Machine classification for probe-based quantum thermometry

Luiz, F. S.; Oliveira, A. de; Fanchini, Felipe F.; Landi, Gabriel T.

doi:10.1103/physreva.105.022413

Cited by 8 publications

(4 citation statements)

References 61 publications

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“…We then combine these numerical ansatzes with physical insights to analytically prove that C can display the quadratic scaling of equation (2), with a slightly worse prefactor that depends on the locality of the Hamiltonian (3), thus answering affirmatively Q. These results add on recent applications of Machine-Learning based techniques in the field of quantum thermodynamics [55][56][57][58][59][60][61][62], as well as in other domains, including protein folding [63], many-body problems [64][65][66], geosciences [67], algorithm discovery [68].…”

Section: Via Realistic Hamiltonians Ie Featuring Two-body and Local I...mentioning

confidence: 81%

Optimal thermometers with spin networks

Abiuso,

Andrea Erdman,

Ronen

et al. 2024

Quantum Sci. Technol.

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The heat capacity C of a given probe is a fundamental quantity that determines, among other properties, the maximum precision in temperature estimation. In turn, C is limited by a quadratic scaling with the number of constituents of the probe, which provides a fundamental limit in quantum thermometry. Achieving this fundamental bound with realistic probes, i.e. experimentally amenable, remains an open problem. In this work, we tackle the problem of engineering optimal thermometers by using networks of spins. Restricting ourselves to two-body interactions, we derive general properties of the optimal conﬁgurations and exploit machine-learning techniques to ﬁnd the optimal couplings. This leads to simple architectures, which we show analytically to approximate the theoretical maximal value of C and maintain the optimal scaling for short- and long-range interactions. Our models can be encoded in currently available quantum annealers, and ﬁnd application in other tasks requiring Hamiltonian engineering, ranging from quantum heat engines to adiabatic Grover’s search.

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Section: Via Realistic Hamiltonians Ie Featuring Two-body and Local I...mentioning

confidence: 81%

Optimal thermometers with spin networks

Abiuso,

Andrea Erdman,

Ronen

et al. 2024

Quantum Sci. Technol.

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“…The non-equilibrium regime, i.e. when the probe is measured before reaching equilibrium, requires a more precise knowledge of the non-equilibrium dynamics but also offers new possibilities for estimating T with a higher precision [16,19,36,37] and lower backaction [38]. This has been extensively explored in the context of ultracold gases [4,5,[39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Strongly coupled fermionic probe for nonequilibrium thermometry

Ravell Rodríguez,

Mehboudi,

Horodecki

et al. 2024

New J. Phys.

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We characterise the measurement sensitivity, quantified by the Quantum Fisher Information (QFI), of a single-fermionic thermometric probe strongly coupled to the sample of interest, a fermionic bath, at temperature T. For nonequilibrium protocols, in which the probe is measured before reaching equilibrium with the sample, we find new behaviour of the measurement sensitivity arising due to non-Markovian dynamics. First, we show that the QFI displays a highly non-monotonic behaviour in time, in contrast to the Markovian case where it grows monotonically until equilibrium, so that non-Markovian revivals can be exploited to reach a higher QFI. Second,the QFI rate is maximised at a finite interrogation time t, which we characterize, in contrast to the solution t→0 known in the Markovian limit [Quantum 6, 869 (2022)]. Finally, we consider probes make up of few fermions and discuss different collective enhancements in the measurement precision.

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“…An estimator is said to be sufficient if, and only if, the pdf p(X| θ) of X 1 , ..., X n conditioned on θ is independent of θ [70] (pp. [305][306][307]. This property means that no particular function or combination of the samples X 1 , ..., X n in θ is more likely than others.…”

Section: Properties Of Estimatorsmentioning

confidence: 99%

“…In that sense, we could also employ adaptive strategies [24,28,128,231]; allowing us to optimize the coupling parameter γτ SE between the system and the bath, the probe state [230] or the basis (in the scenario of collective measurements). An extension of the model to continuous variables systems [305], and also the use of machine learning tools [306], are also promising research venues.…”

Section: Chapter 9 Conclusionmentioning

confidence: 99%

Estimação Bayesiana para termometria colisional e computação quântica não-holonômica em tempo ótimo

Alves

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Machine classification for probe-based quantum thermometry

Cited by 8 publications

References 61 publications

Optimal thermometers with spin networks

Optimal thermometers with spin networks

Strongly coupled fermionic probe for nonequilibrium thermometry

Estimação Bayesiana para termometria colisional e computação quântica não-holonômica em tempo ótimo

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