Papers
Decoder Performance in Hybrid CV-Discrete Surface-Code Threshold Estimation Using LiDMaS+
Dennis Delali Kwesi Wayo, Chinonso Onah, Leonardo Goliatt +1 more·Mar 6, 2026
Threshold estimation is central to fault-tolerant quantum computing, but the reported threshold depends not only on the code and noise model, but also on the decoder used to interpret syndrome data. We study this dependence for surface-code threshold...
The toric code under antiferromagnetic isotropic Heisenberg interactions
Won Jang, Robert Peters, Thore Posske·Mar 5, 2026
We investigate the impact of an isotropic antiferromagnetic Heisenberg perturbation on the toric code, focusing on the resulting quantum phase transition and the nature of the phase that emerges beyond topological order. Using neural-network quantum ...
Scalable Digital Compute-in-Memory Ising Machines for Robustness Verification of Binary Neural Networks
Madhav Vadlamani, Rahul Singh, Yuyao Kong +2 more·Mar 5, 2026
Verification of binary neural network (BNN) robustness is NP-hard, as it can be formulated as a combinatorial search for an adversarial perturbation that induces misclassification. Exact verification methods therefore scale poorly with problem dimens...
Machine Learning the Strong Disorder Renormalization Group Method for Disordered Quantum Spin Chains
A. Ustyuzhanin, J. Vahedi, S. Kettemann·Mar 5, 2026
We train machine learning algorithms to infer the entanglement structure of disordered long-range interacting quantum spin chains by learning from the strong disorder renormalisation group (SDRG) method. The system consists of $S=1/2$-quantum spins c...
Programmable superconducting neuron with intrinsic in-memory computation and dual-timescale plasticity for ultra-efficient neuromorphic computing
Muen Wang, Shucheng Yang, Yuxiang Lin +8 more·Mar 5, 2026
The escalating energy demands of artificial intelligence pose a critical challenge to conventional computing. Leveraging the efficiency of event-driven, in-memory neuromorphic architectures into the superconducting circuits with ultra-high speed and ...
Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators
Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil +1 more·Mar 4, 2026
Software-hardware co-design is essential for optimizing in-memory computing (IMC) hardware accelerators for neural networks. However, most existing optimization frameworks target a single workload, leading to highly specialized hardware designs that ...
From Reachability to Learnability: Geometric Design Principles for Quantum Neural Networks
Vishal S. Ngairangbam, Michael Spannowsky·Mar 3, 2026
Classical deep networks are effective because depth enables adaptive geometric deformation of data representations. In quantum neural networks (QNNs), however, depth or state reachability alone does not guarantee this feature-learning capability. We ...
QAOA-Predictor: Forecasting Success Probabilities and Minimal Depths for Efficient Fixed-Parameter Optimization
Rodrigo Coelho, Georg Kruse, Jeanette Miriam Lorenz·Mar 3, 2026
Quantum Computing promises to solve complex combinatorial optimization problems more efficiently than classical methods, with the Quantum Approximate Optimization Algorithm (QAOA) being a leading candidate. Recent fixed-parameter variations of QAOA e...
Layer-wise QUBO-Based Training of CNN Classifiers for Quantum Annealing
Mostafa Atallah, Rebekah Herrman·Mar 3, 2026
Variational quantum circuits for image classification suffer from barren plateaus, while quantum kernel methods scale quadratically with dataset size. We propose an iterative framework based on Quadratic Unconstrained Binary Optimization (QUBO) for t...
Learning Hamiltonians for solid-state quantum simulators
Jarosław Pawłowski, Mateusz Krawczyk·Mar 3, 2026
We introduce a generalizable framework for learning to identify effective Hamiltonians directly from experimental data in solid-state quantum systems. Our approach is based on a physics-informed neural network architecture that embeds physical constr...
Identification of quantum generative circuits with parallel quantum neural network
Zheping Wu, Xiaopeng Huang, Hengyue Jia +2 more·Mar 3, 2026
The rapid emergence of quantum technology has raised new challenges in distinguishing various quantum circuits of similar functions. In this work, we propose parallel quantum embedding neural network (ParaQuanNet) for the efficient identification of ...
Neural quantum support vector data description for one-class classification
Changjae Im, Hyeondo Oh, Daniel K. Park·Mar 3, 2026
One-class classification (OCC) is a fundamental problem in machine learning with numerous applications, such as anomaly detection and quality control. With the increasing complexity and dimensionality of modern datasets, there is a growing demand for...
Quantum-Inspired Fine-Tuning for Few-Shot AIGC Detection via Phase-Structured Reparameterization
Kaiyang Xing, Han Fang, Zhaoyun Chen +4 more·Mar 2, 2026
Recent studies show that quantum neural networks (QNNs) generalize well in few-shot regimes. To extend this advantage to large-scale tasks, we propose Q-LoRA, a quantum-enhanced fine-tuning scheme that integrates lightweight QNNs into the low-rank ad...
Closing the Loop: Resource-aware Hybrid NAS Guided by Analytical and Hardware-Calibrated Quantum Cost Modeling
Muhammad Kashif, Alberto Marchisio, Muhammad Shafique·Feb 28, 2026
Hybrid quantum-classical neural networks (HQNNs) integrate quantum circuits with classical layers, each operating under fundamentally different computational paradigms, which makes hardware resource estimation challenging. The training of quantum cir...
Exact and Asymptotically Complete Robust Verifications of Neural Networks via Quantum Optimization
Wenxin Li, Wenchao Liu, Chuan Wang +4 more·Feb 28, 2026
Deep neural networks (DNNs) enable high performance across domains but remain vulnerable to adversarial perturbations, limiting their use in safety-critical settings. Here, we introduce two quantum-optimization-based models for robust verification th...
Comparing Classical and Quantum Variational Classifiers on the XOR Problem
Miras Seilkhan, Adilbek Taizhanov·Feb 27, 2026
Quantum machine learning applies principles such as superposition and entanglement to data processing and optimization. Variational quantum models operate on qubits in high-dimensional Hilbert spaces and provide an alternative approach to model expre...
Learning spectral density functions in open quantum systems
Felipe Peleteiro, João Victor Shiguetsugo Kawanami Lima, Pedro Marcelo Prado +2 more·Feb 27, 2026
Spectral density functions quantify how environmental modes couple to quantum systems and govern their open dynamics. Inferring such frequency-dependent functions from time-domain measurements is an ill-conditioned inverse problem. Here, we use exact...
On Emergences of Non-Classical Statistical Characteristics in Classical Neural Networks
Hanyu Zhao, Yang Wu, Yuexian Hou·Feb 27, 2026
Inspired by measurement incompatibility and Bell-family inequalities in quantum mechanics, we propose the Non-Classical Network (NCnet), a simple classical neural architecture that stably exhibits non-classical statistical behaviors under typical and...
Quantum Deep Learning: A Comprehensive Review
Yanjun Ji, Zhao-Yun Chen, Marco Roth +10 more·Feb 26, 2026
Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalization, and scalability, can be enhanced based on specific resource constra...
Noise-adaptive hybrid quantum convolutional neural networks based on depth-stratified feature extraction
Taehyun Kim, Israel F. Araujo, Daniel K. Park·Feb 25, 2026
Hierarchical quantum classifiers, such as quantum convolutional neural networks (QCNNs), represent recent progress toward designing effective and feasible architectures for quantum classification. However, their performance on near-term quantum hardw...