Papers
Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision
Mohamed Afane, Quanjiang Long, Haoting Shen +4 more·Jan 26, 2026
Current quantum neural networks suffer from extreme sensitivity to both adversarial perturbations and hardware noise, creating a significant barrier to real-world deployment. Existing robustness techniques typically sacrifice clean accuracy or requir...
Quantum Machine Learning Using Quantum Illumination With Quantum Enhanced Interference
Pallab Biswas, Tamal Maity·Jan 25, 2026
Quantum Machine Learning(QML) is developed by combining quantum mechanics principles with classical machine learning techniques in a hybrid framework that can give faster, exponential, more efficient power of quantum computing with the data driven in...
Efficient quantum machine learning with inverse-probability algebraic corrections
Jaemin Seo·Jan 23, 2026
Quantum neural networks (QNNs) provide expressive probabilistic models by leveraging quantum superposition and entanglement, yet their practical training remains challenging due to highly oscillatory loss landscapes and noise inherent to near-term qu...
Calibration-Conditioned FiLM Decoders for Low-Latency Decoding of Quantum Error Correction Evaluated on IBM Repetition-Code Experiments
Samuel Stein, Shuwen Kan, Chenxu Liu +6 more·Jan 22, 2026
Real-time decoding of quantum error correction (QEC) is essential for enabling fault-tolerant quantum computation. A practical decoder must operate with high accuracy at low latency, while remaining robust to spatial and temporal variations in hardwa...
Explaining the advantage of quantum-enhanced physics-informed neural networks
Nils Klement, Veronika Eyring, Mierk Schwabe·Jan 21, 2026
Partial differential equations (PDEs) form the backbone of simulations of many natural phenomena, for example in climate modeling, material science, and even financial markets. The application of physics-informed neural networks to accelerate the sol...
Deep Learning Approaches to Quantum Error Mitigation
Leonardo Placidi, Ifan Williams, Enrico Rinaldi +4 more·Jan 20, 2026
We present a systematic investigation of deep learning methods applied to quantum error mitigation of noisy output probability distributions from measured quantum circuits. We compare different architectures, from fully connected neural networks to t...
Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
Vincent Gurgul, Ying Chen, Stefan Lessmann·Jan 20, 2026
This paper presents a Quantum Reinforcement Learning (QRL) solution to the dynamic portfolio optimization problem based on Variational Quantum Circuits. The implemented QRL approaches are quantum analogues of the classical neural-network-based Deep D...
Quantum Qualifiers for Neural Network Model Selection in Hadronic Physics
Brandon B. Le, D. Keller·Jan 19, 2026
As quantum machine-learning architectures mature, a central challenge is no longer their construction, but identifying the regimes in which they offer practical advantages over classical approaches. In this work, we introduce a framework for addressi...
Rethinking Quantum Noise in Quantum Machine Learning: When Noise Improves Learning
Linghua Zhu, Yulong Dong, Ziyu Zhang +1 more·Jan 19, 2026
Quantum noise is conventionally viewed as a fundamental obstacle in near-term quantum computing, motivating extensive error correction and mitigation strategies. We present numerical evidence that challenges this consensus. Through experiments on qua...
Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning
Fan Fan, Yilei Shi, Mihai Datcu +5 more·Jan 19, 2026
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the dev...
Equation-Free Discovery of Open Quantum Systems via Paraconsistent Neural Networks
Aleyna Ceyran, Jair Minoro Abe·Jan 19, 2026
Modeling the dynamics of open quantum systems on noisy intermediate-scale quantum (NISQ) devices constitutes a major challenge, as high noise levels and environmental degradations lead to the decay of pure quantum states (decoherence) and energy loss...
Interpolation of unitaries with time-dependent Hamiltonians via Deep Learning
Antonio Guerra, Daniel Uzcategui-Contreras, Aldo Delgado +1 more·Jan 18, 2026
Quantum systems governed by time-dependent Hamiltonians pose significant challenges for the accurate computation of unitary time-evolution operators, which are essential for predicting quantum state dynamics. In this work, we introduce a physics-info...
Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization
Qingyu Meng, Yangshuai Wang·Jan 17, 2026
Quantum neural networks (QNNs) have attracted growing interest for scientific machine learning, yet in regression settings they often suffer from limited trainability under noisy gradients and ill-conditioned optimization. We propose a hybrid quantum...
Indoor Occupancy Classification using a Compact Hybrid Quantum-Classical Model Enabled by a Physics-Informed Radar Digital Twin
Sebastian Ratto, Ahmed N. Sayed, Neda Rojhani +6 more·Jan 17, 2026
Indoor occupancy classification enables privacy-preserving monitoring in settings such as remote elder care, where presence information helps triage alarms without cameras or wearables. Radar suits this role by sensing motion through occlusions and i...
Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs
Alberto Coppi, Ema Puljak, Lorenzo Borella +6 more·Jan 15, 2026
We present a systematic study of Tensor Network (TN) models $\unicode{x2013}$ Matrix Product States (MPS) and Tree Tensor Networks (TTN) $\unicode{x2013}$ for real-time jet tagging in high-energy physics, with a focus on low-latency deployment on Fie...
Searching for Quantum Effects in the Brain: A Bell-Type Test for Nonclassical Latent Representations in Autoencoders
I. K. Kominis, C. Xie, S. Li +2 more·Jan 15, 2026
Whether neural information processing is entirely classical or involves quantum-mechanical elements remains an open question. Here we propose a model-agnostic, information-theoretic test of nonclassicality that bypasses microscopic assumptions and in...
Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction
Kai Zhang, Zhengzhong Yi, Shaojun Guo +13 more·Jan 14, 2026
Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation (FTQC). Neural network decoders like AlphaQubit have demonstrated potential, achieving higher accuracy than traditional human-designed decoding algorithms....
Quantum State Discrimination Enhanced by FPGA-Based AI Engine Technology
Anastasiia Butko, Artem Marisov, David I. Santiago +1 more·Jan 13, 2026
Identifying the state of a quantum bit (qubit), known as quantum state discrimination, is a crucial operation in quantum computing. However, it has been the most error-prone and time-consuming operation on superconducting quantum processors. Due to s...
On measurement-dependent variance in quantum neural networks
Andrey Kardashin, Konstantin Antipin·Jan 12, 2026
Variational quantum circuits have become a widely used tool for performing quantum machine learning (QML) tasks on labeled quantum states. In some specific tasks or for specific variational ansätze, one may perform measurements on a restricted part o...
Measurement-based acceleration of optical computations
I. V. Vovchenko, A. A. Zyablovsky, A. A. Pukhov +1 more·Jan 12, 2026
Analog coprocessors are intensively developing nowadays with the aim to optimize energy computations of neural networks. In this work we focus on the possibility of using detection of collective oscillations in optical systems for computational purpo...