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quant-ph2026

Formal Verification of Quantum Ancilla Safety

Jiqi Li, Jingyi Mei, Wang Fang +1

Ensuring ancilla safety is a critical correctness requirement for quantum compilation, since ancilla qubits are routinely introduced to implement complex operations with fewer gate…

quant-ph2026

Quantum states supported by matroids

Xiaowei Huang, Fei Shi, Lijun Zhang +1

In this work, we establish a structural correspondence between quantum states and matroid theory. This connection demonstrates that key properties of quantum states, including enta…

quant-ph2026

Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool

Ji Guan, Mingsheng Ying

As with classical neural networks, quantum machine learning (QML) models are vulnerable to small input perturbations that can significantly alter output predictions. Certifying the…

quant-ph2026

QSeqSim: A Symbolic Simulator for Qiskit While Loops Using Sequential Quantum Circuits

Zihao Li, Ji Guan, Mingsheng Ying

We present a tool QSeqSim, a Qiskit-integrated symbolic backend that fills the current gap of having no Qiskit-native support for simulating while-loop quantum programs and their i…

quant-ph2026

Quantum Multi-Level Estimation of Functionals of Discrete Distributions

Kean Chen, Minbo Gao, Tongyang Li +2

We propose a quantum multi-level estimation framework for a functional of a discrete distribution . We partition the values into logarith…

quant-ph2026

Experimental robustness benchmarking of quantum neural networks on a superconducting quantum processor

Hai-Feng Zhang, Zhao-Yun Chen, Peng Wang +17

Quantum machine learning (QML) models, like their classical counterparts, are vulnerable to adversarial attacks, hindering their secure deployment. Here, we report the first system…