 | Tongyan Wu, Amine Bentellis, Alona Sakhnenko and Jeanette Lorenz | Generalization Bounds in Hybrid-Quantum Machine Learning Models |
| 52 | Sabri Meyer, Francesco Scala, Franceso Tacchino and Aurelien Lucchi | Gradient Scalability on Super-polynomially Complex Quantum Landscapes |
| 229 | Eunok Bae, Nari Choi, Jeonghyun Shin and Minjin Choi | Grover's algorithm with W state-based initialization for solving the exact-cover problem |
| 196 | Ignacio Acedo | Hardware Adapted Quantum Machine Learning with Pulse-Level optimization |
| 2 | Giacomo Franceschetto, Marcin Płodzień, Maciej Lewenstein, Antonio Acín and Pere Mujal | Harnessing quantum back-action for time-series processing |
| 41 | Daniil Rabinovich, Luis Ernesto Campos Espinoza, Georgii Paradezhenko and Kirill Lakhmanskiy | Heuristic ansatz design for trainable ion-native digital-analog quantum circuits |
| 142 | Chen-Yu Liu | Hybrid Parameterized Quantum States for Variational Quantum Learning |
| 314 | Siddhant Dutta and Sadok Ben Yahia | Hybrid Quantum Kolmogorov-Arnold Networks for High Energy Physics Analysis at the LHC |
| 303 | Parvathy Gopakumar, Rubell Marion Lincy G, Salvatore Sinno and Shruthi Thuravakkath | Hybrid Quantum Transfer Learning Models for Credit Risk Assessment |
| 172 | Ankith Mohan, Tobias Haug, Kishor Bharti and Jamie Sikora | Hybrid quantum-classical heuristics for optimizing large separable operators |
| 16 | Hongsuk Yi | Hybrid Quantum-Classical Traffic Flow Classification with Deep Feature Extraction |
| 293 | Zhan Yu, Naixu Guo, Gabriel Matos, Nikhil Khatri, Pranav Kalidindi, Yizhan Han, Lirandë Pira, Soumik Adhikary, Steve Clark and Patrick Rebentrost | Implementing Quantum Transformers on Trapped Ion Devices |
| 262 | Nathan Haboury, Mo Kordzanganeh, Alexey Melnikov and Pavel Sekatski | Information plane and compression-gnostic feedback in quantum machine learning |
| 274 | Yule Mayevsky, Akram Youssry, Ritik Sareen, Gerardo Paz-Silva and Alberto Peruzzo | Interpretable Machine Learning for Quantum Control |
| 134 | Alona Sakhnenko, Christian Mendl and Jeanette Miriam Lorenz | Is data-efficient learning feasible with quantum models? |
| 340 | Abdullah Kazi and Jayesh Hire | Learning Boolean Functions with Non-Local Dependencies via Hybrid Quantum-Classical Neural Networks |
| 20 | Haimeng Zhao, Yuzhen Zhang and John Preskill | Learning to erase quantum states: thermodynamic implications of quantum learning theory |
| 70 | Cenk Tüysüz, Maria Demidik, Luuk Coopmans, Enrico Rinaldi, Vincent Croft, Yacine Haddad, Matthias Rosenkranz and Karl Jansen | Learning to generate high-dimensional distributions with low-dimensional quantum Boltzmann machines |
| 270 | Armando Angrisani | Learning Unitaries with Quantum Statistical Queries |
| 273 | Alexandra Ramôa, Luis Santos and Akihito Soeda | Low Cost Experimental Design for Frequency Estimation |
| 152 | Vincent Han Leong Lau, Wenyu Guo, Ratnajit Sarkar, Aaron Tranter, Qian Ling Kee, Rigui Zhou, Ping Koy Lam, Mile Gu and Tao Wang | Machine Learning-Assisted Parametric Modulation in Atomic Magnetometry |
| 35 | Si Min Chan, Apimuk Sornsaeng and Dario Poletti | MANTIS: Multiple Anomaly-Detection Networks for Tensor Inspired Solutions |
| 59 | Letian Tang | More-efficient Quantum Multivariate Mean Value Estimator from Generalized Grover Operator |
| 49 | Yong Wang, Zhenghao Yin, Tobias Haug, Ciro Pentangelo, Simone Piacentini, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame and Philip Walther | Multiple photons enhance data efficiency in quantum machine learning |
| 18 | V Vijendran, Dax Enshan Koh, Eunok Bae, Hyukjoon Kwon, Ping Koy Lam and Syed M Assad | Near-Optimal Parameter Tuning of Level-1 QAOA for Ising Models |
| 306 | Dohyoung Lee, Daniel K. Park and Taeyoung Park | Neural Quantum Embedded Self-supervised Learning |
| 198 | Junseo Lee and Nhat A. Nghiem | New aspects of quantum topological data analysis: Betti number estimation, and testing and tracking of homology and cohomology classes |
| 149 | Datong Chen and Huangjun Zhu | Nonstabilizerness Enhances Thrifty Shadow Estimation |
| 208 | Petros Georgiou, Aaron Mark Thomas, Sharu Theresa Jose and Osvaldo Simeone | On the Generalization of Adversarially Trained Quantum Classifiers |
| 80 | Maxime Meyer, Soumik Adhikary, Naixu Guo and Patrick Rebentrost | Online Learning of Pure States is as Hard as Mixed States |
| 242 | Jan Li, Tim Coopmans, Patrick Emonts, Kenneth Goodenough, Jordi Tura and Evert van Nieuwenburg | Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning |
| 102 | Seokhoon Jeong and Daniel Kyungdeock Park | Optimization Framework for Data-Adaptive and Hardware-Efficient Quantum Data Embedding |
| 130 | Marc Drudis, Alberto Baiardi, Mattia Chiurco, Francesco Tacchino, Stefan Woerner and Christa Zoufal | Optimizing Quantum Time Dynamics with Classical Support |
| 48 | Daniel Pranjic and Semih Celiksümer | Optimizing Shadow Tomography for Many-Body Observables |
| 302 | Andrii Kurkin, Kevin Shen, Susanne Pielawa, Hao Wang and Vedran Dunjko | Parameterized IQP-QCBM generative model: universality with hidden units and kernel-adaptive efficient training on classical hardware |
| 121 | Thais Lima Silva, Lucas Borges and Leandro Aolita | Partition function estimation with a quantum coin toss |
| 251 | Shivani Pillay, Ilya Sinayskiy and Francesco Petruccione | Physics-Informed Neural Networks for Simulating Open Quantum Systems |
| 205 | Reyhaneh Aghaei Saem, Behrang Tafreshi, Zoe Holmes and Supanut Thanasilp | Pitfalls in the hunt for scalable parameterized quantum models |
| 263 | Nayoung Lee, Minsoo Shin, Asel Sagingalieva, Ayush Joshi Tripathi, Karan Pinto and Alexey Melnikov | Predictive Control with Hybrid Depth-Infused Quantum Neural Networks |
| 211 | Jonathan Teo, Xin Wei Lee and Hoong Chuin Lau | Probabilistic Greedy Behaviour of the Equivariant Quantum Circuit |
| 230 | Bence Bakó, Dániel Nagy, Péter Hága, Zsófia Kallus and Zoltán Zimborás | Problem-informed Graphical Quantum Generative Learning |
| 165 | Carlos Bravo-Prieto, Elies Gil-Fuster, Sergi Masot-Llima and Tommaso Guaita | Prospects for quantum advantage in ML from the representability of quantum functions |
| 328 | Lucas Tecot, Di Luo and Cho-Jui Hsieh | Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise |
| 61 | Alvaro Romero Mato, Boyang Chen, Nathan Eskue and Vahid Nasrabadi | QAOA based Neural Architecture Search |
| 167 | Daphne Wang, Anthony Walsh, Hugo Fruchet, Ariane Soret and Pierre-Emmanuel Emeriau | QCirCNN: a photonic-native quantum circular convolution network based on circulant matrices |
| 31 | Hsin-Yu Wu, Annie E. Paine, Evan Philip, Antonio A. Gentile and Oleksandr Kyriienko | Quantum Algorithm for Solving Nonlinear Differential Equations Based on Physics-Informed Effective Hamiltonians |
| 170 | Martin Larocca and Vojtech Havlicek | Quantum algorithms for representation-theoretic multiplicities |
| 9 | Jorge Martínez de Lejarza, Hsin-Yu Wu, Oleksandr Kyriienko, German Rodrigo and Michele Grossi | Quantum Chebyshev Probabilistic Models for Fragmentation Functions |
| 252 | Andrey Kardashin, Vladimir Palyulin and Konstantin Antipin | Quantum convolutional neural networks produce higher variance in regression tasks |
| 279 | Chaemoon Im and Joongheon Kim | Quantum Differential Privacy in Quantum Federated Learning |
| 91 | Shaukat Ali | Quantum Extreme Learning Machines: Insights from Industrial and Real-World Applications |
| 185 | Francesco Aldo Venturelli, Marco Parigi, Stefano Martina, Natalia Muñoz Moruno, Filippo Caruso, Alba Cervera Lierta and Miguel Ángel González Ballester | Quantum generative diffusion models for medical imaging |
| 169 | Snehal Raj, Brian Coyle, Léo Monbroussou and Elham Kashefi | Quantum Graph Neural Networks for the Travelling Salesman Problem |
| 336 | Leonardo Lavagna, Francesca De Falco and Massimo Panella | Quantum Hyperdimensional Computing for Pattern Completion |
| 67 | Taehee Ko, Hyeong Won Yu, Inho Lee, Sangkook Choi and Hyowon Park | Quantum medical image encoding and compression using Fourier-based methods |
| 288 | Minh Triet Chau, Hyeokjea Kwon, Sung Won Yun, Kevin Ferreira, Thi Ha Kyaw and Jack Baker | Quantum Neural Density Functionals in Density Functional Theory |
| 155 | Diksha Sharma, Vivek Balasaheb Sabale, Atul Kumar and Thirumalai M | Quantum Neural Networks Facilitating Quantum State Classification |
| 135 | Tianqi Chen and Jian Feng Kong | Quantum Optimization Towards Large-Scale Molecular Docking on a Quantum Computer |
| 300 | Gabriel Mejia Ruiz, Eileen Kuhn and Achim Streit | Quantum Principal Basis Learning (qPBL) for image classification |
| 227 | Santhanam Madabushi Srinivasan and Nisarg Vyas | Quantum reservoir computing with a single quantum chaotic node |
| 319 | Takahiro Kajisa | Quantum Scalar Field Theoretic Extension of Boltzmann Machines to Solve a Class of Moment Matching Problems |
| 301 | Shao Hen Chiew, Armando Angrisani, Zoë Holmes and Giuseppe Carleo | Quantum simulation in the Heisenberg picture via Vectorization |
| 151 | Donovan Slabbert, Dean Brand and Francesco Petruccione | Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels |
| 189 | Myeonghwan Seong, Yujin Kim, Chanyoung Kim, Daniel K. Park and Youngjoon Hong | Quantum spectral operator learning for solving partial differential equations |
| 131 | Tobias Fellner, David Kreplin, Samuel Tovey and Holm Christian | Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning |
| 203 | Nikita Kuznetsov and Ernesto Campos | Quantum-Inspired Self-Attention in a Large Language Model |
| 136 | Elizabeth Sarell, Ashwin Girish, Hector Spencer-Wood, Michael Puerrer, Christopher Messenger, Fiona Speirits and Sarah Croke | Reducing Circuit Depth of Amplitude Encoding for Gravitational Waves |
| 194 | Junghoon Justin Park, Jungwoo Seo, Sangyoon Bae, Samuel Yen-Chi Chen, Huan-Hsin Tseng, Jiook Cha and Shinjae Yoo | Resting-state fMRI Analysis using Quantum Time-series Transformer |
| 318 | Mingxuan Liu, Valerio Scarani, Alexia Auffeves and Kiarn Laverick | Retrodictive Approach to Quantum State Smoothing |
| 190 | Zihao Li, Huangjun Zhu and Masahito Hayashi | Robust and efficient verification of measurement-based quantum computation |
| 277 | Sixuan Wu, Zanqiu Shen, Chenghong Zhu, Guangxi Li and Xin Wang | Scalable Non-Stabilizerness Recognition with Machine Learning |
| 228 | Xiyao Feng, Chenghong Zhu, Xian Wu, Jingbo Wang, Guangxi Li and Xin Wang | ShuttleFormer: A Machine Learning Approach to Shuttle Scheduling in Trapped-Ion |
| 176 | Yi-Hsin Lin, Scott Smart and Prineha Narang | Signed Designs for Learning Quantum State Properties with Applications |
| 249 | Anastasiia Nikolaeva, Evgeniy Kiktenko, Ilia Zalivako, Alexander Gircha, Alexander Borisenko, Ilya Semerikov, Aleksey Fedorov and Nikolay Kolachevsky | Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor |
| 308 | Aldo Lamarre and Dominik Safranek | Tailor Made Embeddings For Quantum Machine Learning |
| 290 | Kaining Zhang, Junyu Liu, Liu Liu, Liang Jiang, Min-Hsiu Hsieh and Dacheng Tao | The curse of random quantum data and a cure from Pauli distribution |
| 337 | Leonardo Lavagna, Francesca De Falco and Massimo Panella | The Effectiveness of Classical and Hybrid Models for MaxCut problem |
| 186 | Steven Kordonowy and Hannes Leipold | The Lie Algebra of XY-mixer Topologies and Warm Starting QAOA for Constrained Optimization |
| 171 | Mina Abbaszadeh, Mariam Zomorodi, Mehrnoosh Sadrzadeh, Vahid Salari and Philip Kurian | Toward Quantum Machine Translation via Quantum Natural Language Processing |
| 46 | Mafalda Ramôa, Luis Santos, Nicholas Mayhall, Edwin Barnes and Sophia Economou | Towards Optimization-Free Adaptive Ansatze |
| 109 | Timothee Dao, Ege Yilmaz, Ibrahim Shehzad, Christophe Pere, Kumar Ghosh, Corey O'Meara, Giorgio Cortiana, Stefan Woerner and Francesco Tacchino | Towards quantum extreme learning and reservoir computing on utility-scale digital quantum processors |
| 305 | Nikhil Khatri, Stefan Zohren and Gabriel Matos | Trainability of Parameterised Linear Combinations of Unitaries |
| 284 | Monit Sharma and Hoong Chuin Lau | Transferable Equivariant Quantum Circuits for TSP: Generalization Bounds and Empirical Validation |
| 79 | Valentin Heyraud, Héloise Chomet and Jules Tilly | Unified Framework for Matchgate Classical Shadows |
| 311 | Gregory White, Petar Jurcevic, Charles Hill and Kavan Modi | Unifying non-Markovian characterisation with an efficient and self-consistent framework |
| 122 | Aaron Thomas, Harry Youel and Sharu Jose | VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priors |
| 202 | Xavi Font Aragones and Miguel Ángel González Ballester | Wavelet vision transformers and quantum pyramidal networks for biomedical image analysis |
| 299 | Slimane Thabet, Léo Monbroussou, Eliott Z. Mamon and Jonas Landman | When Quantum and Classical Models Disagree: Learning Beyond Minimum Norm Least Square |
Poster Session II: Tuesday
| EasyChairID | Authors | Title |
|---|---|---|
| 259 | Morounfoluwa Obidare, Ernesto Campos and Daniil Rabinovich | A distributed approach to quantum approximate optimization |
| 182 | Jiasheng Isaac Cheong, Marcia Zhang, Wei Kit Tan, Solomonraj Wilson, Tianqi Chen and Mai Chan Lau | A Hybrid Quantum-Classical AI Approach for Scalable and Precise Prediction of Cell-Level Molecular Biomarkers from Histology |
| 178 | Utkarsh Singh, Marco Armenta, Jean-Frederic Laprade, Aaron Z. Goldberg and Khabat Heshami | A Resource-Efficient Quantum Kernel for High-Dimensional Learning on NISQ Devices |
| 201 | Krzysztof Bieniasz and Hans-Martin Rieser | Active learning with quantics tensor networks |
| 195 | Junghoon Justin Park, Jiook Cha, Samuel Yen-Chi Chen, Huan-Hsin Tseng and Shinjae Yoo | Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles |
| 333 | Fute Wong | Advanced for-loop for QML algorithm search |
| 327 | Sebastian Nagies, Chiara Capecci, Kevin T. Geier, Marcel Seelbach Benkner, Javed Akram, Sebastian Rubbert, Dimitrios Bantounas, Michael Moeller, Michael Johanning and Philipp Hauke | Advances in Quantum Annealing: From PUBO Formulations to Practical Implementation |
| 294 | Hayata Yamasaki, Natsuto Isogai and Mio Murao | Advantage of Quantum Machine Learning from General Computational Advantages |
| 96 | Chirag Wadhwa, Laura Lewis, Elham Kashefi and Mina Doosti | Agnostic Process Tomography |
| 179 | Haiyue Kang, Younghun Kim, Eromanga Adermann, Martin Sevior and Muhammad Usman | Almost fault--tolerant quantum machine learning with drastic overhead reduction |
| 322 | Kensuke Kamisoyama, Lento Nagano and Koji Terashi | Analyzing Generalization Error in Quantum Kernel Methods using Random Matrix Theory |
| 173 | Dhiya Dharampal, Francesco Petruccione and Ilya Sinayskiy | Applications of Quantum Convolutional Neural Networks in Medical Image Processing |
| 204 | James Sud, Kunal Marwaha and Adrian She | Average-Case Algorithms for Local Hamiltonian Problems |
| 25 | Fariha Azad, Matteo Inajetovic, Stefan Kühn and Anna Pappa | Barren-plateau free variational quantum simulation of Z2 lattice gauge theories |
| 81 | Alexandra Ramôa and Luis Santos | Bayesian Quantum Amplitude Estimation |
| 258 | Antonio Mandarino, Christian Candeago, Paolo Da Rold, Michele Grossi and Pawel Horodecki | Characterization of distillable bipartite system enhanced by collective measurement and machine learning |
| 101 | Matteo D'Anna, Yuxuan Zhang, Roeland Wiersema and Juan Carrasquilla Alvarez | Circuit compression for 2D quantum dynamics |
| 329 | V Vijendran, Dax Enshan Koh, Ping Koy Lam and Syed M Assad | Classical and Quantum Heuristics for the Binary Paint Shop Problem |
| 145 | Matteo Inajetovic, Johannes Jung, Matic Petrič, Raphael Seidel and René Zander | Compilable QSVM with HHL in Qrisp |
| 12 | Tuyen Nguyen and Maria Kieferova | Convergence and Generalization of Warm-Starting Variational Quantum Algorithm |
| 92 | Aakash Ravindra Shinde, Arianne Meijer-van de Griend, Ilmo Salmenperä, Valter Uotila and Jukka K. Nurminen | Data Embedding on Two-Qubit interaction using Ising XYZ Hamiltonian model on Multiple Basis |
| 312 | Anurag Saha Roy | Differentiable Digital Twins & Machine Learning for Quantum Computing |
| 313 | Kin Ian Lo, Hala Hawashin, Mina Abbazadeh, Tilen Limback-Stokin and Mehrnoosh Mehrnoosh Sadrzadeh | DisCoCLIP: A Distributional Compositional Tensor Network Encoder for Vision-Language Understanding |
| 58 | Amena Khatun and Muhammad Usman | Distilling Quantum Adversarial Manipulations via Classical Autoencoders |
| 56 | Ivana Nikoloska, Hamdi Joudeh, Ruud van Sloun and Osvaldo Simeone | Dynamic Estimation Loss Control in Variational Quantum Sensing via Online Conformal Inference |
| 234 | Saad Amir, Anton Dekusar and Biswajit Basu | Effect of Hybrid Model Structure on Reinforcement Learning Performance |
| 163 | Rafael Gomez Lurbe | Efficient Estimation of the Quantum Fisher Information Matrix in Commuting-Block Variational Circuits |
| 232 | Yewei Yuan, Outongyi Lv and Nana Liu | Efficient Offline Reinforcement Learning via Quantum Reward Encoding |
| 100 | Peter Röseler, Oliver Schaudt, Helmut Berg, Christian Bauckhage and Matthias Koch | Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints |
| 119 | Francesco Ghisoni and Alessandro Berti | Efficient State Preparation with Bucket Brigade QRAM and Segment Tree |
| 320 | Emin Tarakci and Emine Can | Employing an Integrated MCDM with Quantum Fuzzy Logic in Next-Generation Communication |
| 285 | Jayne Thompson, Paul Riechers, Andrew Garner, Thomas Elliott and Mile Gu | Energetic advantages for quantum agents in online execution of complex strategies |
| 326 | Sebastian Nagies, Emiliano Tolotti, Davide Pastorello and Enrico Blanzieri | Enhancing Expressivity of Quantum Neural Networks Based on the SWAP test |
| 236 | Yixuan Hu, Mengru Ma and Jiangwei Shang | Error-mitigated quantum state tomography using neural networks |
| 107 | Martin Mauser, Solène Four, Lena Marie Predl, Riccardo Albiero, Francesco Ceccarelli, Roberto Osellame, Philipp Petersen, Borivoje Dakic, Iris Agresti and Philip Walther | Experimental data re-uploading with provable enhanced learning capabilities |
| 332 | Ritu Thombre and Lirandë Pira | Explaining Quanvolutional Neural Networks: A Frobenius Norm-Based Approach |
| 286 | Mariia Sobchuk, Arsalan Motamedi, Grecia Castelazo and Pooya Ronagh | Fast quantum algorithms for PDEs with pseudospectral Fourier method via preconditioning |
| 98 | Hyeondo Oh and Daniel Kyungdeock Park | Finding Lottery Tickets in Quantum Machine Learning: Sparse Trainability in Variational Quantum Circuits |
| 297 | Rupayan Bhattacharjee, Sergi Abadal, Carmen G. Almudever and Eduard Alarcón | Full-Stack Assessment Framework for Quantum Machine Learning Models |
| 218 | Matteo Robbiati and Andrea Papaluca | Full-stack quantum machine learning on self-hosted quantum devices with Qiboml |
| 132 | Tobias Fellner, Samuel Tovey, Christian Holm and Michael Spannowsky | Generating Quantum Reservoir State Representations with Random Matrices |
| 214 | Azadeh Alavi, Fatemeh Kouchmeshki, Abdolrahman Alavi, Jiayang Niu and Yongli Ren | Geometry-Aware Dictionary Learning and Quantum-Guided Bagging for Qubit-Efficient Recommender Systems |
| 272 | Yule Mayevsky, Akram Youssry, Ritik Sareen, Gerardo Paz-Silva and Alberto Peruzzo | Graybox Approach for Qudit System Identification and Control |
| 341 | Payal D. Solanki and Anh Pham | Harnessing Quantum Dynamics for Robust and Scalable Quantum Extreme Learning Machines |
| 283 | Monit Sharma and Hoong Chuin Lau | Hybrid Learning and Optimization Methods for Solving Capacitated Vehicle Routing Problem |
| 307 | Jian Feng Kong, Chee Kwan Gan, Stefano Carrazza and Jun Yong Khoo | Hybrid Quantum-Classical Neural Networks with Data Reuploading for Binary Medical Image Classification |
| 164 | Florian Heininger, Niels Halama, Sakshi Singh, Jeanette Miriam Lorenz and Matthias Weidemüller | Imbalanced classification with quantum kernel methods |
| 27 | Mafalda Ramôa, Panagiotis Anastasiou, Luis Santos, Edwin Barnes, Nicholas Mayhall and Sophia Economou | Improving Adaptive Variational Quantum Algorithms |
| 160 | Francesco Scala, Giacomo Guarnieri, Dario Gerace and Aurelien Lucchi | Improving Quantum Neural Networks performances by Noise-Induced Equalization |
| 17 | Arthur Mendonça Faria, Ignacio Fernández Graña and Savvas Varsamopoulos | Inductive Graph Representation Learning with Quantum Graph Neural Networks |
| 235 | Gordon Yuan Ning Ma, Ioannis Leonidas and Dimitris G. Angelakis | Information-Minimal Two-Body Moment Learning for Scalable QUBO Optimisation |
| 117 | Aoi Hayashi, Akitada Sakurai and Kae Nemoto | Information-theoretic evaluation of quantum machine learning model complexity |
| 22 | Doron Podoleanu | Interference Beats Hallucination? A Controlled Study of Hybrid Quantum–Classical Language Models for Code Generation |
| 239 | Nicola Assolini, Luca Marzari, Isabella Mastroeni and Alessandra Di Pierro | Interval-based Analysis of Variational Quantum Algorithms |
| 338 | Leonardo Lavagna, Francesca De Falco and Massimo Panella | Is the QAOA the Ultimate Solution for the MaxCut problem? |
| 99 | Yuqi Li, Zhouhang Shi, Li Shen, Haitao Ma, Jinge Bao and Yunlong Xiao | Language Model for Large-Text Transmission in Noisy Quantum Communications |
| 157 | Jinge Bao and Francisco Escudero Gutiérrez | Learning junta distributions, quantum junta states, and QAC0 circuits |
| 106 | Eric Brunner, Luuk Coopmans, Gabriel Matos, Matthias Rosenkranz, Frederic Sauvage and Yuta Kikuchi | Lindblad engineering for quantum Gibbs state preparation under the eigenstate thermalization hypothesis |
| 180 | Siddhartha Emmanuel Morales Guzman | Low Rank Piecewise Polynomial Data Encoding |
| 138 | Roselyn Nmaju, Sarah Croke and Fiona Speirits | Low-depth measurement-based deterministic quantum state preparation |
| 44 | Alice Barthe, Casper Gyurik, Jordi Tura, Vedran Dunjko and Patrick Emonts | Lower Bounding Betti Numbers using Tensor Networks |
| 199 | Prashasti Tiwari | Many Body Eigenvalue Problems with a Trapped Ion System |
| 112 | Hector Spencer-Wood, John Jeffers and Sarah Croke | Measurement disturbance tradeoffs in unsupervised quantum classification |
| 159 | Yang Qian, Xinbiao Wang, Yuxuan Du, Yong Luo and Dacheng Tao | MG-Net: Learn to Customize QAOA with Circuit Depth Awareness |
| 222 | Korbinian Stein, Davide Bincoletto and Jakob Kottmann | Modeling Quantum Circuit Parameterswith Size-Independent Machine Learning Models |
| 245 | Illia Lukin, Vladyslav Bohun, Mykola Lukhanko and Maciej Koch-Janusz | MPS-based Fourier series loading |
| 137 | Wenyu Guo, Qing Liu, Ximing Wang, Chufan Lyu, Jayne Thompson, Andrew J.P. Garner, Aaron Tranter, Rigui Zhou, Chengran Yang and Mile Gu | Neural network-assisted quantum compilation |
| 141 | Seongwook Shin, Ryan Sweke and Hyunseok Jeong | New perspectives on quantum kernels through the lens of entangled tensor kernels |
| 7 | Natchapol Patamawisut and Yen-Jui Chang | Nonlinear Quantum Image Encoding via Information Mixing |
| 105 | Nicolo Colombo and Thomas Gargan | On Bounding Quantum Fidelity with Conformal Prediction |
| 275 | Guilherme Adamatti Bridi, Debbie Lim, Lirandë Pira, Raqueline Azevedo Medeiros Santos, Franklin de Lima Marquezino and Soumik Adhikary | On the Necessity of Overparameterization in the Quantum Walk Optimization Algorithm |
| 315 | Rebecca Erbanni, Gregory A. L. White, Jens Eisert and Matthias C. Caro | On the structure of easy and hard-to-learn positive MPOs |
| 74 | Niclas Götting, Steffen Wilksen, Alexander Steinhoff, Frederik Lohof and Christopher Gies | Optically Probing Quantum Reservoir Memory |
| 5 | Bodo Rosenhahn, Tobias J. Osborne and Christoph Hirche | Optimization Driven Quantum Circuit Reduction |
| 223 | Chenghong Zhu, Hongshun Yao, Yingjian Liu and Xin Wang | Optimizer-Dependent Generalization Bound for Quantum Neural Networks |
| 30 | Benjamin Stott, Grégoire Barrué and Tony Quertier | Photonic Quantum Kernel methods for Malware Classification |
| 241 | Marco Parigi, Stefano Martina, Francesco Aldo Venturelli and Filippo Caruso | Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion |
| 87 | Taisei Nohara, Itsuki Noda and Satoshi Oyama | Positive-Unlabeled Learning for Training an Entanglement Detector |
| 63 | Takao Tomono and Kazuya Tsujimura | Potential of multi-anomalies detection using quantum machine learning |
| 146 | Aaron Thomas, Yu-Cheng Chen, Hubert Valencia, Sharu Jose and Ronin Wu | QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning |
| 325 | Nouhaila Innan and Muhammad Shafique | Q-Compression: Quantum-Aware Model Compression Techniques for Scalable Quantum Machine Learning |
| 23 | Petr Ivashkov, Po-Wei Huang, Kelvin Koor, Lirandë Pira and Patrick Rebentrost | QKAN: Quantum Kolmogorov-Arnold Networks |
| 68 | Anderson Melchor Hernandez, Filippo Girardi, Davide Pastorello and Giacomo De Palma | Quantitative convergence of trained quantum neural networks to a Gaussian process |
| 128 | Ge Bai, Francesco Buscemi and Valerio Scarani | Quantum Bayes’ rule from a minimum change principle: gradient-free belief updates for quantum learning |
| 85 | Natchapol Patamawisut and Ruchipas Bavontaweepanya | Quantum Circuit Optimization for Variational Grover's Search via ZX Calculus |
| 280 | Kouhei Nakaji, Jonathan Wurtz, Haozhe Huang, Luis Mantilla Calderon, Karthik Panicker, Elica Kyoseva and Alán Aspuru-Guzik | Quantum circuits as a game: A reinforcement learning agent for quantum compilation and its application to reconfigurable neutral atom arrays |
| 238 | Hoang Anh Nguyen, Nhu Duc Dinh, Le Tu Uyen Tu and Van Duy Nguyen | Quantum Deep Learning Force Field |
| 14 | Neeshu Rathi and Sanjeev Kumar | Quantum Ensemble Learning with QRAM-Based Subsampling and Shallow Clustering Weak Learners |
| 158 | Borja Aizpurua, Saeed Jahromi, Sukhbinder Singh and Román Orús | Quantum Large Language Models via Tensor Network Disentanglers |
| 246 | Riccardo Molteni, Simon C. Marshall and Vedran Dunjko | Quantum machine learning advantages beyond hardness of evaluation |
| 267 | Hon Wai Lau, Aoi Hayashi, William John Munro, Jayne Thompson and Mile Gu | Quantum Memory Resource Advantage in Reinforcement Learning |
| 75 | João F. Bravo, Daniel Basilewitsch, Christian Tutschku and Frederick Struckmeier | Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images |
| 254 | Danijela Markovic | Quantum neuromorphic computing with parametrically coupled bosonic modes in superconducting resonators |
| 224 | Sreetama Das, Gian Luca Giorgi and Roberta Zambrini | Quantum reservoir computing for temporal series processing using spin-boson systems |
| 255 | Michał Siemaszko, Martin Mauser, Iris Agresti, Philip Walther and Magdalena Stobińska | Quantum reservoir computing with multiple photonic memristors and skip connections |
| 330 | Luke Antoncich, Milan Kornjaca, Jonathan Wurtz, Jing Chen, Pascal Elahi and Casey Myers | Quantum Reservoir Computing with Small Datasets |
| 156 | Maitreyee Sarkar, Lisa Roy, Aksah Gutal, Atul Kumar and Manikandan Paranjothy | Quantum Simulations of Chemical Reactions: Achieving Accuracy with NISQ Devices |
| 268 | Ritik Sareen, Akram Youssry, Yule Mayevsky and Alberto Peruzzo | Quantum State Preparation using Dynamical Invariants and Machine Learning |
| 265 | Luis Ernesto Campos Espinoza and Dmitry Guskov | Quantum variational parameters as classical data outputs |
| 339 | Soham Pawar and Dibakar Das | Quantum-Hybrid Siamese Networks with Inter-Channel Weight Interaction |
| 34 | Hideki Okawa, Qing-Guo Zeng, Xian-Zhe Tao and Man-Hong Yung | Quantum-Inspired Optimization for High Energy Physics |
| 278 | Seungcheol Oh, Chaemoon Im and Joongheon Kim | Qubit Trajectory Analysis in Quantum Neural Networks |
| 90 | Chengsi Mao, Changhao Yi and Huangjun Zhu | Qudit shadow estimation based on the Clifford group and the power of a single magic gate |
| 309 | Nikhil Khatri, Gabriel Matos, Luuk Coopmans and Stephen Clark | Quixer: A Quantum Transformer Model |
| 233 | Hoang-Anh Nguyen, Duy-Tung Nguyen, Tran-Hien Vo, Dang-Khanh Nguyen, Nhu-Duc Dinh and Van-Duy Nguyen | Qutrit-Based Quantum Circuit Design via Reinforcement Learning for Simulating Three-Flavor Collective Neutrino Oscillations |
| 36 | Felix J. Beckmann and João F. Bravo | Ravines in quantum cost landscapes: Opportunities for enhanced VQA predictions on quantum data |
| 64 | Maxwell West, Antonio Anna Mele, Martín Larocca and Marco Cerezo | Real classical shadows |
| 316 | Abdullah Kazi and Jayesh Hire | Recurrent Measurement-Based Quantum Machine Learning (MBQML) with Feedback |
| 124 | Jan Nogué Gómez | Reinforcement Learning Based Quantum Circuit Optimization via ZX-Calculus |
| 140 | Weijie Xiong, Zoe Holmes, Armando Angrisani, Yudai Suzuki, Thiparat Chotibut and Supanut Thanasilp | Role of scrambling, noise, and symmetry in temporal learning with quantum systems |
| 177 | Hongshun Yao, Yingjian Liu, Tengxiang Lin and Xin Wang | Sample-Efficient Estimation of Nonlinear Quantum State Functions |
| 225 | Chenghong Zhu, Xian Wu, Hao-Kai Zhang, Sixuan Wu, Guangxi Li and Xin Wang | Scalable Quantum Architecture Search based on Relative Fluctuation of Landscapes |
| 216 | Antonin Sulc | Self-Optimizing Quantum Circuits via Reinforcement Learning and Language Models |
| 289 | Su Yeon Chang, Supanut Thanasilp, Zoë Holmes and Marco Cerezo | Simulation cost of classically simulable quantum machine learning models |
| 206 | Jonathan Teo, Xin Wei Lee and Hoong Chuin Lau | Size-Invariant Properties at Depth 1 of the Equivariant Quantum Circuit |
| 264 | Callum Duffy, Marcin Jastrzebski and Sarah Malik | Spectral Bias in Parameterised Quantum Circuits |
| 89 | Vardaan Mongia, Abhishek Kumar, Shashi Prabhakar and R.P. Singh | Strengthening the no-go theorem for QRNGs |
| 191 | Léo Monbroussou, Jonas Landman, Letao Wang, Alex Bredariol Grilo and Elham Kashefi | Subspace Preserving Quantum Convolutional Neural Network Architectures |
| 153 | Jun Qi, Chao-Han Huck Yang and Pin-Yu Chen | Tensor Network-Enhanced Variational Quantum Circuits: Theory, Hybrid Architectures, and Scalable Optimization for Quantum Machine Learning |
| 310 | Mario Herrero-González, Ross Grassie, Kieran McDowall, Sjoerd Beentjes, Ava Khamseh and Elham Kashefi | The Born Ultimatum: Simulability in Quantum Generative Models. Capturing Higher-Order Moments from Data. |
| 250 | Ilya Sinayskiy | The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices |
| 269 | Muhammad Al-Zafar Khan, Jamal Al-Karaki, Assala Benmalek, Abdullah Al Omar Ghalib and Marwan Omar | The Ubiquity of QPINNs: A Multi-Domain Study |
| 127 | Zherui Wang, Jordi Tura and Patrick Emonts | Topological data analysis with variational quantum circuits |
| 123 | Caesnan Leditto, Angus Southwell, Behnam Tonekaboni, Muhammad Usman, Kavan Modi and Gregory White | Topological Signal Processing on Quantum Computers for Higher-Order Network Analysis |
| 231 | Eleanor Kedem, Ryan Sweke and Francesco Petruccione | Toward Undetectable Backdoors in Variational Quantum Models |
| 53 | Adrián Pérez-Salinas, Berta Casas, Xavier Bonet-Monroig and Hao Wang | Towards a Framework for Analyzing Quantum Machine Learning Algorithms |
| 162 | Brian Coyle, Snehal Raj and El Amine Cherrat | Training parameterized quantum circuits with forward gradients |
| 237 | Yunting Li and Huangjun Zhu | Universal and Efficient Quantum State Verification via Schmidt Decomposition and Mutually Unbiased Bases |
| 54 | Adrián Pérez-Salinas, Mahtab Yaghubi Rad, Alice Barthe and Vedran Dunjko | Universal approximation of continuous functions with minimal quantum circuits |
| 282 | Kosuke Ito, Hiroshi Yano, Yudai Suzuki, Akira Tanji and Naoki Yamamoto | Unsupervised domain adaptation for quantum data under state and distribution shifts |
| 69 | M. Emre Sahin, Cenk Tüysüz, Edoardo Altamura, David Galvin, Oscar Wallis, Alex Gregory, Paul Edwards, Stefano Mensa and Christa Zoufal | Usable Information in Quantum Machine Learning |
| 247 | Vladyslav Los and Maciej Koch-Janusz | Variational bounds of quantum information-theoretic quantities for quantum error correcting code analytics |
| 118 | Benchi Zhao and Keisuke Fujii | Variational quantum Hamiltonian engineering |
| 57 | Alessio Pecilli and Matteo Rosati | Variational quantum self-attention for the prediction of classical and quantum data sequences |
| 84 | Fong Yew Leong, Dax Enshan Koh, Jian Feng Kong, Siong Thye Goh, Jun Yong Khoo, Wei-Bin Ewe, Hongying Li, Jayne Thompson and Dario Poletti | Variational Quantum Solver for Time-Fractional Differential Equations with Efficient Memory Scaling |
| 73 | Annie Paine, Smit Chaudhary and Antonio Gentile | Weak Form Regularisation for Solving Differential Equations with Quantum Neural Networks |
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