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Photonic Links for Spin-Based Quantum Sensors
Authors:
M. Reefaz Rahman,
Karsten Schnier,
Ryan Goldsmith,
Benjamin J. Lawrie,
Joseph M. Lukens,
Seongsin M. Kim,
Patrick Kung
Abstract:
A growing variety of optically accessible spin qubits have emerged in recent years as key components for quantum sensors, qubits, and quantum memories. However, the scalability of conventional spin-based quantum architectures remains limited by direct microwave delivery, which introduces thermal noise, electromagnetic cross-talk, and design constraints for cryogenic, high-field, and distributed sy…
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A growing variety of optically accessible spin qubits have emerged in recent years as key components for quantum sensors, qubits, and quantum memories. However, the scalability of conventional spin-based quantum architectures remains limited by direct microwave delivery, which introduces thermal noise, electromagnetic cross-talk, and design constraints for cryogenic, high-field, and distributed systems. In this work, we present a unified framework for RF-over-fiber (RFoF) control of optically accessible spins through RFoF optically detected magnetic resonance (ODMR) spectroscopy of nitrogen-vacancy (NV) centers in diamond. The RFoF platform relies on an electro-optically modulated telecom-band laser that transmits microwave signals over fiber and a high-speed photodiode that recovers the RF waveform to drive NV center spin transitions. We obtain an RFoF efficiency of 1.81\% at 2.90~GHz, corresponding to $P_{\mathrm{RF,out}}=-0.7$~dBm. The RFoF architecture provides a path toward low-noise, thermally isolated, and cryo-compatible ODMR systems bridging conventional spin-based quantum sensing protocols with emerging distributed quantum technologies.
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Submitted 29 January, 2026;
originally announced January 2026.
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An Interpretable Convolutional Neural Network Framework for Fluid Dynamics
Authors:
Kwame Agyei-Baah,
Muhammad Rizwanur Rahman,
E. R. Smith
Abstract:
Modelling fluid dynamics with machine learning (ML) has advanced rapidly, yet most data driven approaches remain opaque because they rely on complex architectures to capture nonlinear flow behaviour. This lack of interpretability limits the reliability and hinders the understanding of when and why some models succeed or fail. To address this, we present a transparent approach that provides insight…
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Modelling fluid dynamics with machine learning (ML) has advanced rapidly, yet most data driven approaches remain opaque because they rely on complex architectures to capture nonlinear flow behaviour. This lack of interpretability limits the reliability and hinders the understanding of when and why some models succeed or fail. To address this, we present a transparent approach that provides insights into how data-driven fluids dynamics and ML work. This is achieved by training a convolutional neural network (CNN), on data from a simple laminar fluid flow, to behave as an operator that exactly matches the finite-difference numerics, providing a direct link between well established theory and this new world of ML models. Importantly, the model demonstrates strong generalisation capability by reproducing the dynamics for a wide range of distinct and unseen flow conditions within the same flow category. The CNN learns the forward Euler three-point stencil weights, capturing physical principles such as consistency and symmetry despite having only three tuneable weights. This interpretable ML model goes beyond pure numerical training (numCNN),the approach is shown to work when trained on analytical (anCNN) and even molecular dynam ics (mdCNN) data. In some cases, the physics is not captured, and thanks to the simple and interpretable form, these CNNs provide insight into the limits, pitfalls and best practice of data-driven fluid models. Because the approach is based on finite-difference operators, it naturally extends to many structured-grid computational fluid dynamics problems, including turbulent, multiphase and multiscale flows as well as systems beyond the continuum such as molecular dynamics.
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Submitted 8 July, 2026; v1 submitted 17 January, 2026;
originally announced January 2026.
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Nanoscale Surfactant Transport: Bridging Molecular and Continuum Models
Authors:
Muhammad Rizwanur Rahman,
James P. Ewen,
Li Shen,
D. M. Heyes,
Daniele Dini,
E. R. Smith
Abstract:
Surfactant transport is central to a diverse range of natural phenomena, and for many practical applications in physics and engineering. Surprisingly, this process remains relatively poorly understood at the molecular scale. This study investigates the mechanism behind the transport of surfactant monolayers on flat and curved liquid vapor interfaces using nonequilibrium molecular dynamics simulati…
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Surfactant transport is central to a diverse range of natural phenomena, and for many practical applications in physics and engineering. Surprisingly, this process remains relatively poorly understood at the molecular scale. This study investigates the mechanism behind the transport of surfactant monolayers on flat and curved liquid vapor interfaces using nonequilibrium molecular dynamics simulations, which are compared with the continuum transport model. This approach not only provides fresh molecular level insight into surfactant dynamics, but also confirms the nanoscale mechanism of the lateral migration of surfactant molecules along a thin film that continuously deforms as surfactants spread. By connecting the continuum model where the long wave approximations prevail, to the molecular details where such approximations break down, we establish that the transport equation preserves substantial accuracy in capturing the underlying physics. Moreover, the relative importance of the different mechanisms of the transport process are identified. Consequently, we derive a novel, exact molecular equation for surfactant transport along a deforming surface. Finally, our findings demonstrate that the spreading of surfactants at the molecular scale adheres to expected scaling laws and aligns well with experimental observations.
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Submitted 10 August, 2024;
originally announced August 2024.
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Life and Death of a Thin Liquid Film
Authors:
Muhammad Rizwanur Rahman,
Li Shen,
James P. Ewen,
D. M. Heyes,
Daniele Dini,
E. R. Smith
Abstract:
Thin films, bubbles and membranes are central to numerous natural and engineering processes, i.e., in thin-film solar cells, coatings, biosensors, electrowetting displays, foams, and emulsions. Yet, the characterization and an adequate understanding of their rupture is limited by the scarcity of atomic detail. We present here the complete life-cycle of freely suspended films using non-equilibrium…
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Thin films, bubbles and membranes are central to numerous natural and engineering processes, i.e., in thin-film solar cells, coatings, biosensors, electrowetting displays, foams, and emulsions. Yet, the characterization and an adequate understanding of their rupture is limited by the scarcity of atomic detail. We present here the complete life-cycle of freely suspended films using non-equilibrium molecular dynamics simulations of a simple atomic fluid free of surfactants and surface impurities, thus isolating the fundamental rupture mechanisms. Counter to the conventional notion that rupture occurs randomly, we discovered a short-term 'memory' by rewinding in time from a rupture event, extracting deterministic behaviors from apparent stochasticity. A comprehensive investigation of the key rupture-stages including both unrestrained and frustrated propagation is made - characterization of the latter leads to a first-order correction to the classical film-retraction theory. Furthermore, the highly resolved time window reveals that the different modes of the morphological development, typically characterized as heterogeneous nucleation and spinodal decomposition, continuously evolve seamlessly with time from one into the other.
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Submitted 1 November, 2023;
originally announced November 2023.
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Thin Film Rupture from the Atomic Scale
Authors:
Muhammad Rizwanur Rahman,
Li Shen,
James P. Ewen,
Benjamin Collard,
D. M. Heyes,
Daniele Dini,
E. R. Smith
Abstract:
The retraction of thin films, as described by the Taylor-Culick (TC) theory, is subject to widespread debate, particularly for films at the nanoscale. We use non-equilibrium molecular dynamics simulations to explore the validity of the assumptions used in continuum models, by tracking the evolution of holes in a film. By deriving a new mathematical form for the surface shape and considering a loca…
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The retraction of thin films, as described by the Taylor-Culick (TC) theory, is subject to widespread debate, particularly for films at the nanoscale. We use non-equilibrium molecular dynamics simulations to explore the validity of the assumptions used in continuum models, by tracking the evolution of holes in a film. By deriving a new mathematical form for the surface shape and considering a locally varying surface tension at the front of the retracting film, we reconcile the original theory with our simulation data to recover a corrected TC speed valid at the nanoscale.
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Submitted 12 December, 2022; v1 submitted 18 November, 2022;
originally announced November 2022.
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The Intrinsic Fragility of the Liquid-Vapor Interface: A Stress Network Perspective
Authors:
Muhammad Rizwanur Rahman,
Li Shen,
James P. Ewen,
Daniele Dini,
E. R. Smith
Abstract:
The evolution of the liquid-vapour interface of a Lennard-Jones fluid is examined with molecular dynamics simulations using the intrinsic sampling method. Results suggest, in agreement with capillary wave theory, clear damping of the density profiles as the temperature is increased. We identify a linear variation of the space-filling nature (fractal dimension) of the stress-clusters at the intrins…
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The evolution of the liquid-vapour interface of a Lennard-Jones fluid is examined with molecular dynamics simulations using the intrinsic sampling method. Results suggest, in agreement with capillary wave theory, clear damping of the density profiles as the temperature is increased. We identify a linear variation of the space-filling nature (fractal dimension) of the stress-clusters at the intrinsic surface with increasing surface tension, or equivalently, with decreasing temperature. A percolation analysis of these stress networks indicates that the stress field is more disjointed at higher temperatures. This leads to more fragile interfaces that result in a reduction in surface tension at higher temperature.
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Submitted 5 April, 2022; v1 submitted 24 January, 2022;
originally announced January 2022.
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Double emulsion drop evaporation and resurfacing of daughter droplet
Authors:
Muhammad Rizwanur Rahman,
Prashant R. Waghmare
Abstract:
In this study, we present experimental and theoretical analyses of double emulsion drop evaporation. After the apparent completion of evaporation of the inner phase of a double emulsion drop, surprisingly, a resurfacing of a daughter droplet is observed. We further investigated to hypothesize this phenomenon which allowed us to obtain a prolonged fixed contact line evaporation for a single phase d…
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In this study, we present experimental and theoretical analyses of double emulsion drop evaporation. After the apparent completion of evaporation of the inner phase of a double emulsion drop, surprisingly, a resurfacing of a daughter droplet is observed. We further investigated to hypothesize this phenomenon which allowed us to obtain a prolonged fixed contact line evaporation for a single phase drop along with similar occurrence of resurfacing as of the double emulsion drops.
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Submitted 24 October, 2018;
originally announced October 2018.