Poster Sessions
Poster Presentation Preparation Instructions
- All posters should be based on the submitted abstract that have been accepted
by the Program 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 the first-time presenters, guides on preparing impactful poster are abundant
online, and you can find some inspirations 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.
Set-up & Removal of your Posters
- The poster session is located next to the conference room.
- 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 organizing Committee is not responsible for any damage or
loss of posters. Any posters remaining after the removal time will be removedand discarded after the event.
| Submission Number | Â | Date | Time |
| Poster Set-up | 17 Nov 2025, Monday | 12.30pm – 6.00pm | |
| Poster Presentation | 17 Nov 2025, Monday | 4.30pm – 6.00pm | |
| Poster Removal | 17 Nov 2025, Monday | 6.00pm | |
| Poster Display | 18 Nov 2025, Tuesday | 12.30pm – 6.00pm | |
| Poster Presentation | 18 Nov 2025, Tuesday | 4.30pm – 6.00pm | |
| Poster Removal | 18 Nov 2025, Tuesday | 6.00pm |
Monday Session
| EasyChairID | Title |
|---|---|
| A Bit of Freedom Goes a Long Way: Quantum and Classical Algorithms for Online Learning of MDPs under a Generative Model | |
| A compositional framework for leveraging quantum learning to circumvent no-go theorems | |
| A distributed approach to quantum approximate optimization | |
| A faster converging qDRIFT algorithm with application to Hamiltonian data encoding | |
| A good basis allows for classical shadows with arbitrary group representations | |
| A Hybrid Quantum-Classical AI Approach for Scalable and Precise Prediction of Cell-Level Molecular Biomarkers from Histology | |
| A Practical Cross-Platform, Multi-Algorithm Study of Quantum Optimisation for Configurational Analysis of Materials | |
| A Resource-Efficient Quantum Kernel for High-Dimensional Learning on NISQ Devices | |
| A Resource-Efficient Quantum-Classical Model for Protein–Ligand Binding Affinity Prediction | |
| A study of face recognition system | |
| A Study on Stabilizer R\'enyi Entropy Estimation using Machine Learning | |
| A Unified Frequency Principle for Quantum and Classical Machine Learning | |
| A Unified Theory of Quantum Neural Network Loss Landscapes | |
| A unifying account of warm start guarantees for patches of quantum landscapes | |
| Accelerating Inference for Multilayer Convolutional Neural Networks with Quantum Computers | |
| Active learning with quantics tensor networks | |
| Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles | |
| Advanced Ensemble Smart Classifications for Nifty Smart Market Trends | |
| Advances in Quantum Annealing: From PUBO Formulations to Practical Implementation | |
| Agnostic Process Tomography | |
| AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation | |
| Almost fault--tolerant quantum machine learning with drastic overhead reduction | |
| An Attention-Based Quantum Phase Transition Detection on NISQ Devices | |
| An efficient approach to realize Quantum Random Features | |
| Analyzing Generalization Error in Quantum Kernel Methods using Random Matrix Theory | |
| Applications of Quantum Convolutional Neural Networks in Medical Image Processing | |
| Applying Many Worlds Interpretation in Quantum Information Theory | |
| Auxiliary-Free Replica Shadows: Efficient Estimation of Multiple Nonlinear Quantum Properties | |
| Average-Case Algorithms for Local Hamiltonian Problems | |
| Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization | |
| Barren plateau-free and noise-robust quantum advantage for learning data with group symmetries | |
| Bayesian Learning of Quantum Hardware Dynamics | |
| Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection | |
| Benchmarking Quantum Algorithms for Gaussian Process Regression | |
| Bridging Remote Sensing and Quantum Computing: Snow Depth Estimation with LSTM and QLSTM | |
| Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit | |
| Certifying Adversarial Robustness in Quantum Machine Learning: From Theory to Physical Validation | |
| Certifying Optimality of VQA Solutions via Sparse SOS Hierarchies | |
| Challenges and limitations of quantum kernel methods | |
| Characterizing quantum resourcefulness via group-Fourier decompositions | |
| Circuit compression for 2D quantum dynamics | |
| Classical and Quantum Heuristics for the Binary Paint Shop Problem | |
| Classical-quantum hybrid support vector data description for one-class classification | |
| Classification of Quantum Correlations via Quantum-inspired Machine Learning | |
| Compilable QSVM with HHL in Qrisp | |
| Conservative Quantum Offline Model-Based Optimization | |
| Convergence and Generalization of Warm-Starting Variational Quantum Algorithm | |
| Data Clustering as a Quantum Computing Use-Cas | |
| Data Embedding on Two-Qubit interaction using Ising XYZ Hamiltonian model on Multiple Basis | |
| Decoded Quantum Interferometry | |
| Deep Reinforcement Learning for real-time context-aware gate calibration | |
| Dequantization and expressivity in photonic quantum Fourier models | |
| Designing Privacy-Preserving Architectures in Quantum Federated Learning | |
| Designing quantum machine learning models for graphs | |
| Differentiable Digital Twins & Machine Learning for Quantum Computing | |
| Digital–analog quantum learning on Rydberg atom arrays | |
| DisCoCLIP: A Distributional Compositional Tensor Network Encoder for Vision-Language Understanding | |
| Distilling Quantum Adversarial Manipulations via Classical Autoencoders | |
| Distilling the knowledge with quantum neural networks | |
| Do you know what q-means? | |
| Double Descent in Quantum Kernel Methods | |
| Double-bracket quantum algorithms for ground-state preparation via cooling | |
| Dynamical Regimes and Memory Performance in Quantum Reservoirs: Insights from Random Matrix Theory | |
| Effect of Hybrid Model Structure on Reinforcement Learning Performance | |
| Efficient Estimation of the Quantum Fisher Information Matrix in Commuting-Block Variational Circuits | |
| Efficient learning for linear properties of bounded-gate quantum circuits | |
| Efficient Offline Reinforcement Learning via Quantum Reward Encoding | |
| Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints | |
| Efficient State Preparation with Bucket Brigade QRAM and Segment Tree | |
| EHands: Quantum Protocol for Polynomial Computation on Real-Valued Encoded States | |
| Employing an Integrated MCDM with Quantum Fuzzy Logic in Next-Generation Communication | |
| Energetic advantages for quantum agents in online execution of complex strategies | |
| Enhancing Expressivity of Quantum Neural Networks Based on the SWAP test | |
| Ensemble Techniques for Multi-Label Text Classification: A Study Using the Aviation Safety Reporting System Dataset | |
| Entanglement detection via machine learning techniques | |
| Entanglement scaling in matrix product state representation of smooth functions and their shallow quantum circuit approximations | |
| Entanglement-induced provable and robust quantum learning advantages | |
| Error-mitigated quantum state tomography using neural networks | |
| Experimental data re-uploading with provable enhanced learning capabilities | |
| Experimental quantum memristor-based reservoir computing | |
| Explaining Quanvolutional Neural Networks: A Frobenius Norm-Based Approach | |
| Expressive equivalence of classical and quantum restricted Boltzmann machines | |
| Expressivity Limits of Quantum Reservoir Computing | |
| Fake News Detection using Hybrid Classical-Quantum Transfer Learning Approach | |
| Fast quantum algorithms for PDEs with pseudospectral Fourier method via preconditioning | |
| Fast, Accurate and Interpretable Graph Classification with Topological Kernels: An Even More Scalable Alternative to Weisfeiler-Lehman Kernels | |
| Finding Lottery Tickets in Quantum Machine Learning: Sparse Trainability in Variational Quantum Circuits | |
| Flexible quantum Kolmogorov-Arnold networks via generalized fractional-order Chebyshev functions and trainable QSVT basis | |
| Forecasting the Lorenz System Using a Hierarchical Tensor Network Model | |
| Fourier Fingerprints of Ansatzes in Quantum Machine Learning | |
| From Bits to Qubits: Comparative Insights into Embedding Strategies for Machine Learning | |
| Full-Stack Assessment Framework for Quantum Machine Learning Models | |
| Full-stack quantum machine learning on self-hosted quantum devices with Qiboml | |
| Generalization Bounds in Hybrid-Quantum Machine Learning Models | |
| Generating Quantum Reservoir State Representations with Random Matrices | |
| Grover's algorithm with W state-based initialization for solving the exact-cover problem | |
| Hamiltonian Locality Testing via Trotterized Postselection | |
| Hardware Adapted Quantum Machine Learning with Pulse-Level optimization | |
| Harnessing quantum back-action for time-series processing | |
| Harnessing Quantum Dynamics for Robust and Scalable Quantum Extreme Learning Machines | |
| Heuristic ansatz design for trainable ion-native digital-analog quantum circuits | |
| Hybrid Learning and Optimization Methods for Solving Capacitated Vehicle Routing Problem | |
| Hybrid Parameterized Quantum States for Variational Quantum Learning | |
| Hybrid Quantum Kolmogorov-Arnold Networks for High Energy Physics Analysis at the LHC | |
| Hybrid Quantum Transfer Learning Models for Credit Risk Assessment | |
| Hybrid quantum-classical heuristics for optimizing large separable operators | |
| Hybrid Quantum-Classical Neural Networks with Data Reuploading for Binary Medical Image Classification | |
| Hybrid Quantum-Classical Traffic Flow Classification with Deep Feature Extraction | |
| Imbalanced classification with quantum kernel methods | |
| Implementing Quantum Transformers on Trapped Ion Devices | |
| Improving Adaptive Variational Quantum Algorithms | |
| Improving Quantum Neural Networks performances by Noise-Induced Equalization | |
| Inductive Graph Representation Learning with Quantum Graph Neural Networks | |
| Information plane and compression-gnostic feedback in quantum machine learning | |
| Information-Minimal Two-Body Moment Learning for Scalable QUBO Optimisation | |
| Information-theoretic evaluation of quantum machine learning model complexity | |
| Interactive proofs for verifying (quantum) learning and testing | |
| Interference Beats Hallucination? A Controlled Study of Hybrid Quantum–Classical Language Models for Code Generation | |
| Interpretable Machine Learning for Quantum Control | |
| Interval-based Analysis of Variational Quantum Algorithms | |
| Is data-efficient learning feasible with quantum models? | |
| Kernel-based Dequantization of Quantum Machine Learning | |
| Language Model for Large-Text Transmission in Noisy Quantum Communications | |
| Learning junta distributions, quantum junta states, and QAC0 circuits | |
| Learning Physically Consistent Quantum Decoherence Simulation | |
| Learning pure quantum states (almost) without regret | |
| Learning Quantum States with Tunable Loss Functions | |
| Learning to erase quantum states: thermodynamic implications of quantum learning theory | |
| Learning to generate high-dimensional distributions with low-dimensional quantum Boltzmann machines | |
| Learning Unitaries with Quantum Statistical Queries | |
| Lindblad engineering for quantum Gibbs state preparation under the eigenstate thermalization hypothesis | |
| Low Cost Experimental Design for Frequency Estimation | |
| Low Rank Piecewise Polynomial Data Encoding | |
| Low-depth measurement-based deterministic quantum state preparation | |
| Lower Bounding Betti Numbers using Tensor Networks | |
| Machine Learning Derived Entanglement Witnesses with Trainable Measurements | |
| MANTIS: Multiple Anomaly-Detection Networks for Tensor Inspired Solutions | |
| Many Body Eigenvalue Problems with a Trapped Ion System | |
| Measurement disturbance tradeoffs in unsupervised quantum classification | |
| MG-Net: Learn to Customize QAOA with Circuit Depth Awareness | |
| Modeling Quantum Circuit Parameterswith Size-Independent Machine Learning Models | |
| More-efficient Quantum Multivariate Mean Value Estimator from Generalized Grover Operator | |
| MPS-based Fourier series loading | |
| Multiple photons enhance data efficiency in quantum machine learning | |
| Multiple-basis representation of quantum states | |
| Natural gradient and parameter estimation for quantum Boltzmann machines |
Tuesday Session
| EasyChairID | Title |
|---|---|
| Near-Optimal Parameter Tuning of Level-1 QAOA for Ising Models | |
| Neural network-assisted quantum compilation | |
| Neural Quantum Embedded Self-supervised Learning | |
| New aspects of quantum topological data analysis: Betti number estimation, and testing and tracking of homology and cohomology classes | |
| New perspectives on quantum kernels through the lens of entangled tensor kernels | |
| Nonlinear Quantum Image Encoding via Information Mixing | |
| Nonstabilizerness Enhances Thrifty Shadow Estimation | |
| On Bounding Quantum Fidelity with Conformal Prediction | |
| On the Generalization of Adversarially Trained Quantum Classifiers | |
| On the Necessity of Overparameterization in the Quantum Walk Optimization Algorithm | |
| On the structure of easy and hard-to-learn positive MPOs | |
| Optically Probing Quantum Reservoir Memory | |
| Optimal Haar random fermionic linear optics circuits | |
| Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning | |
| Optimization Driven Quantum Circuit Reduction | |
| Optimization Framework for Data-Adaptive and Hardware-Efficient Quantum Data Embedding | |
| Optimizer-Dependent Generalization Bound for Quantum Neural Networks | |
| Optimizing Quantum Time Dynamics with Classical Support | |
| Optimizing Shadow Tomography for Many-Body Observables | |
| Parameterized IQP-QCBM generative model: universality with hidden units and kernel-adaptive efficient training on classical hardware | |
| Partition function estimation with a quantum coin toss | |
| Pauli Propagation: A computational framework for simulating quantum systems | |
| Photonic Quantum Kernel methods for Malware Classification | |
| Physics-Informed Neural Networks for Simulating Open Quantum Systems | |
| Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion | |
| Pitfalls in the hunt for scalable parameterized quantum models | |
| Polynomial Speed-Up in Photonic Neural Networks via Adaptive State Injection | |
| Positive-Unlabeled Learning for Training an Entanglement Detector | |
| Potential of multi-anomalies detection using quantum machine learning | |
| Probabilistic Greedy Behaviour of the Equivariant Quantum Circuit | |
| Problem-informed Graphical Quantum Generative Learning | |
| Prospects for quantum advantage in ML from the representability of quantum functions | |
| Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise | |
| QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning | |
| QCirCNN: a photonic-native quantum circular convolution network based on circulant matrices | |
| "Q-Compression: Quantum-Aware Model Compression Techniques for Scalable Quantum Machine Learning | |
| QKAN: Quantum Kolmogorov-Arnold Networks | |
| Quantum Advantage in Learning Quantum Dynamics | |
| Quantum Algorithm for Solving Nonlinear Differential Equations Based on Physics-Informed Effective Hamiltonians | |
| Quantum algorithms for representation-theoretic multiplicities | |
| Quantum Bayes’ rule from a minimum change principle: gradient-free belief updates for quantum learning | |
| Quantum Chebyshev Probabilistic Models for Fragmentation Functions | |
| Quantum Circuit simulation with a local time-dependent variational principle | |
| Quantum circuits as a game: A reinforcement learning agent for quantum compilation and its application to reconfigurable neutral atom arrays | |
| Quantum computing and persistence in TDA | |
| Quantum Convolutional Neural Network for Color Image Classification | |
| Quantum convolutional neural networks produce higher variance in regression tasks | |
| Quantum Deep Learning Force Field | |
| Quantum Differential Privacy in Quantum Federated Learning | |
| Quantum Ensemble Learning with QRAM-Based Subsampling and Shallow Clustering Weak Learners | |
| Quantum Extreme Learning Machines: Insights from Industrial and Real-World Applications | |
| Quantum Feature Maps for High Frequency Time Series | |
| Quantum generative diffusion models for medical imaging | |
| Quantum Graph Neural Networks for the Travelling Salesman Problem | |
| Quantum HodgeRank: Topology-Based Rank Aggregation on Quantum Computers | |
| Quantum Hyperdimensional Computing for Pattern Completion | |
| Quantum Large Language Models via Tensor Network Disentanglers | |
| Quantum machine learning advantages beyond hardness of evaluation | |
| Quantum medical image encoding and compression using Fourier-based methods | |
| Quantum Memory Resource Advantage in Reinforcement Learning | |
| Quantum multiple kernel learning with entropy power inequalities | |
| Quantum Neural Density Functionals in Density Functional Theory | |
| Quantum Neural Networks Facilitating Quantum State Classification | |
| Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images | |
| Quantum neuromorphic computing with parametrically coupled bosonic modes in superconducting resonators | |
| Quantum Optimization Towards Large-Scale Molecular Docking on a Quantum Computer | |
| Quantum Principal Basis Learning (qPBL) for image classification | |
| Quantum Recurrent Embedding Neural Network | |
| Quantum reservoir computing with a single quantum chaotic node | |
| Quantum reservoir computing with multiple photonic memristors and skip connections | |
| Quantum simulation in the Heisenberg picture via Vectorization | |
| Quantum simulation with sum-of-squares spectral amplification | |
| Quantum Simulations of Chemical Reactions: Achieving Accuracy with NISQ Devices | |
| Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels | |
| Quantum spectral operator learning for solving partial differential equations | |
| Quantum State Preparation using Dynamical Invariants and Machine Learning | |
| Quantum state-agnostic work extraction (almost) without dissipation | |
| Quantum thermodynamics, semidefinite optimization, and quantum Boltzmann machines | |
| Quantum variational parameters as classical data outputs | |
| Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning | |
| Quantum-Hybrid Siamese Networks with Inter-Channel Weight Interaction | |
| Quantum-Inspired Optimization for High Energy Physics | |
| Quantum-Inspired Self-Attention in a Large Language Model | |
| Quartic quantum speedups for planted inference | |
| Qubit Trajectory Analysis in Quantum Neural Networks | |
| Qudit shadow estimation based on the Clifford group and the power of a single magic gate | |
| Quixer: A Quantum Transformer Model | |
| Qutrit-Based Quantum Circuit Design via Reinforcement Learning for Simulating Three-Flavor Collective Neutrino Oscillations | |
| Real classical shadows | |
| Recurrent Measurement-Based Quantum Machine Learning (MBQML) with Feedback | |
| Reducing Circuit Depth of Amplitude Encoding for Gravitational Waves | |
| Reinforcement Learning Based Quantum Circuit Optimization via ZX-Calculus | |
| Retrodictive Approach to Quantum State Smoothing | |
| Robust and efficient verification of measurement-based quantum computation | |
| Role of scrambling, noise, and symmetry in temporal learning with quantum systems | |
| Sample importance in data-driven decoding | |
| Sample-Efficient Estimation of Nonlinear Quantum State Functions | |
| Scalable Non-Stabilizerness Recognition with Machine Learning | |
| Scalable Quantum Architecture Search based on Relative Fluctuation of Landscapes | |
| Scalable, hardware-efficient and noise-aware multivariate quantum state preparation | |
| Self-Optimizing Quantum Circuits via Reinforcement Learning and Language Models | |
| Shedding light on classical shadows | |
| ShuttleFormer: A Machine Learning Approach to Shuttle Scheduling in Trapped-Ion | |
| Size-Invariant Properties at Depth 1 of the Equivariant Quantum Circuit | |
| Spectral Bias in Parameterised Quantum Circuits | |
| StoCQS: stochastic strategy for Ansatz tree construction in Krylov-based linear solver | |
| Strengthening the no-go theorem for QRNGs | |
| Subspace Preserving Quantum Convolutional Neural Network Architectures | |
| Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor | |
| Tailor Made Embeddings For Quantum Machine Learning | |
| Tensor Network-Enhanced Variational Quantum Circuits: Theory, Hybrid Architectures, and Scalable Optimization for Quantum Machine Learning | |
| Testing classical properties from quantum data | |
| The abelian state hidden subgroup problem: Learning stabilizer groups and beyond | |
| The Born Ultimatum: Simulability in Quantum Generative Models. Capturing Higher-Order Moments from Data. | |
| The curse of random quantum data and a cure from Pauli distribution | |
| The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices | |
| The Effectiveness of Classical and Hybrid Models for MaxCut problem | |
| The Lie Algebra of XY-mixer Topologies and Warm Starting QAOA for Constrained Optimization | |
| The Ubiquity of QPINNs: A Multi-Domain Study | |
| Tolerant Testing of Stabilizer States with Mixed State Inputs | |
| Topological data analysis with variational quantum circuits | |
| Topological Signal Processing on Quantum Computers for Higher-Order Network Analysis | |
| Toward Quantum Machine Translation via Quantum Natural Language Processing | |
| Towards a Framework for Analyzing Quantum Machine Learning Algorithms | |
| Towards Optimization-Free Adaptive Ansatze | |
| Towards quantum extreme learning and reservoir computing on utility-scale digital quantum processors | |
| Trainability of Parameterised Linear Combinations of Unitaries | |
| Training parameterized quantum circuits with forward gradients | |
| Transferable Equivariant Quantum Circuits for TSP: Generalization Bounds and Empirical Validation | |
| Unified Framework for Matchgate Classical Shadows | |
| Unifying non-Markovian characterisation with an efficient and self-consistent framework | |
| Universal and Efficient Quantum State Verification via Schmidt Decomposition and Mutually Unbiased Bases | |
| Universal approximation of continuous functions with minimal quantum circuits | |
| Unsupervised domain adaptation for quantum data under state and distribution shifts | |
| Usable Information in Quantum Machine Learning | |
| VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priors | |
| Variational bounds of quantum information-theoretic quantities for quantum error correcting code analytics | |
| Variational LOCC-assisted quantum circuits for long-range entangled states | |
| Variational quantum algorithms with exact geodesic transport | |
| Variational quantum Hamiltonian engineering | |
| Variational quantum self-attention for the prediction of classical and quantum data sequences | |
| Variational Quantum Solver for Time-Fractional Differential Equations with Efficient Memory Scaling | |
| Verifiable End-to-End Delegated Variational Quantum Algorithms | |
| Wavelet vision transformers and quantum pyramidal networks for biomedical image analysis | |
| Weak Form Regularisation for Solving Differential Equations with Quantum Neural Networks | |
| When Quantum and Classical Models Disagree: Learning Beyond Minimum Norm Least Square |
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