1434 matching published research entries
Quantum Research · 2026-11-04
arXiv:2611.04075v1 Announce Type: new Abstract: In Fault-Tolerant Quantum Computing (FTQC) using surface codes, reducing $T$-count is critical for efficient computation due to the high cost of $T$-state distillation. Probabilistic Quantum Circuit with Fallback (PQF) synthesis significantly lowers $T$-counts compared to standard protocols like Gridsynth, but introduces high circuit dynamism through extra gates…
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.04047v1 Announce Type: new Abstract: We consider a quantum integrated sensing and communication problem over an optical link in which a low-power transmitter simultaneously communicates with a receiver and identifies the range of a defect producing a backscattered signal.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.04009v1 Announce Type: new Abstract: It was conjectured by Wehrl and proven by Lieb that the Shannon differential entropy of the Husimi function of a continuous-variable quantum state is lower bounded by the value it takes on coherent states. This corroborates the intuition, familiar from Heisenberg's uncertainty relations, that coherent states are minimal uncertainty states.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03987v1 Announce Type: new Abstract: Optimizing continuous-variable functions on qubit-based quantum hardware requires a finite representation of the search space and a solver-compatible representation of the objective function.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03984v1 Announce Type: new Abstract: Nitrogen-vacancy (NV) center spin ensembles are a promising platform for accurate vector magnetometry, as they offer measurements referenced to a spin Hamiltonian in an anisotropic crystal with native vector sensing capabilities.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03943v1 Announce Type: new Abstract: Standard analyses of quantum phase sensing often neglect the overhead of generating squeezed-light by treating the pump as an undepleted classical resource. Realistically, the pump is a quantum object that undergoes significant depletion and entangles with the produced squeezed light, raising the question of how the crossover from a classical to a quantum pump limits…
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03910v1 Announce Type: new Abstract: Multiple seemingly distinct frameworks are commonly proposed to explain the emergence of effective non-Hermitian dynamics in quantum systems coupled to an environment. Furthermore, it remains contested how to properly calculate expectation values or whether thermal equilibrium states exist in non-Hermitian systems.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03908v1 Announce Type: new Abstract: Bosonic and fermionic quantum statistics describe all known elementary and emergent particles in three dimensions, although many attempts have been made to discover other valid quantum statistical frameworks.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03894v1 Announce Type: new Abstract: The algebraic and environmental characteristics of mixed unitary quantum operations are investigated. It is established that the composition of two uniformly n-mixed unitary channels is itself n-noisy, and this n-noisy property is shown to be an invariant under unitary equivalence.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03891v1 Announce Type: new Abstract: Quantum reservoir computing (QRC) processes temporal information through quantum dynamics, but it remains unclear whether small QRC models can forecast real data competitively with classical models and whether their predictions survive the transfer from ideal simulation to noisy hardware.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03851v1 Announce Type: new Abstract: We propose a physics-informed neural network (PINN) approach for high-fidelity quantum gate learning in both one- and two-qubit systems. We train a PINN using the time-dependent Schr\"{o}dinger equation, where smooth control fields with fixed boundary conditions are obtained by minimizing a loss function that considers model dynamics, gate error, physical constraints…
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03845v1 Announce Type: new Abstract: We introduce a scalable framework for globally optimal quantum-circuit partitioning and entanglement-aware block resynthesis. The partitioning problem is formulated as an integer linear program over qubit-feasible convex subcircuits, supporting objectives such as minimizing the number of partitions or a weighted execution cost.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03811v1 Announce Type: new Abstract: We find that coherent error can induce a phase transition from the clean surface code phase into an extended long-range entangled critical regime with diverging Markov length. To demonstrate this, we establish two general results of independent interest.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03788v1 Announce Type: new Abstract: Entanglement-based quantum key distribution from ground to satellite has been proposed as a scalable primitive for global quantum networks and will be demonstrated as part of the imminent QEYSSat mission. Both theoretical studies and experimental emulations have shown that a significant asymptotic key rate can be expected.
arXiv QuantumRead article →Quantum Research · 2026-11-04
arXiv:2611.03706v1 Announce Type: new Abstract: A quantum-secure withdrawal framework is presented by replacing reproducible classical credentials with quantum tokens for authentication. The protocol integrates token generation, authentication, and verification into the existing banking workflow using experimentally motivated quantum control techniques.
arXiv QuantumRead article →Machine Learning · 2026-11-04
arXiv:2611.04043v1 Announce Type: new Abstract: We study score learning from denoising pairs when the data contain a hidden non-Gaussian component. If covariance reveals the component, its subspace can be recovered directly. If covariance is isotropic, the first distinguishing moment has order $k^\ast$, and the learner must extract that moment from clean points and their noisy views.
arXiv MLRead article →Machine Learning · 2026-11-04
arXiv:2611.04010v1 Announce Type: new Abstract: To enable effective generalization in offline meta-RL, it is essential to accurately infer the underlying task through latent task representations, which provide a compact and transferable summary of task-specific characteristics for guiding policy generation.
arXiv MLRead article →Machine Learning · 2026-11-04
arXiv:2611.04007v1 Announce Type: new Abstract: Prestress governs the stability, stiffness, and dynamic behavior of tensegrity structures, but it is not directly observable from sparse structural measurements, and it may decrease during service life. Under strong excitation, the cables may slacken and the dynamics become nonsmooth.
arXiv MLRead article →Machine Learning · 2026-11-04
arXiv:2611.03981v1 Announce Type: new Abstract: This paper develops a mean-field formation transport method with low online computational cost for large-scale multi-agent systems. Classical mean-field planners often require repeated solutions of coupled forward and backward partial differential equations, making online replanning computationally demanding.
arXiv MLRead article →Machine Learning · 2026-11-04
arXiv:2611.03968v1 Announce Type: new Abstract: Probing frozen representations layer by layer is now standard practice, but reporting when a signal first becomes decodable requires a rule for reading a depth curve, and the obvious rule is wrong.
arXiv MLRead article →Machine Learning · 2026-11-04
arXiv:2611.03966v1 Announce Type: new Abstract: Large Language Bayes (LLB) transforms a natural language problem description into a collection of candidate probabilistic programs and performs inference within each model, combining their predictions using Bayesian model averaging.
arXiv MLRead article →Machine Learning · 2026-11-04
arXiv:2611.03964v1 Announce Type: new Abstract: Evolution equations with time-dependent coefficients force a neural solver to spend capacity on a scale that the equation already determines. We show that a single classical object, the Ermakov-Pinney scale function $\rho$, can be written into the input layer of a physics-informed neural network as a parameter-free embedding.
arXiv MLRead article →Machine Learning · 2026-11-04
arXiv:2611.03962v1 Announce Type: new Abstract: Many causal inference problems require sampling from interventional outcome distributions rather than estimating low-dimensional summaries. Existing approaches often rely on fitting generative models of the target distribution together with additional nuisance components, which can be computationally demanding for high-dimensional outcomes.
arXiv MLRead article →Machine Learning · 2026-11-04
arXiv:2611.03955v1 Announce Type: new Abstract: Large language models (LLMs) can memorize and reproduce fragments of their training data, raising privacy and legal concerns that motivate machine unlearning. Yet poisoned training examples can remain statistically detectable after unlearning, which raises the question of whether current unlearning objectives remove what the model has memorized at all.
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