Optimal control of a quantum sensor: A fast algorithm based on an analytic solution

Anno: 2024

Autori: Hernandez-Gomez S., Balducci F., Fasiolo G., Cappellaro P., Fabbri N., Scardicchio A.

Affiliazione autori: Univ Firenze, European Lab Nonlinear Spect LENS, I-50019 Sesto Fiorentino, Italy; Univ Firenze, Dipartimento Fis Astron, I-50019 Sesto Fiorentino, Italy; MIT, Res Lab Elect, Cambridge, MA 02139 USA; Abdus Salam Int Ctr Theoret Phys, Str Costiera 11, I-34151 Trieste, Italy; INFN, Sez Trieste, Via Valerio 2, I-34127 Trieste, Italy; SISSA, Via Bonomea 265, I-34136 Trieste, Italy; Univ Trieste, Piazzale Europa 1, I-34127 Trieste, Italy; MIT, Dept Nucl Sci & Engn, Dept Phys, Cambridge, MA 02139 USA; Consiglio Nazl Ric CNR INO, Ist Nazl Ott, I-50019 Sesto Fiorentino, Italy.

Abstract: Quantum sensors can show unprecedented sensitivities, provided they are controlled in a very specific, optimal way. Here, we consider a spin sensor of time-varying fields in the presence of dephasing noise, and we show that the problem of finding the pulsed control field that optimizes the sensitivity (i.e., the smallest detectable signal) can be mapped to the determination of the ground state of a spin chain. We find an approximate but analytic solution of this problem, which provides a lower bound for the sensitivity and a pulsed control very close to optimal, which we further use as initial guess for realizing a fast simulated annealing algorithm. We experimentally demonstrate the sensitivity improvement for a spin-qubit magnetometer based on a nitrogen-vacancy center in diamond.

Giornale/Rivista: SCIPOST PHYSICS

Volume: 17 (1)      Da Pagina: 4-1  A: 4-20

Maggiori informazioni: This work was supported by the European Commission-EU under GAn. 101070546-MUQUABIS, and by the European Union-Next Generation EU within the PNRRMUR Project PE0000023-NQSTI, the PRIN 2022 PNRR M4C2 Project QUASAR 20225HYM8N[CUP B53D23004980006], and the I-PHOQS Infrastructure[IR0000016, ID D2B8D520, CUPD2B8D520]
Parole chiavi: Solid-state Spin; Spherical Model; Diamond; Optimization
DOI: 10.21468/SciPostPhys.17.1.004

Citazioni: 2
dati da “WEB OF SCIENCE” (of Thomson Reuters) aggiornati al: 2025-05-18
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