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Nick Reynolds

Publications and source records attributed to Nick Reynolds.

3 recordsLinked to original sources

A Dual Metastable-State Encoding Architecture for Quantum Processing with $^{171}\mathrm{Yb}$ Atom Arrays

Neutral-atom arrays combine scalable qubit registers, long coherence times, flexible optical control, and strong Rydberg-mediated entangling interactions, making them a promising platform for quantum information processing. However, physical error rates remain a challenge, and fault-tolerant quantum error correction (QEC) requires repeated mid-circuit measurement and reset of ancilla qubits without disturbing nearby data qubits. This requirement introduces significant control and architectural overhead, making qubit encoding an important architectural decision. Here, we propose a dual metastable-state qubit encoding for $^{171}\mathrm{Yb}$ atoms that utilizes two independent qubit subspaces in the $(6s6p)\,{}^3\mathrm{P}_0$ and $(6s6p)\,{}^3\mathrm{P}_2$ manifolds. The ${}^3\mathrm{P}_0$ manifold provides a long-coherence nuclear-spin (NS) qubit suitable for storage and arithmetic operations, while the ${}^3\mathrm{P}_2$ manifold provides a hyperfine-spin (HF) qubit, with $\Delta_{\mathrm{HF}} = 2\pi \times 6.7~\mathrm{GHz}$, that enables fast Raman operations and direct state-selective imaging. Coherent shelving between the two metastable manifolds connects the qubit subspaces, allowing operations to be assigned to spectrally distinct processor zones. We simulate single-qubit and two-qubit gate fidelities in ${}^3\mathrm{P}_2$, as well as coherent shelving between the HF and NS qubit subspaces. We incorporate these physical-level estimates into an architectural resource estimation and logical-level simulation. Our approach integrates mid-circuit measurements and fast qubit operations within a single-species platform, providing a versatile framework for future fault-tolerant quantum computing with neutral-atom qubits.

quant-ph

bursty_dynamics: A Python Package for Exploring the Temporal Properties of Longitudinal Data

Understanding the temporal properties of longitudinal data is critical for identifying trends, predicting future events, and making informed decisions in any field where temporal data is analysed, including health and epidemiology, finance, geosciences, and social sciences. Traditional time-series analysis techniques often fail to capture the complexity of irregular temporal patterns present in such data. To address this gap, we introduce bursty_dynamics, a Python package that enables the quantification of bursty dynamics through the calculation of the Burstiness Parameter (BP) and Memory Coefficient (MC). In temporal data, BP and MC provide insights into the irregularity and temporal dependencies within event sequences, shedding light on complex patterns of disease aetiology, human behaviour, or other information diffusion over time. An event train detection method is also implemented to identify clustered events occurring within a specified time interval, allowing for more focused analysis with reduced noise. With built-in visualisation tools, bursty_dynamics provides an accessible yet powerful platform for researchers to explore and interpret the temporal dynamics of longitudinal data. This paper outlines the core functionalities of the package, demonstrates its applications in diverse research domains, and discusses the advantages of using BP, MC, and event train detection for enhanced temporal data analysis.

q-bio.QM

Detection of Irregular, Sub-mm Opaque Structures in the Orion Molecular Clouds: Protostars within 10000 years of formation?

We report ALMA and VLA continuum observations that potentially identify the four youngest protostars in the Orion Molecular Clouds taken as part of the Orion VANDAM program. These are distinguished by bright, extended, irregular emission at 0.87 mm and 8 mm and are optically thick at 0.87 mm. These structures are distinct from the disk or point-like morphologies seen toward the other Orion protostars. The 0.87 mm emission implies temperatures of 41-170 K, requiring internal heating. The bright 8 mm emission implies masses of 0.5 to 1.2 M_sun assuming standard dust opacity models. One source has a Class 0 companion, while another exhibits substructure indicating a companion-candidate. Three compact outflows are detected, two of which may be driven by companions, with dynamical times of ~300 to ~400 years. The slowest outflow may be driven by a first hydrostatic core. These protostars appear to trace an early phase when the centers of collapsing fragments become optically thick to their own radiation and compression raises the gas temperature. This phase is thought to accompany the formation of hydrostatic cores. A key question is whether these structures are evolving on free fall times of ~100 years, or whether they are evolving on Kelvin-Helmholtz times of several thousand years. The number of these sources imply a lifetime of ~6000 years, in closer agreement with the Kelvin-Helmholtz time. In this case, rotational and/or magnetic support could be slowing the collapse.

astro-ph.SR