arXiv Machine Learning

Quantum-Logic Tsetlin Machines: Interpretable Quantum Machine Learning with Commuting Projector Clauses

The paper introduces the Quantum-Logic Tsetlin Machine (QL‑TM), a variant of Tsetlin Machines that replaces Boolean literals with quantum propositions represented by projectors while keeping the classical include/exclude automata. Clauses are limited to commuting measurement contexts and are activated through the Born probability of their joint projector, allowing an exact reduction to ordinary Boolean TM clauses in diagonal computational‑basis contexts. Experiments on Bell states, phase‑flip syndromes, stabilizer tasks, and noise‑robust settings demonstrate that correct non‑diagonal contexts recover physically meaningful clauses, whereas diagonal or wrong contexts lose phase or syndrome information, and the context‑budget results align with the predicted separability ladder.

arXiv Machine Learning

Sep 18

QEncodeBench: Can Large Language Models Encode Classical Problems into Verified Quantum Oracles?

QEncodeBench evaluates whether large language models can translate classical constraint problems into verified quantum phase oracles. The benchmark measures the correctness of generated circuits using an adversarial self‑validated verifier that checks full solution‑set equivalence while enforcing resource limits. Results show that models lacking a reasoning mode perform poorly, whereas enabling native reasoning improves accuracy tenfold; semantic errors dominate, and neuro‑symbolic pipelines close most gaps by delegating critical composition to deterministic procedures.

By Xujun Che, Hanhan Wu, Yuchen Yuan, Chenyang Yu

llms agents

arXiv AI

Jun 15

Aligning Quantum Operators with Large Language Models

arXiv:2606. 13811v1 Announce Type: cross Abstract: Can Large Language Models (LLMs) understand and reason about quantum operators?

By Rogerio Feris, Yunchao Liu, Pengyuan Li, Hang Hua, David Kremer

llms diffusion benchmarks

arXiv AI

Jul 13

Quantum Logic as the Logic of Contexts

arXiv:2607. 09032v1 Announce Type: cross Abstract: Quantum logic is usually presented as a non-classical departure from ordinary reasoning forced on us by quantum mechanics, with classical logic kept as the secure starting point.

By Haruki Emori, Atsushi Iriki, Andrei Khrennikov, Kazunori Kondo

Hugging Face Trending Papers

Jul 13

Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks

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.

fine-tuning

arXiv Machine Learning

Jul 20

Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling

arXiv:2607. 15313v1 Announce Type: new Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities.

By Junhao Song, Yu Zhou, William Knottenbelt, Yudong Cao

agents

arXiv Machine Learning

Sep 2

Towards unsupervised representation learning for quantum data: quantum models with inference and generation

arXiv:2609.00372v1 Announce Type: cross Abstract: With quantum sensors, simulators and networks emerging, a future of quantum technology may produce quantum states as data---that is, coherently rathe...

By Robin Lorenz, Eric Brunner, Marcello Benedetti

arXiv AI

Sep 15

Proving olympiad geometry theorems on a superconducting quantum processor

arXiv:2609.14533v1 Announce Type: cross Abstract: Automated theorem proving seeks to use computational systems to prove or disprove mathematical and logical statements [1, 2]. It underpins a wide ran...

By Ning Wang, Zheng-Zhi Sun, Zhengyi Cui, Yiren Zou, Aosai Zhang, Fanhao Shen, Jiarun Zhong, Zehang Bao, Zitian Zhu, Han Wang, Jia-Nan Yang, Jiayuan Shen, Gongyu Liu, Yanzhe Wang, Yihang Han, Yiyang He, Jiahua Huang, Sailang Zhou, Xinrong Zhang, Yaozu Wu, Zixuan Song, Jinfeng Deng, Hang Dong, Qi Ye, Weikang Li, Si Jiang, Yixuan Ma, Shuangyue Geng, Zhide Lu, Chao Song, Hekang Li, Pengfei Zhang, Qiujiang Guo, H. Wang, Dong-Ling Deng

arXiv Machine Learning

Jun 9

Quantum feature-map learning with reduced resource overhead

arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.

By Jonas J\"ager, Philipp Els\"asser, Elham Torabian

efficiency benchmarks

arXiv AI

Jun 9

Repair Before Veto, When Repair Is Hidden: Quantum-Accessible Features for Repair-Augmented Constraint Learning

arXiv:2606. 08020v1 Announce Type: cross Abstract: Hard-constraint decision systems usually veto infeasible candidates.

By Yifan Wang

agents

arXiv Machine Learning

Jul 27

Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support

arXiv:2607. 22516v1 Announce Type: cross Abstract: A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data.

By Peiyong Wang, Udaya Parampalli, Casey R. Myers

benchmarks safety

arXiv Machine Learning

Jul 14

Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks

arXiv:2607. 11843v1 Announce Type: cross Abstract: Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood.

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

fine-tuning

arXiv Machine Learning

Jul 23

Machine-learned syndrome post-selection for reliable quantum error correction

arXiv:2607. 19563v1 Announce Type: cross Abstract: Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for that require costly decoder-level information.

By Tobias Haug, Askery Canabarro, Leandro Aolita

efficiency

Related stories