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.
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:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
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.
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.
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.
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.
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.
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.
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
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.
arXiv:2607. 21409v1 Announce Type: cross Abstract: A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs).
The paper introduces GenQAS, a tensor network‑guided reinforcement learning framework that uses a learned local transition model to generate synthetic circuit transitions for prioritized generative replay. By mixing these synthetic transitions with real experience during Double Deep Q‑Network updates, GenQAS addresses sample starvation in quantum architecture search. Across benchmarks ranging from 6 to 15 qubits, the method improves success probabilities, identifies compact circuits, and reduces steps to chemical accuracy by up to 92.7%.