The paper develops a finite‑measurement framework for inferring the symmetry group that a quantum learning model should respect, based on candidate transformations and limited data. It shows that observable‑invisible transformations correspond to the stabilizer of a projected state when the probe span is invariant, and that recovered generators form a valid subgroup with a continuous invisible space identified via its Lie algebra. The authors introduce an unbiased shadow statistic that improves estimation rates, establish optimal gap dependence through a commuting‑qubit lower bound, and provide tools for task validation, bias quantification, and capacity analysis, all illustrated with Ising‑chain calculations.
By Zeyu Chen
arXiv:2606. 11673v1 Announce Type: cross Abstract: Standard dot-product self-attention computes, in a single layer, only pairwise (order-2) interactions between tokens; representing a generic order-$k$ interaction is known to require either super-quadratic resources in one layer or composition across depth.
By Jian Xu, Chao Li, Delu Zeng, John Paisley, Qibin Zhao
arXiv:2607. 06230v1 Announce Type: cross Abstract: Parameterized quantum circuits (PQCs) are increasingly used as policies and value functions in quantum reinforcement learning, yet it remains unclear when and why quantum policies generalize.
By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao
arXiv:2606. 12211v1 Announce Type: cross Abstract: A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest.
By Jeongho Bang, Kyoungho Cho, Jeongwoo Jae
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:2607. 02444v1 Announce Type: cross Abstract: We study stabilizer state testing and learning with limited coherent quantum memory.
By Srinivasan Arunachalam, Louis Schatzki
arXiv:2606. 28833v1 Announce Type: new Abstract: Quantum kernel estimation on near-term hardware is shot-budgeted: every entry of the kernel Gram matrix is a Bernoulli expectation that must be sampled with a finite number of circuit executions.
By Jian Xu, Delu Zeng, Qibin Zhao
arXiv:2605. 30952v2 Announce Type: replace Abstract: Two recent results have reshaped quantum Gaussian processes (QGPs).
By Jian Xu, Chao Li, Guang Lin, Yuning Qiu, Delu Zeng, John Paisley, Qibin Zhao
arXiv:2608. 05110v1 Announce Type: cross Abstract: Near-term quantum hardware limits circuit depth and often imposes geometrically local connectivity for quantum generative models, restricting the output distributions accessible to shallow unitary Born models.
By Arunava Majumder, Marius Krumm, Hendrik Poulsen Nautrup, Hans J. Briegel
arXiv:2607. 17219v1 Announce Type: cross Abstract: Human survey respondents exhibit question-order effects that satisfy the QQ (quantum question) equality, an a priori, parameter-free prediction of the projective quantum question-order model.
By Pilsung Kang
arXiv:2510. 06945v2 Announce Type: replace Abstract: Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher information matrix (FIM) are effective for predicting their training and prediction performance.
By Lorenzo Pastori, Veronika Eyring, Mierk Schwabe
arXiv:2606. 13422v2 Announce Type: replace-cross Abstract: We develop theoretical foundations for a practical quantum-advantage mechanism in quantum-informed machine learning for chaotic dynamical systems.
By Maida Wang, Xiao Xue, Minh Chung, Peter V. Coveney