most citedApproximating local properties by tensor network states with constant bond dimension

11 citations

8 papers

math.AP2026

Analytical solutions of 1D Maxwell's equations via infinite-order expansions

David Wei Ge

Analytical solutions to Maxwell's equations are essential for understanding the causal and instantaneous behavior of electromagnetic fields, yet they are challenging to obtain in o…

cs.SE2026

ECLAIR: A Causally-Grounded AI Framework for Scientific Discovery in Empirical Software Engineering

Alejandro Velasco, Daniel Rodriguez-Cardenas, Dipin Khati +2

The scientific method has long guided empirical research in Software Engineering (SE), but the complexity of modern software systems often hinders its systematic application. This…

quant-ph2026

Hoare meets Heisenberg: A Lightweight Logic for Quantum Programs

Aarthi Sundaram, Robert Rand, Kartik Singhal +2

We show that Gottesman's (1998) semantics for Clifford circuits based on the Heisenberg representation gives rise to a lightweight Hoare-like logic for efficiently characterizing a…

cs.CL2026

Scaling Textual Gradients via Sampling-Based Momentum

Zixin Ding, Junyuan Hong, Zhan Shi +6

LLM-based prompt optimization, which uses LLM-provided ``textual gradients'' (feedback) to refine prompts, has emerged as an effective method for automatic prompt engineering. Howe…

quant-ph202611 cited

Approximating local properties by tensor network states with constant bond dimension

Yichen Huang

Classical simulation of quantum many-body systems is a fundamental challenge due to their exponentially large Hilbert spaces. Tensor network states are a powerful ansatz to efficie…

cs.IR20262 cited

Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval

Sachin Yadav, Deepak Saini, Anirudh Buvanesh +6

We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace. Conventional Siamese-style approa…