Recent Innovations in Solid-State and Molecular Qubits for Quantum Information Applications

Lavroff, Robert H; Pennington, Doran L; Hua, Ash Sueh; Li, Barry Yangtao; Williams, Jillian A; Alexandrova, Anastassia N.

doi:10.1021/acs.jpca.1c08677

Cited by 1 publication

(1 citation statement)

References 43 publications

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“…Spin defects in solid-state host materials have become prime candidate hardware systems for advanced quantum technologies. − Prominent representatives include atom-like quantum emitters in diamond, , silicon carbide, hexagonal boron nitride (hBN), , zinc oxide, transition metal dichalcogenides, as well as rare-earth ions in solids, , and optically active donors in silicon. , These atom-like emitters are attractive for quantum applications as they often possess spin states that can be readily manipulated and read out, while also displaying long coherence time, room-temperature operation, the ability to create entangled states, and spin-dependent optical transitions that allow for spin-photon interfacing and long-distance transmission of quantum information. , Additionally, the solid-state host materials are technology-ready. Potential devices can be realized leveraging well-established nanofabrication techniques from the semiconductor industry and hold prospects for seamless integration with on-chip electronic, magnetic and photonic nanostructures. − Relevant applications span over a wide range of fields including quantum communication − and computation, − quantum simulation, , and quantum metrology and sensing. ,, …”

Section: Introductionmentioning

confidence: 99%

Fast Characterization of Optically Detected Magnetic Resonance Spectra via Data Clustering

Stone,

Whitefield,

Kianinia

et al. 2024

J. Phys. Chem. C

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Optically detected magnetic resonance (ODMR) has become a well-established and powerful technique for measuring the spin state of solid-state quantum emitters, at room temperature. Relying on spin-dependent recombination processes involving the emitters' ground, excited and metastable states, ODMR is enabling spin-based quantum sensing of nanoscale electric and magnetic fields, temperature, strain and pressure, as well as imaging of individual electron and nuclear spins. Central to many of these sensing applications is the ability to reliably analyze ODMR data, as the resonance frequencies in these spectra map directly onto target physical quantities acting on the spin sensor. However, this can be onerous, as relatively long integration times�from milliseconds up to tens of seconds�are often needed to reach a signal-to-noise level suitable to determine said resonances using traditional fitting methods. Here, we present an algorithm based on data clustering that overcomes this limitation and allows determining the resonance frequencies of ODMR spectra with better accuracy (∼1.3× factor), higher resolution (∼4.7× factor) and/or overall fewer data points (∼5× factor) than standard approaches based on statistical inference. The proposed clustering algorithm (CA) is thus a powerful tool for many ODMR-based quantum sensing applications, especially when dealing with noisy and scarce data sets.

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