Tootfinder

Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
Ratun Rahman, Atit Pokharel, Dinh C. Nguyen
https://arxiv.org/abs/2507.12492

Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data privacy over quantum networks. However, quantum noise remains a significant barrier in QFL, since modern quantum devices experience heterogeneous noise levels due to variances in hardware quality and sensitivity to quantum decoherence, resulting in inadequate training performance. To address this issue, we propose SpoQ…

Resisting Quantum Key Distribution Attacks Using Quantum Machine Learning
Ali Al-kuwari, Noureldin Mohamed, Saif Al-kuwari, Ahmed Farouk, Bikash K. Behera
https://arxiv.org/abs/2509.14282

Resisting Quantum Key Distribution Attacks Using Quantum Machine Learning
The emergence of quantum computing poses significant risks to the security of modern communication networks as it breaks today's public-key cryptographic algorithms. Quantum Key Distribution (QKD) offers a promising solution by harnessing the principles of quantum mechanics to establish secure keys. However, practical QKD implementations remain vulnerable to hardware imperfections and advanced attacks such as Photon Number Splitting and Trojan-Horse attacks. In this work, we investigate the pot…

Quantum router of silicon-vacancy centers via a diamond waveguide
Wen-Jie Zhang, Xi Yan, Jun-Hong An
https://arxiv.org/abs/2509.14793 https://arxiv.org/pdf…

Quantum router of silicon-vacancy centers via a diamond waveguide
As a key component of quantum networks, quantum router distributes quantum information among different quantum nodes. Silicon-vacancy (SiV) center in diamond offers a promising platform for quantum technology due to its strong strain-induced coupling with phonons. However, the development of practical quantum router faces challenges of achieving long-range entanglement and suppressing decoherence. Here, we propose a non-Markovian quantum router based on a diamond waveguide embedded with an arra…

Quantum Utility in Simulating the Real-time Dynamics of the Fermi-Hubbard Model using Superconducting Quantum Computers
Talal Ahmed Chowdhury, Vladimir Korepin, Vincent R. Pascuzzi, Kwangmin Yu
https://arxiv.org/abs/2509.14196

Quantum Utility in Simulating the Real-time Dynamics of the Fermi-Hubbard Model using Superconducting Quantum Computers
The Fermi-Hubbard model is a fundamental model in condensed matter physics that describes strongly correlated electrons. On the other hand, quantum computers are emerging as powerful tools for exploring the complex dynamics of these quantum many-body systems. In this work, we demonstrate the quantum simulation of the one-dimensional Fermi-Hubbard model using IBM's superconducting quantum computers, employing over 100 qubits. We introduce a first-order Trotterization scheme and extend it to an o…

Practical Subarchitectures for Optimal Quantum Layout Synthesis
Kostiantyn V. Milkevych, Jaco van de Pol, Irfansha Shaik
https://arxiv.org/abs/2507.12976 h…

Practical Subarchitectures for Optimal Quantum Layout Synthesis
Quantum Layout Synthesis (QLS) maps a logical quantum circuit to a physical quantum platform. Optimal QLS minimizes circuit size and depth, which is essential to reduce the noise on current quantum platforms. Optimal QLS is an NP-hard problem, so in practice, one maps a quantum circuit to a subset of the complete quantum platform. However, to guarantee optimality, one still has to consider exponentially many subarchitectures. We introduce an effective method to enumerate relevant subarchitect…

Resources for bosonic metrology: quantum-enhanced precision from a superselection rule perspective
Astghik Saharyan, Eloi Descamps, Arne Keller, P\'erola Milman
https://arxiv.org/abs/2507.13245

Resources for bosonic metrology: quantum-enhanced precision from a superselection rule perspective
Quantum optics and atomic systems are prominent platforms for exploiting quantum-enhanced precision in parameter estimation. However, not only are quantum optical and atomic systems often treated separately, but even within quantum optics, identifying optimal probes (quantum states) and evolutions (parameter-dependent dynamics) typically relies on case-by-case analyses. Mode, and sometimes only particle entanglement, can yield quantum enhancement of precision in continuous- and discrete-variabl…

Generalized quantum Chernoff bound
Kun Fang
https://arxiv.org/abs/2508.12889 https://arxiv.org/pdf/2508.12889

Generalized quantum Chernoff bound
We establish a generalized quantum Chernoff bound for the discrimination of multiple sets of quantum states, thereby extending the classical and quantum Chernoff bounds to the general setting of composite and correlated quantum hypotheses. Specifically, we consider the task of distinguishing whether a quantum system is prepared in a state from one of several convex, compact sets of quantum states, each of which may exhibit arbitrary correlations. Assuming their stability under tensor product, w…

High-Accuracy Temporal Prediction via Experimental Quantum Reservoir Computing in Correlated Spins
Yanjun Hou, Juncheng Hua, Ze Wu, Wei Xia, Yuquan Chen, Xiaopeng Li, Zhaokai Li, Xinhua Peng, Jiangfeng Du
https://arxiv.org/abs/2508.12383

High-Accuracy Temporal Prediction via Experimental Quantum Reservoir Computing in Correlated Spins
Physical reservoir computing provides a powerful machine learning paradigm that exploits nonlinear physical dynamics for efficient information processing. By incorporating quantum effects, quantum reservoir computing gains superior potential in machine learning applications, for the quantum dynamics are exponentially costly to simulate classically. Here, we present a novel quantum reservoir computing approach based on correlated quantum spin systems, exploiting natural quantum many-body interac…

Dynamic syndrome decoder in volume-law phases of hybrid quantum circuits
Dawid Paszko, Marcin Szyniszewski, Arijeet Pal
https://arxiv.org/abs/2508.13045 https://

Dynamic syndrome decoder in volume-law phases of hybrid quantum circuits
Phases of matter with volume-law entanglement are frequently observed in quantum circuits and have numerous applications, ranging from deepening our understanding of quantum mechanics to advancements in quantum computing and cryptography. Their capacity to host entangled, complex quantum information is complemented by their ability to efficiently obscure it from quantum measurements through scrambling, reminiscent of quantum error-correction. However, the issue of initial-state decodability has…

Scaling Hybrid Quantum-HPC Applications with the Quantum Framework
Srikar Chundury, Amir Shehata, Seongmin Kim, Muralikrishnan Gopalakrishnan Meena, Chao Lu, Kalyana Gottiparthi, Eduardo Antonio Coello Perez, Frank Mueller, In-Saeng Suh
https://arxiv.org/abs/2509.14470

Scaling Hybrid Quantum-HPC Applications with the Quantum Framework
Hybrid quantum-high performance computing (Q-HPC) workflows are emerging as a key strategy for running quantum applications at scale in current noisy intermediate-scale quantum (NISQ) devices. These workflows must operate seamlessly across diverse simulators and hardware backends since no single simulator offers the best performance for every circuit type. Simulation efficiency depends strongly on circuit structure, entanglement, and depth, making a flexible and backend-agnostic execution model…