Papers
Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computation
Andi Gu, J. Pablo Bonilla Ataides, Mikhail D. Lukin +1 more·Apr 9, 2026
Quantum error correction (QEC) is essential for scalable quantum computing. However, it requires classical decoders that are fast and accurate enough to keep pace with quantum hardware. While quantum low-density parity-check codes have recently emerg...
Hardware-Aware Quantum Support Vector Machines
Adil Mubashir Chaudhry, Ali Raza Haider, Hanzla Khan +1 more·Apr 9, 2026
Deploying quantum machine learning algorithms on near-term quantum hardware requires circuits that respect device-specific gate sets, connectivity constraints, and noise characteristics. We present a hardware-aware Neural Architecture Search (NAS) ap...
A Thermodynamic SU(1,1) Witness Framework for Double-Quantum NMR Signals in Neural Tissue
Christian Kerskens·Apr 8, 2026
Entanglement criteria based on variances or Fisher information are well developed for compact collective spin algebras, but their extension to non-compact dynamical sectors is less straightforward. In particular, double-quantum (DQ) observables assoc...
LaScA: Language-Conditioned Scalable Modelling of Affective Dynamics
Kosmas Pinitas, Ilias Maglogiannis·Apr 8, 2026
Predicting affect in unconstrained environments remains a fundamental challenge in human-centered AI. While deep neural embeddings dominate contemporary approaches, they often lack interpretability and limit expert-driven refinement. We propose a nov...
QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks
Kooshan Maleki, Alberto Marchisio, Muhammad Shafique·Apr 8, 2026
Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expressivity, trainability, and resource use, while limited qubits often necessitate circuit cutting. Exi...
XR-CareerAssist: An Immersive Platform for Personalised Career Guidance Leveraging Extended Reality and Multimodal AI
N. D. Tantaroudas, A. J. McCracken, I. Karachalios +2 more·Apr 8, 2026
Conventional career guidance platforms rely on static, text-driven interfaces that struggle to engage users or deliver personalised, evidence-based insights. Although Computer-Assisted Career Guidance Systems have evolved since the 1960s, they remain...
A hardware efficient quantum residual neural network without post-selection
Amena Khatun, Akib Karim, Muhammad Usman·Apr 8, 2026
We propose a hardware efficient quantum residual neural network which implements residual connections through a deterministic linear combination of identity and variational unitaries, enabling fully differentiable training. In contrast to the previou...
Soft-Quantum Algorithms
Basil Kyriacou, Mo Kordzanganeh, Maniraman Periyasamy +1 more·Apr 7, 2026
Quantum operations on pure states can be fully represented by unitary matrices. Variational quantum circuits, also known as quantum neural networks, embed data and trainable parameters into gate-based operations and optimize the parameters via gradie...
Shot-Based Quantum Encoding: A Data-Loading Paradigm for Quantum Neural Networks
Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy +1 more·Apr 7, 2026
Efficient data loading remains a bottleneck for near-term quantum machine-learning. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-space capacity or require circuit depths that exceed the coherence bud...
Pixel-Translation-Equivariant Quantum Convolutional Neural Networks via Fourier Multiplexers
Dmitry Chirkov, Igor Lobanov·Apr 7, 2026
Convolutional neural networks owe much of their success to hard-coding translation equivariance. Quantum convolutional neural networks (QCNNs) have been proposed as near-term quantum analogues, but the relevant notion of translation depends on the da...
Scaling Laws for Hybrid Quantum Neural Networks: Depth, Width, and Quantum-Centric Diagnostics
Danil Vyskubov, Kirill Vyskubov, Nouhaila Innan +1 more·Apr 7, 2026
Hybrid quantum neural networks are increasingly explored for classification, yet it remains unclear how their performance and quantum behavior scale with circuit depth and qubit count. We present a controlled scaling study of hybrid quantum-classical...
Quantum Machine Learning for particle scattering entanglement classification
Hala Elhag, Yahui Chai·Apr 7, 2026
Entanglement is a key quantity for characterizing quantum correlations in particle scattering processes, but its direct evaluation is computationally demanding on quantum hardware. In this work, we investigate whether fermion density profiles, which ...
Loss-aware state space geometry for quantum variational algorithms
Ankit Gill, Kunal Pal·Apr 7, 2026
The natural gradient descent optimisation technique is an efficient optimising protocol for broad classes of classical and quantum systems that takes the underlying geometry of the parameter manifold into account by means of using either the Fisher i...
Real-time Dynamics in 3D for up to 1000 Qubits with Neural Quantum States: Quenches and the Quantum Kibble--Zurek Mechanism
Vighnesh Dattatraya Naik, Zheng-Hang Sun, Markus Heyl·Apr 6, 2026
Exponential complexity of many-body wave functions limits accurate numerical simulations of real-time dynamics, especially beyond 1D, where rapid entanglement growth poses severe challenges. Neural Quantum States (NQS) have emerged as a powerful appr...
Hybrid Fourier Neural Operator for Surrogate Modeling of Laser Processing with a Quantum-Circuit Mixer
Mateusz Papierz, Asel Sagingalieva, Alix Benoit +3 more·Apr 6, 2026
Data-driven surrogates can replace expensive multiphysics solvers for parametric PDEs, yet building compact, accurate neural operators for three-dimensional problems remains challenging: in Fourier Neural Operators, dense mode-wise spectral channel m...
Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
Poornima Kumaresan, Shwetha Singaravelu, Lakshmi Rajendran +1 more·Apr 6, 2026
Quantum machine learning (QML) stands at the intersection of quantum computing and artificial intelligence, offering the potential to solve problems that remain intractable for classical methods. However, the current landscape of QML software framewo...
The physical basis of information flow in neural matter: a thermocoherent perspective on cognitive dynamics
Onur Pusuluk·Apr 5, 2026
Information flow is central to contemporary accounts of cognition, yet its physical basis in living neural matter remains poorly specified. Here, we develop a multiscale resource-theoretical framework motivated by the \textit{thermocoherent effect}, ...
Post-Selection-Free Decoding of Measurement-Induced Area-Law Phases via Neural Networks
Hui Yu, Jiangping Hu, Shi-Xin Zhang·Apr 4, 2026
Monitored quantum circuits host a rich variety of exotic non-equilibrium phases. Among the most representative examples are measurement-induced phase transitions between distinct area-law entangled states. However, because these transitions are chara...
Learning high-dimensional quantum entanglement through physics-guided neural networks
Yang Xu, Hao Zhang, Wenwen Zhang +5 more·Apr 3, 2026
High-gain spontaneous parametric down-conversion (SPDC) produces bright squeezed vacuum with rich high-dimensional entanglement, but its output is inherently multimodal and non-perturbative, making the full modal characterization a major computationa...
Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks
Letao Wang, Abdel Lisser, Sreejith Sreekumar +1 more·Apr 3, 2026
Partial differential equations (PDEs) play a crucial role in financial mathematics, particularly in portfolio optimization, and solving them using classical numerical or neural network methods has always posed significant challenges. Here, we investi...