The paper presents a tutorial on enhancing quantum amplitude embedding through data re-uploading, a technique that repeatedly encodes input data between trainable blocks to increase model expressiveness. It demonstrates that each additional upload raises the reachable polynomial order by two and provides simulation results showing significant improvements in test coefficient of determination for higher-order targets. The study offers practical guidance on selecting the number of uploads based on the polynomial order required for a given task.
By Hyojun Ahn, Joongheon Kim
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
arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.
By Chi-Sheng Chen, En-Jui Kuo
arXiv:2608. 14691v1 Announce Type: new Abstract: Sequence models are conventionally distinguished by their backbone, the mechanism that routes information across positions, such as attention or recurrence.
By Ahmed Nebli, Hadi Saadatdoorabi, Christopher Keibel, Kevin Yam
arXiv:2602. 06699v2 Announce Type: replace-cross Abstract: We propose a variational quantum implementation of self-attention (QSA)-the core operation in transformers and large language models-which predicts future elements of a sequence by forming overlap-weighted combinations of past data.
By Alessio Pecilli, Matteo Rosati
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
The paper proposes Entanglement-Weighted Pruning (EWP), a method for unlearning a client’s contribution from a federated quantum classifier without retraining from scratch. EWP scores each trainable circuit parameter by combining a Fisher‑information estimate on the target client’s data with a structural entanglement weight, pruning the lowest‑scoring parameters and optionally fine‑tuning the remaining ones. Experiments on a four‑qubit data‑re‑uploading ansatz trained with FedAvg across five simulated supply‑chain‑risk clients show that EWP achieves accuracy comparable to full retraining while reducing forgetting and wall‑clock time by about sixteenfold, outperforming random, Fisher‑only, or entanglement‑only pruning.
By Aditya Kumar, Sumit Chongder
arXiv:2509. 14026v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions.
By Jiun-Cheng Jiang, Morris Yu-Chao Huang, Tianlong Chen, Hsi-Sheng Goan
The paper compares single‑qubit re‑uploading (serial) variational quantum circuits with multi‑qubit circuits that use the same encoding budget for one‑dimensional inputs. It shows that although a serial circuit can represent any function a multi‑qubit circuit can, its first‑order training is more costly because all parameters move the state along a single phase‑locking direction, limiting the number of effective tangent directions. Experiments demonstrate that for larger encoding budgets the serial circuit converges far less often than the multi‑qubit control, and its held‑out error can be up to 1.5 times higher under Adam optimization.
By Michael Poppel, David Bucher, Maximilian Zorn, Markus Baumann, Sebastian W\"olckert, Claudia Linnhoff-Popien, Philipp Altmann, Jonas Stein
arXiv:2610.06076v2 Announce Type: replace-cross
Abstract: Preparing quantum states from classical data can cost more than the computation they serve; most loaders tailor a circuit to each input. Here...
By Pablo Herrero G\'omez, Antonio Jimeno Morenilla, David Mu\~noz-Hern\'andez, Higinio Mora Mora
arXiv:2609.37134v1 Announce Type: cross
Abstract: Frozen language models (LMs) are increasingly used as fixed feature extractors for downstream reranking, scoring, and preference modeling, raising a...
By Emily Jimin Roh, Hyojun Ahn, Hoyeong Lee, Soohyun Park, Sung Whan Yoon, Vaneet Aggarwal, Joongheon Kim
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