arXiv subjects

Akshat Rana

Publications and source records attributed to Akshat Rana.

5 recordsLinked to original sources

arXiv2026

Spin bath mediated long-lived coherent oscillations of NV centers in diamond

Decoherence is the biggest bottleneck in all quantum technologies. For nitrogen-vacancy (NV) centers in diamond, the loss of coherence is caused by the electron and nuclear spin bath of the diamond lattice. Here, we demonstrate that the spin bath - that typically causes decoherence - entangles the spin states of the NV electron and the host $^{14}$N nucleus. The many-body interaction between the $^{14}$N nucleus - electron - bath spins at an energy level anti-crossing occurring for an applied magnetic field orientation perpendicular to the NV axis is responsible for this effect. This is observed experimentally in NV ensembles via electron spin-echo measurements, where the echo envelope is modulated at the frequency of a $^{14}$N nuclear spin transition. Using numerical simulations, we show that the spin bath coupling to the NV centers is essential for observing this modulation. Due to the zero first-order Zeeman effect at the anti-crossing, the observed oscillations have long spin-echo coherence times, 2--3 times those at the parallel magnetic field orientation. The oscillation frequency is highly stable and robust against environmental fluctuations. These findings provide new opportunities for fundamental studies of many-body physics and quantum sensing.

quant-ph↗

arXiv2026

SG-CoT: An Ambiguity-Aware Robotic Planning Framework using Scene Graph Representations

Ambiguity poses a major challenge to large language models (LLMs) used as robotic planners. In this letter, we present Scene Graph-Chain-of-Thought (SG-CoT), a two-stage framework where LLMs iteratively query a scene graph representation of the environment to detect and clarify ambiguities. First, a structured scene graph representation of the environment is constructed from input observations, capturing objects, their attributes, and relationships with other objects. Second, the LLM is equipped with retrieval functions to query portions of the scene graph that are relevant to the provided instruction. This grounds the reasoning process of the LLM in the observation, increasing the reliability of robotic planners under ambiguous situations. SG-CoT also allows the LLM to identify the source of ambiguity and pose a relevant disambiguation question to the user or another robot. Extensive experimentation demonstrates that SG-CoT consistently outperforms prior methods, with a minimum of 10% improvement in question accuracy and a minimum success rate increase of 4% in single-agent and 15% in multi-agent environments, validating its effectiveness for more generalizable robot planning.

cs.RO↗

arXiv2025

What Work is AI Actually Doing? Uncovering the Drivers of Generative AI Adoption

Purpose: The rapid integration of artificial intelligence (AI) systems like ChatGPT, Claude AI, etc., has a deep impact on how work is done. Predicting how AI will reshape work requires understanding not just its capabilities, but how it is actually being adopted. This study investigates which intrinsic task characteristics drive users' decisions to delegate work to AI systems. Methodology: This study utilizes the Anthropic Economic Index dataset of four million Claude AI interactions mapped to O*NET tasks. We systematically scored each task across seven key dimensions: Routine, Cognitive, Social Intelligence, Creativity, Domain Knowledge, Complexity, and Decision Making using 35 parameters. We then employed multivariate techniques to identify latent task archetypes and analyzed their relationship with AI usage. Findings: Tasks requiring high creativity, complexity, and cognitive demand, but low routineness, attracted the most AI engagement. Furthermore, we identified three task archetypes: Dynamic Problem Solving, Procedural & Analytical Work, and Standardized Operational Tasks, demonstrating that AI applicability is best predicted by a combination of task characteristics, over individual factors. Our analysis revealed highly concentrated AI usage patterns, with just 5% of tasks accounting for 59% of all interactions. Originality: This research provides the first systematic evidence linking real-world generative AI usage to a comprehensive, multi-dimensional framework of intrinsic task characteristics. It introduces a data-driven classification of work archetypes that offers a new framework for analyzing the emerging human-AI division of labor.

econ.GN↗

arXiv2025

Imaging of microwave magnetic field orientation using continuous-wave experiments on nitrogen-vacancy centers in diamond

Imaging of microwave magnetic fields with nano-scale resolution has interesting applications. Specifically, detecting the orientation of the microwave fields is useful in condensed matter physics and quantum control. However, most of the existing methods for microwave field imaging are limited to detecting the magnitude of the fields. Due to their small sensor size and favorable optical and spin properties, nitrogen-vacancy (NV) centers in diamond are highly suitable for imaging dc and ac magnetic fields. The reported methods for detecting the orientation of microwave magnetic fields use pulsed Rabi frequency measurements. Here, we demonstrate imaging of the orientation of microwave magnetic fields by only using continuous-wave experiments on NV centers. This simplifies the sensor apparatus and is particularly advantageous in applications where pulsing of the target microwave field is not possible. The method requires static bias magnetic field oriented perpendicular to the quantization axis of NV centers. We detect the direction of an arbitrary microwave magnetic field using NV centers of two different orientations. Moreover, we demonstrate that the projection of the microwave fields onto a plane can be imaged using NV centers of single orientation. It can be straightforwardly implemented using a single NV center.

quant-ph↗

arXiv2023

Vector detection of AC magnetic fields by Nitrogen-Vacancy centers of single orientation in diamond

Nitrogen-Vacancy (NV) centers in diamond have useful properties for detecting both AC and DC magnetic fields with high sensitivity at nano-scale resolution. Vector detection of AC magnetic fields can be achieved by using NV centers having three different orientations. Here, we propose a method to achieve this by using NV centers of single orientation. In this method, a static magnetic field is applied perpendicular to the NV axis, leading to strong mixing of the $m_{s}=-1$ and $1$ electron spin states. As a result, all three electron spin transitions of the triplet ground state have non-zero dipole moments, with each transition coupling to a single component of the magnetic field. This can be used to measure both strength and orientation of the applied AC field. To validate the technique, we perform a proof of principle experiment using a subset of ensemble NV centers in diamond, all having the same orientation.

quant-ph↗