1714 matching published research entries
Quantum Research · 2026-10-30
arXiv:2610.37839v1 Announce Type: new Abstract: We present an end-to-end resource estimation of Shor's algorithm on superconducting cat qubits, from its mathematical description to the allocation and scheduling of physical qubits on a chip layout. We review modern versions of Shor's algorithm for RSA, ECDLP and FFDLP, and make their logical circuits and trade-offs explicit.
arXiv QuantumRead article →Quantum Research · 2026-10-30
arXiv:2610.37831v1 Announce Type: new Abstract: Covariant integral quantization is developed on the double discrete cylinder phase space, with the torus as configuration space, and yields the Wigner distribution and the stellar representation. This phase space is particularly well suited to translationally invariant states, such as the Gottesman Kitaev Preskill (GKP) states.
arXiv QuantumRead article →Quantum Research · 2026-10-30
arXiv:2610.37807v1 Announce Type: new Abstract: Magic states are a leading proposal for realizing fault-tolerant and universal quantum computation. At the present moment, much of the theory of magic states is not understood. Even simple questions such as what single qubit states have magic remain open.
arXiv QuantumRead article →Quantum Research · 2026-10-30
arXiv:2610.37791v1 Announce Type: new Abstract: We study the error thresholds of a new topological quantum error-correcting code under the independent bit-flip and phase-flip noisy channel. By mapping to classical spin-glass models, we find that if Pauli X and Z errors are equally probable, the threshold of vertex syndromes is improved by 30% compared to that of the toric code, while the threshold of plaquette…
arXiv QuantumRead article →Quantum Research · 2026-10-30
arXiv:2610.37785v1 Announce Type: new Abstract: The characterization of possible transformations between quantum states and the quantification of entanglement are central for understanding quantum resources. Explicit results are, however, scarce since they require complicated optimizations over transformation protocols or state decompositions.
arXiv QuantumRead article →Quantum Research · 2026-10-30
arXiv:2610.37772v1 Announce Type: new Abstract: Grover's algorithm is a textbook example of quantum advantage, providing a quadratic query speedup over classical methods for unstructured search. In this Letter, we characterize the non-stabilizerness, or magic, developed throughout Grover's protocol, quantifying quantum complexity with respect to the efficiently simulable stabilizer states.
arXiv QuantumRead article →Quantum Research · 2026-10-30
arXiv:2610.37755v1 Announce Type: new Abstract: Preparing complex quantum states of light for quantum information processing tasks often proceeds by converting from simpler states via experimentally accessible operations. We provide fundamental bounds on such probabilistic conversions, both exact and approximate.
arXiv QuantumRead article →Quantum Research · 2026-10-30
arXiv:2610.37745v1 Announce Type: new Abstract: Ordinary $0$-form symmetries $G$ can mix with $1$-form symmetries $\Pi_2$ to form a $2$-group $\mathcal{G}$. We give a concrete lattice definition for a class of finite $2$-group symmetries in terms of local relations among symmetry operators. These relations identify the Postnikov class $[\rho]\in H^3(BG,\Pi_2)$ that characterizes the mixing.
arXiv QuantumRead article →Machine Learning · 2026-10-30
arXiv:2610.38037v1 Announce Type: new Abstract: Context: Non-determinism in deep learning algorithm design and implementation leads to performance variation, meaning model performance is not a single value, but rather a distribution. These model performance distributions are underexplored despite their impact on robustness.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.38036v1 Announce Type: new Abstract: Bounds based on Integral Probability Metrics (IPMs) are ubiquitous in machine learning and statistics, appearing, e.g., in generalization, alignment, fairness, and privacy. IPMs reduce the discrepancy between two distributions to a single worst-case gap between expectations.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.38034v1 Announce Type: new Abstract: Reinforcement learning holds the promise of robots that learn manipulation skills from interaction alone, without pretrained priors or demonstration data. In practice, on-policy methods such as PPO routinely stall on long-horizon, contact-rich tasks: dense reward shaping carries the policy through stages, yet stitching them together and discovering sparsely rewarded…
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.38021v1 Announce Type: new Abstract: Glioblastoma and other high-grade gliomas (HGG) have a poor prognosis and are associated with progressive functional decline. The standard of care to assess progressive disease is contrast-enhanced MRI, which is expensive, claustrophobic, and performed only once every 2-3 months.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.38015v1 Announce Type: new Abstract: Numbers computed from trained models, such as an example's difficulty or a model's benchmark lead, change with seed and prompt. We ask when such measurements can be corrected or compared. Noise shrinks correlations, and the textbook correction divides by the square root of two estimated reliabilities.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.38003v1 Announce Type: new Abstract: Earthquake sequences are stochastic on two separated timescales: the seismic waves of any individual earthquake propagate over seconds to minutes, while the earthquakes themselves arrive as a sparse, self-exciting occurrence process, with inter-event intervals from seconds to months. Respecting both scales in one training objective is the open challenge.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.38001v1 Announce Type: new Abstract: Benign fine-tuning can erode safety alignment in large language models, and sequential adaptation substantially compounds this risk. Prior work mitigates this erosion by replaying harmful-prompt refusals and benign-compliance examples, while other work instead matches base-model logits via distillation.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.37994v1 Announce Type: new Abstract: PairFormer-based all-atom models demonstrate strong capabilities in biomolecular binder design. However, existing methods often use all-atom representations for modality-specific binder design, such as protein and peptide design.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.37993v1 Announce Type: new Abstract: This paper studies two non-transvenous implantable cardioverter-defibrillators (ICDs), namely the Subcutaneous ICD (S-ICD) and the Extravascular ICD (EV-ICD). Both avoid the complications of transvenous leads but are prone to oversensing, which can trigger inappropriate shocks associated with increased mortality.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.37986v1 Announce Type: new Abstract: Full-order surrogates for high-dimensional nonlinear flows are often unnecessary when applications require only selected degrees of freedom. We propose reduced-order models that bypass full-state reconstruction by deploying graph neural networks (GNNs) directly on coarse meshes.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.37979v1 Announce Type: new Abstract: Identifying a small molecule from its tandem mass spectrum is limited by paired data. Foundation models offer a way around this scarcity: pretrain a spectrum encoder on unlabeled spectra and a molecule encoder on a large corpus of structures, then align the two frozen representations, as in vision-language and as the Platonic Representation Hypothesis suggests.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.37976v1 Announce Type: new Abstract: We empirically investigate how MLPs and Transformers differ when trained on data sets containing one or more features encoded by noisy k-parity. Noisy k-parity gives us hard synthetic data sets in which we can control the predictivity and availability of every feature.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.37972v1 Announce Type: new Abstract: Machine learning (ML) architectures are being developed for fast emulation of numerical simulators to accelerate both scientific discovery and engineering design. While these approaches are well-tested on popular PDE (partial differential equation) benchmarks, it is necessary to evaluate their performance on datasets that more accurately reflect industrial and…
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.37963v1 Announce Type: new Abstract: Time-series data play an important role in various tasks, such as weather forecasting, web traffic analysis, and speech recognition. Recently, pioneering studies have increasingly adopted Joint-Embedding Predictive Architecture (JEPA) based encoders to learn time-series representations through latent-space prediction.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.37952v1 Announce Type: new Abstract: Rounded capacity cuts (RCCs) are among the most effective cuts for capacitated vehicle-routing relaxations, but exact separation is computationally prohibitive at scale. Existing heuristic and neural separators can be fast, but often fail to produce sufficiently many effective cuts within practical separation budgets.
arXiv MLRead article →Machine Learning · 2026-10-30
arXiv:2610.37936v1 Announce Type: new Abstract: Generative flows can admit different velocity fields that induce the same continuous marginal evolution but behave differently after numerical discretization. We study this finite-step non-equivalence while keeping the marginal path and numerical method fixed.
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