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:2607. 11013v1 Announce Type: cross Abstract: 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.
arXiv:2602. 23409v3 Announce Type: replace-cross Abstract: Angle-encoded variational quantum circuits admit a truncated Fourier series representation of their output, but approximating functions with maximum frequency $\omega_{\max}$ using fixed unary encoding requires $\mathcal{O}(\omega_{\max})$ encoding gates.
arXiv:2607. 16800v1 Announce Type: cross Abstract: Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, yet their training suffers from sensitivity to circuit depth, initialization, and landscape pathologies such as barren plateaus.
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. 23743v2 Announce Type: replace-cross Abstract: Variational quantum circuits train poorly on chaotic forecasting, usually blamed on barren plateaus (exponentially vanishing gradients).
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 investigates whether the adaptive circuit‑growth method Q‑FLAIR, which stops adding gates when training loss ceases to improve, can be combined with Caro et al.’s bound on generalization to predict how circuit depth should scale with training data size. Experiments on full‑resolution MNIST 3‑vs‑5 classification with training sets from 2,000 to 10,000 samples show no clear relationship between dataset size and the final circuit size; both circuit size and test accuracy vary non‑monotonically, and seed‑to‑seed variance is large. While the empirical generalization gap never exceeds Caro et al.’s bound in 14 of 15 runs, the gap correlates only weakly with the bound’s value, indicating that the bound’s predictive power is limited.
arXiv:2609.14424v1 Announce Type: cross Abstract: Reporting the Fisher geometry of a trained variational quantum model is routine; quoting the shot budget that would establish it is not. Certifying a...
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.
The paper studies how an eavesdropper can adaptively attack quantum key distribution (QKD) systems when channel noise and device drift vary over time. By modeling the attack as a constrained Markov decision process and using reinforcement learning to jointly search gate structures and rotation angles, the authors construct compact attack circuits that perform near the theoretical upper bound for both device‑independent E91 and BB84 protocols under realistic noise models. The results show that adaptive attacks can significantly increase the eavesdropper’s information compared to fixed‑circuit strategies, and that the learned attacks recover known optimal cloners and key‑rate bounds.
The paper demonstrates that by choosing unit‑quaternion coordinates, encrypted updates for variational quantum circuits become bilinear, allowing depth‑one homomorphic federated learning without bootstrapping. This coordinate choice reduces encrypted rotation updates to a single multiplicative level and federated averaging to zero levels, eliminating the previously prohibitive cost of one round per gate. Experiments across two cryptographic backends and up to 20 clients show negligible aggregation error and no measurable loss in utility, with hardware validation on a 156‑qubit processor achieving near‑optimal fidelity.