The paper investigates how a learner’s need to keep acquired knowledge usable creates a trade‑off between storage and access costs, identifying precise endpoints that dictate different physical data organizations. For exact concept‑learning tasks, the authors derive a storage‑read curve within a constant factor, showing that one‑read compound queries require answer‑specific cells and incur exponentially costly updates, whereas one‑bit, order‑independent updates necessitate fact‑specific cells. The study further establishes robust bounds on expected distance from a coordinate encoding under uniform content distribution, limits the information inaccessible to a one‑read consumer, and explores how preservation constraints and distributed encodings affect update strategies, ultimately explaining how distinctions, uses, and update constraints jointly determine resource costs and organization.
By Yiling Wu (BridgeM, Inc.)
The paper presents a reinforcement‑learning approach to schedule link‑level entanglement in quantum networks, using a Markov Decision Process and double deep Q‑networks with message‑passing neural networks. The resulting policies achieve 100% success rates even when the link activation probability is reduced by up to 71% compared to baseline heuristics, and maintain at least 80% success when task placements are hardware‑restricted. The authors also develop metrics to interpret the learned policy and employ a large language model to generate a heuristic that matches the DQN performance, suggesting a scalable method for extracting interpretable strategies in large quantum networks.
By Leon Rode, Sumeet Khatri, Supartha Podder
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
By Min Chen, Yu Gan, Xin Jin, Yuqing Li, Junqi Wang, Zeguan Wu, Yunfei Wang, Bingzhi Zhang, Priyam Srivastava, Tianlong Chen, Ankit Kulshrestha, Yuan Liu, Juan Jos\'e Mendoza-Arenas, Kaushik P. Seshadreesan, Sarvagya Upadhyay, Xueyue Zhang, Quntao Zhuang, Junyu Liu
arXiv:2308. 11290v2 Announce Type: replace-cross Abstract: Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality.
By Yuxuan Du, Yibo Yang, Tongliang Liu, Zhouchen Lin, Bernard Ghanem, Dacheng Tao
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
By Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy, Alexey Melnikov
The review examines how machine learning can adapt quantum combinatorial optimization workflows by learning decisions such as formulation, penalties, shot budgets, and backend selection. It categorizes the literature into three paradigms—AI for quantum optimization, quantum for AI-driven optimization, and AI‑quantum co‑optimization—and analyzes 119 papers, finding stronger evidence for AI assisting quantum methods than for quantum enhancing learned optimizers. The authors highlight limited experimental controls, introduce an M0‑M5 evidence hierarchy, and argue that scaling is a systems problem requiring careful allocation of classical and quantum resources.
By Hoong Chuin LAU
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
Amplitude embedding lets a variational quantum model read many classical features with few qubits, but the model output is then only a quadratic form in the normalized input. Data re-uploading, which...
arXiv:2608. 05240v1 Announce Type: cross Abstract: One-bit post-training quantization represents each weight using only its sign, requiring all deployment contexts to share the same binary weight matrix even when their activation statistics favor different sign patterns.
By Yuma Ichikawa, Moeto Mishima
The paper identifies a representational limitation of amplitude encoding in quantum neural networks: normalization makes the encoding invariant to nonzero real rescaling of inputs, which restricts the hypothesis class regardless of the circuit architecture. The authors derive a condition for deterministic preprocessing that removes this ambiguity and propose a fixed nonlinear feature map that distinguishes all real inputs while keeping logarithmic qubit scaling. Experiments on three classification benchmarks and a synthetic dataset show that this new encoding improves accuracy by 21–44 % over both conventional amplitude and angle encodings and a classical neural network.
By Vincent Gurgul, David Prinz
arXiv:2607. 01336v1 Announce Type: cross Abstract: Neural Quantum States (NQS) are a remarkably expressive class of variational ans\"atze for quantum many-body wavefunctions, yet little is understood about their internal mechanisms: trained on variational objectives alone, how do NQS accurately capture physical observables that they have never been explicitly optimized for?
By Zihao Qi, Christopher Earls
arXiv:2609.25044v1 Announce Type: cross
Abstract: This paper presents a quantum semantic communication (QSemCom) framework combining quantum machine learning (QML) and semantic communication (SemCom)...
By Melek Krichen, Nikhitha Nunavath, Riccardo Bassoli, Soumaya Cherkaoui