QUANTICS 2026 Abstracts

Area 1 - Communications, Advances in Technology and Applications

Short Papers

Paper Nr:	18
Title:	Quantum State Tomography of Free-Space Quantum Key Distribution System in Various Channel Conditions
Authors:	Anna Rózsa Veszely, Ákos Uzonyi and Máté Galambos
Abstract:	We conducted quantum state tomography measurements on a free-space entanglement-based quantum key distribution testbed under various channel conditions. The system’s quantum state was examined in the presence of attenuation, background noise, and atmospheric turbulence, with particular emphasis on the impact of channel conditions during polarization and phase calibration. Under favorable conditions, the reconstructed density matrix exhibited a purity of 0.956 and a fidelity of 0.878 with respect to the target Bell state. Introducing attenuation and background noise reduced the coincidence rate by approximately a factor of four but resulted in only a minor change in state quality, with purity and fidelity remaining at 0.94 and 0.901, respectively. In contrast, turbulence-induced fading caused significant degradation, reducing purity to 0.754 and fidelity to 0.687. When polarization and phase calibration were performed under noisy or turbulent conditions, the reconstructed states showed substantially lower quality even in the absence of additional disturbances, with purities as low as 0.571 and fidelities around 0.68. These results demonstrate that while attenuation and background noise have limited impact on a well-calibrated system, low signal-to-noise ratio and turbulence during calibration introduce persistent errors that significantly reduce the final quantum state fidelity.

Area 2 - Intelligent Computing and Simulation

Full Papers

Paper Nr:	17
Title:	Quantum Vision Transformers for High-Resolution Medical Image Classification under Limited Training Data
Authors:	Dominik Freinberger and Philipp Moser
Abstract:	Vision transformers (ViT) have become a leading paradigm in image classification, but they natively lack strong image-specific inductive biases, require substantial training data, and scale unfavorably with increasing token counts. Quantum machine learning offers an alternative computational framework, and a growing body of work explores transformer-like quantum models, including early adaptations to vision. However, existing quantum vision transformers have largely been studied either at low image resolutions or with strong classical dimensionality reduction, leaving their suitability for high-resolution imaging unclear. In this work, we present a quantum vision transformer model that combines patch tokenization of 224×224 medical images with quantum token mixing via a linear combination of unitaries and quantum singular value transformation. We evaluate the model in classical simulation on three medical image classification datasets under a systematic small-sample protocol. As baselines, we compare against strong classical convolutional and transformer architectures. Across datasets and training-set sizes, the proposed QViT consistently outperforms a vanilla ViT variant and remains competitive with stronger classical baselines that incorporate more suitable image-specific inductive biases. These findings support the feasibility of the proposed QViT for high-resolution, low-data medical image classification and suggest that quantum token mixing may provide a viable alternative to vanilla patch-based transformers in this setting.

Short Papers

Paper Nr:	16
Title:	The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment
Authors:	Dominik Freinberger and Philipp Moser
Abstract:	Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although numerous hybrid architectures have been proposed and demonstrated successfully on benchmark tasks, a significant open question remains regarding the specific contribution of quantum components to the overall performance of these models. In this work, we aim to shed light on the impact of quantum processing within hybrid quantum-classical neural network architectures through a rigorous statistical study under ideal, noise-free simulation. We systematically assess a selection of commonly used HQNN architectures on three binary medical classification tasks covering medical signal data as well as planar and volumetric images. In doing so, we examine the influence attributable to classical and quantum aspects such as encoding schemes, entanglement, and circuit size. Within this experimental setting, we find that in best-case scenarios, hybrid models show performance comparable to their classical counterparts. However, in most cases, performance metrics deteriorate when the investigated QNN components replace matched classical processing layers. Our multi-modal analysis provides empirical insights into the contributions of the tested quantum components and advocates for cautious claims and design choices for hybrid models in near-term applications.

Paper Nr:	43
Title:	Quantum Reservoir Computing for Neuromorphic Data Representation
Authors:	Stevens Johnson, Varun Puram and Johnson Thomas
Abstract:	Neuromorphic data takes the form of sparse, event-driven spike trains in which the precise timing of neural firing events encodes information about the stimulus. This fine-grained temporal structure makes neuromorphic data fundamentally different from the static feature vectors or image frames assumed by most mainstream machine learning pipelines, and it calls for models that can process time-ordered events without discarding timing information. This paper investigates quantum reservoir computing (QRC) for representing and classifying such data. We work with the Spiking Heidelberg Digits (SHD) dataset, a neuromorphic benchmark in which spoken digit recordings are converted into sparse spike trains across 700 cochlear channels using a biologically inspired auditory model. From the full 20-class dataset we extract a binary subset consisting of two spoken digit classes, giving 807 training samples and 211 test samples. Our QRC model uses 8 system qubits coupled to 2 ancilla qubits. The entire circuit was simulated on the Qiskit Aer QASM simulator with 512 shots per sample. We compare QRC against two baselines: a classical Echo State Network (ESN) reservoir with 256 neurons, and a classical Spiking Neural Network (SNN) with 256 Leaky Integrate-and-Fire (LIF) neurons. On the 2-class binary task, QRC achieves 58.77% test accuracy, compared with 90.52% for the classical reservoir and 51.18% for the SNN. Notably, while a 256-neuron classical reservoir and an 8-qubit quantum reservoir both operate in a 256-dimensional state space (2^8 = 256), the quantum reservoir achieves this with a 32× reduction in physical components via amplitude encoding, suggesting that a small fixed quantum circuit can serve as an effective temporal feature extractor for spike-based data compared to classical baselines.

Area 3 - Quantum Information and Computing

Full Papers

Paper Nr:	13
Title:	A Multi-Domain Quantum Low-Code Platform
Authors:	Lavinia Stiliadou, Johanna Barzen, Fabian Bühler and Daniel Georg
Abstract:	Quantum computing promises advantages across diverse application domains, such as finance and chemistry. However, developing quantum applications is currently challenging, as they involve quantum circuits whose behavior is defined by linear algebra and probability theory. Furthermore, programming quantum circuits relies on SDKs that require programming knowledge. Consequently, expertise is concentrated among specialists, and knowledge transfer to other domains is limited, slowing the practical adoption of quantum computing. Low-code platforms attempt to reduce this barrier by providing graphical interfaces for application creation. However, current quantum low-code platforms often lack domain-specific abstractions for defining problem structures and their solutions, limiting their applicability. To address this gap, we introduce domain-specific elements that form a layer on top of low-code elements, encoding goals as well as constraints of concrete application domains. Furthermore, if a domain element can be mapped to multiple low-code elements, we combine them into a single workflow that includes all alternatives, enabling an agent to make a selection at runtime. We implement the approach as a prototype and evaluate it through case studies in finance and logistics. The results demonstrate that domain experts can specify quantum models at a higher level of abstraction, reducing the need for quantum programming knowledge.

Paper Nr:	15
Title:	Sub-Cubic Quantum Gate Synthesis via Stochastic Commutator Decomposition
Authors:	Yevgen Kotukh, Serhii Mokliak, Maksym Korobchynskyi, Valeriy Kozlovskyi, Stanislava Kudrenko and Diana Kozlovska
Abstract:	We present Stochastic Commutator Synthesis (SCS), a hybrid quantum gate compilation framework that integrates Kuperberg’s sub-cubic Solovay-Kitaev exponent c ≈ 1.44042 with the error-tailoring machinery of randomized compilation (RC). Classical Solovay–Kitaev implementations produce word lengths of O((log 1/ε)3.97+δ) and accumulate coherent approximation errors that degrade fault-tolerant threshold estimates. Kuperberg’s 2023/2025 result reduces this to O((log 1/ε)1.44042+δ) via doubly exponential convergence and higher-order commutator decompositions. SCS augments this geometric backbone with a Gibbs-sampled stochastic choice of commutator factors at each recursion level, converting coherent synthesis residuals into incoherent, Pauli-twirl-compatible noise—a property exploited by RC. Combined with RL-guided pre-synthesis (Q-PreSyn), SCS achieves consistent T-count reductions of 10–25% and demonstrates fidelity gains of up to 35% on multi-fold Forrelation circuits on trapped-ion hardware (Sandia QSCOUT). We situate SCS within the complexity-theoretic landscape established by the Raz–Tal oracle separation BQP ⊄ PH, arguing that low-error, noise-robust compilation of Forrelation-type circuits constitutes a practical pathway toward demonstrating this separation on physical hardware.

Paper Nr:	24
Title:	Co-Evolutionary Asymmetry vs. Modular Design: Optimization Trade-Offs in Quantum Denoising Autoencoders
Authors:	Jacob L. Cybulski, Jakub Zwoniarski, Artur Strąg and Sebastian Zając
Abstract:	In Quantum Machine Learning (QML), modularity, symmetry-awareness, and pretraining are standard heuristics used to mitigate barren plateaus in deep variational circuits. While effective for symmetric tasks like noiseless data compression, we demonstrate that these intuitions falter in asymmetric tasks such as chaotic time series denoising. This paper investigates the denoising of chaotic Mackey–Glass sequences across a spectrum of Quantum Autoencoder (QAE) architectures, contrasting the performance of co-evolutionary asymmetric designs (Monolith) against multi-stage modular models with pre-trained, synchronized latent spaces (Mirror, Sidekick, and Stacked). Our experiments characterize the “Curse of Asymmetry,” where extensively pretraining a decoder on clean data carves a rigid latent manifold that prevents the subsequent encoder from generalizing to high-entropy noise. Consequently, the baseline Monolith QAE – optimizing an unconstrained, independent encoder and decoder – significantly outperforms all pretrained modular variants in both denoising capacity and parameter efficiency. Furthermore, our complexity analysis identifies a structural “instability zone” for modular designs, proving that pre-trained models require deeper circuits merely to overcome their initial structural biases. By mapping these failures, this work challenges the presumed safety of modularity and fixed structural priors in quantum time series processing, demonstrating that models leveraging co-evolutionary asymmetry can offer superior performance in this context.

Paper Nr:	36
Title:	Impact of Qubit Rotations on NISQ-Era Circuits
Authors:	Elena Desdentado, Macario Polo, Coral Calero and Maria Teresa Baldassarre
Abstract:	Quantum circuit optimisation is essential in the NISQ era, where limited coherence times and gate errors constrain computational reliability. This paper analyses the impact that systematically reducing qubit rotation gates under different angle thresholds, has on the accuracy of the obtained results. The work done combines theoretical and experimental evaluation. Theoretically, we model cumulative gate fidelity and circuit duration relative to qubit coherence time (T2), demonstrating that rotation pruning substantially decreases circuit depth and execution time, which could mitigate the effects of decoherence. Experimentally, on the IBM Quantum ibm_marrakesh backend, we show that complete rotation removal reduces transpilation time by 77.56% and execution time by 51%, but degrades output accuracy. Partial rotation pruning achieves the best trade-off between computational efficiency and algorithmic precision.

Paper Nr:	38
Title:	Topology-Driven Symbolic Verification of Post-Quantum Migration Paths Using Tamarin Prover
Authors:	Vishnu Ajith, Muhammad Ibrahim and Muhammed Sihan Haroon
Abstract:	The transition from classical public-key cryptography to post-quantum cryptography introduces protocol-level risks that are not fully addressed by configuration review, performance benchmarking, or endpoint reachability testing. Under the current abstraction, deployments may appear operationally correct while still permitting secrecy, authentication, or forward-secrecy violations at the protocol level. This paper presents a topology-driven symbolic verification workflow that translates distributed-system communication graphs into Tamarin models for analysis under the Dolev–Yao adversary model. The workflow derives protocol roles, communication constraints, and migration policies from a graph-based deployment representation, producing .spthy models and associated lemmas for executability, secrecy, authentication, and forward secrecy. A canonical topology representation is used to ensure deterministic model generation from semantically equivalent graph inputs. Experimental evaluation across five scenarios indicates that the framework produces discriminative symbolic outcomes rather than uniform failure reports. A registration-only control scenario verifies all reported lemmas, while the remaining scenarios exhibit two distinct falsification patterns: secrecy and forward-secrecy failures in three scenarios, and authentication failure in one scenario. These results indicate that symbolic verification provides a complementary assurance layer for post-quantum migration analysis and can reveal protocol-level risks that are not observable through operational testing alone.

Short Papers

Paper Nr:	31
Title:	Towards Quantum Software Modelling Abstractions for Smart City Optimisation
Authors:	João Cambaia de Almeida, Fernando Brito e Abreu and João Paulo Fernandes
Abstract:	As urban populations grow, cities face increasing pressure to efficiently manage its resources such as mobility networks, energy grids, and public services. All of which require decisions that are, at their core, combinatorial optimisation problems whose solution spaces grow exponentially with city scale, motivating research into quantum computing as a candidate technology for this problem class. Classical Software Engineering (SE) has established that modelling abstractions deliver measurable gains in quality and development efficiency. We argue that quantum software modelling abstractions may offer similar benefits for both the systematic design of quantum algorithms and the rigorous evaluation of their effectiveness in urban optimisation, a topic that remains understudied in the quantum computing literature. We present a three-phase research programme addressing these gaps, with hybrid quantum-classical architectures as the near-term deployment model.

Paper Nr:	34
Title:	QAOA Architectures for Discrete Portfolio Optimization
Authors:	Adrian-Radu Șofariu, Petru Kallay and Tudor Mihoc
Abstract:	Discrete portfolio optimization with fixed-budget constraints is a natural combinatorial optimization problem for variational quantum algorithms. In this work, we compare three QAOA architectures for fixed-budget portfolio selection: a standard penalty-based formulation with an X mixer, a parity-ring XY mixer, and a full XY mixer. The study also considers maximum absolute scaling of the portfolio data and a practical penalty-coefficient heuristic for the standard formulation. The architectures are evaluated on portfolio instances with n = 4 and n = 8 assets and budget B = n/2, using Qiskit Aer simulations across several circuit depths. In addition, noisy simulations are performed using the ibm_berlin fake backend in order to assess the effect of hardware-aware noise. Performance is measured mainly through the mean approximation ratio, with exact-solution behavior reported separately through the optimal solution hit rate. The results indicate that constraint-preserving XY mixer architectures generally provide better approximation quality than the penalty-based formulation. Overall, the study emphasizes the importance of architecture-aware QAOA design for constrained portfolio optimization.

Paper Nr:	35
Title:	Security Monitoring for Deployed Quantum Key Distribution Devices
Authors:	David Koch, Nikita Knaub, Hedwig Koerfgen, Mario Camilo Pardo and Fabian Farina
Abstract:	Quantum Key Distribution (QKD) offers information- theoretic security at the protocol level, yet deployed QKD appliances remain exposed at the implementation layer. Classical IT compromise, insecure management interfaces, firmware manipulation, and physical tampering operate outside the quantum security model and are not detectable through protocol metrics such as QBER alone. Empirical assessments of commercial systems indicate that these classical attack surfaces constitute a primary operational risk. We present an out-of-band security monitoring architecture for deployed QKD devices. The proposed module augments a QKD appliance with an embedded monitoring unit that fuses device telemetry obtained via management interfaces, host and network security events collected through remote syslog ingestion, and physical and environmental sensor signals. Heterogeneous observables are time-synchronized in a common local data store. In the current prototype, QKD telemetry is analyzed using a one-class autoencoder trained on normal-operation data, while host, network, and sensor events provide temporal context, rule-based failsafes, and operator-facing explanations for alerts. Evaluation on collected datasets (e.g., controlled detector-efficiency manipulation scenarios) demonstrates reliable event-level detection under operational constraints. The system is positioned as an implementation-layer assurance mechanism that complements, but does not replace, protocol-level security guarantees.

Paper Nr:	37
Title:	From Telecom Restoration to Maximum Independent Set: Conflict-Graph Modeling and a Preliminary Pauli-Correlation Approach
Authors:	Ali Abbassi, Yann Dujardin, Eric Gourdin, Philippe Lacomme and Caroline Prodhon
Abstract:	We consider a post-failure telecom restoration problem in which disrupted demands are assigned finite sets of admissible restoration paths. The goal is to select a feasible subset of restoration actions maximizing an additive utility. We formulate this problem as a weighted conflict graph, where vertices represent restoration actions and edges represent pairwise incompatibilities. We give an exactness criterion showing when this construction yields a valid reduction to Maximum Weight Independent Set (MWIS). We then investigate quantum variational methods for the resulting graph-optimization layer. The numerical study first evaluates Pauli Correlation Encoding (PCE) on standalone MIS instances, and then instantiates the telecom pipeline on SNDlib-derived weighted conflict graphs to analyze the induced problem sizes and qubit requirements. The results indicate that PCE can recover competitive feasible independent sets on preliminary MIS benchmarks, while the telecom instances highlight the need for qubit-efficient encodings beyond direct one-qubit-per-action formulations.