quant-ph · 2026-07-14 · No. 53

Quantum Physics, 2026-07-14.

5 new papers in quant-ph. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

5 entries
  1. 01

    Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks

    Junrui Zhang, Zemin Chen, Lusi Li, Mohammad Ghasemigol, Daniel Takabi, Rui Ning

    quant-ph · cs.LG

    Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studies have shown that QNNs are vulnerable to backdoor attacks, yet existing quantum backdoors mostly rely on a fixed trigger shared by all poisoned inputs. This fixed-trigger design is a major weakness because many defenses detect or weaken the repeated patterns such...

    arxiv.org/abs/2607.11843 · PDF

  2. 02

    $\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning

    Mariano Caruso, Daniel Ruiz, Alejandro Giraldo, Guido Bellomo

    quant-ph · cs.LG

    Quantitative Structure-Activity Relationship ($\mathtt{QSAR}$) modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predicting compound toxicity, bioavailability, and therapeutic potential. However, classical methods often struggle to effectively map the highly complex, non-linear, and high-dimensional interactions inherent in molecular data, leading to reduced predictive accuracy and...

    arxiv.org/abs/2607.11701 · PDF

  3. 03

    Fixed-Protocol Amortized MPS Tomography with Conformalized Predictive Uncertainty

    Jian Xu, Delu Zeng, John Paisley, Qibin Zhao

    quant-ph · cs.LG

    Quantum state tomography is sample-starved, and the states one prepares live on a narrow, learnable manifold. A $k{=}0$ prior-only control shows that on concentrated families a prior estimate is already near-optimal, so ``high fidelity at few measurements'' can be family memorization rather than tomography; genuine measurement-efficiency needs a model that conditions on the measurements and demonstrably uses them. On a shared...

    arxiv.org/abs/2607.11273 · PDF

  4. 04

    When cheap gradients fail: the measurement cost of attacking quantum classifiers

    Bacui Li, Chandra Thapa, Tansu Alpcan, Udaya Parampalli

    quant-ph · cs.CR · cs.LG

    Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers. We show that finite quantum measurement statistics (shot noise) act as a built-in defense against gradient-based test-time attacks whose cost scales unfavorably for the attacker. Because every gradient component must be inferred from repeated circuit executions under any unbiased gradient-estimation rule, white-box extraction consumes a...

    arxiv.org/abs/2607.11095 · PDF

  5. 05

    Overcoming Fourier Locking in Quantum Data Re-uploading Classifiers via Spectral Homotopy

    Spencer Topel

    quant-ph · cs.LG

    Data re-uploading parameterized quantum circuits (DRU-PQCs) are universal function approximators, yet their expressivity produces oscillatory, non-convex loss landscapes that resist gradient-based optimization. We show that the primary optimization bottleneck in DRU-PQCs is not insufficient capacity but a structural failure mode we term Fourier locking (FL): because encoding weights and entangling layers are nonlinearly coupled, random...

    arxiv.org/abs/2607.11013 · PDF

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