Stochastic Exceptional Points for Noise-Assisted Sensing

Li, Zhipeng; Li, Chenhui; Xiong, Ze; Xu, Guoqiang; Wang, Yongtai Raymond; Tian, Xi; Yang, Xin; Li, Zhu; Zeng, Qihang; Lin, Rongzhou; Liu, Ying; Lee, Jason; Ho, John S.; Qiu, Cheng‐Wei

doi:10.1103/physrevlett.130.227201

Cited by 22 publications

(4 citation statements)

References 34 publications

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“…Exceptional points (EPs) are the spectral singularities of non-Hermitian systems, at which the eigenvalues and the corresponding eigenstates coalesce 1-5 . These degenerate points have been observed in various physical systems, including optical microcavities [6][7][8][9][10] , plasmonic metamaterials 11,12 , photonic crystals 13,14 , acoustics 15 , electronics [16][17][18][19] , and superconducting circuits 20 . In particular, numerous intriguing phenomena are revealed in non-Hermitian optics and photonics.…”

Section: Main Textmentioning

confidence: 97%

Exceptional-point-enhanced phase sensing

Yang

Mao

2023

Preprint

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Optical sensors have become indispensable in various fields, ranging from gravitational wave detection to biomedical imaging and structural health monitoring. Target information is commonly encoded in the phase changes of optical signals. Enhancing the sensitivity to phase changes is crucial for detecting weak signals and extracting valuable data. Recently, exceptional points (EPs), a spectral singularity in non-Hermitian systems, have been explored in various physical systems for sensitivity enhancement. However, it requires precisely controlling the sensor parameters, which restricts the applicability of EP enhancement to certain types of sensors. To overcome this limitation, we propose a novel configuration to implement EP-enhanced sensing. In this approach, a conventional sensor is connected to a microresonator operated at EPs via an optical waveguide. The EP microresonator amplifies optical phase changes from the sensor, converting them into distinct and quantifiable resonance splitting in the transmission spectra of the system. As a proof-of-concept, we demonstrate a six-fold enhancement in the detection limit of fiber-optic strain sensing using this EP-enhanced configuration. By separating the EP control from the sensor to eliminate the need to finely tune the sensor, our configuration enables EP enhancement for a wide range of conventional sensors. This work provides a promising universal platform to apply EP enhancement to diverse phase-dependent structures for ultrahigh-sensitivity sensing in various applications.

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Section: Main Textmentioning

confidence: 97%

Exceptional-point-enhanced phase sensing

Yang

Mao

2023

Preprint

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“…The exotic properties of non-Hermitian systems [ 15 – 18 ] open potential possibilities for diverse advanced applications including phonon lasers [ 9 , 19 – 21 ], chiral mode conversion [ 8 , 22 – 26 ], and enhanced sensing [ 3 , 4 , 27 – 32 ]. The response enhancement at EPs has been widely investigated in microcavities [ 3 , 4 , 29 , 33 ], optomechanical systems [ 31 , 34 , 35 ], and circuits [ 28 , 36 – 38 ].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sensing Mechanism Based on Shifting an Exceptional Point

Mao,

Qin,

Zhang

et al. 2023

Research

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Non-Hermitian systems associated with exceptional points (EPs) are expected to demonstrate a giant response enhancement for various sensors. The widely investigated enhancement mechanism based on diverging from an EP should destroy the EP and further limits its applications for multiple sensing scenarios in a time sequence. To break the above limit, here, we proposed a new enhanced sensing mechanism based on shifting an EP. Different from the mechanism of diverging from an EP, our scheme is an EP nondemolition and the giant enhancement of response is acquired by a slight shift of the EP along the parameter axis induced by perturbation. The new sensing mechanism can promise the most effective response enhancement for all sensors in the case of multiple sensing in a time sequence. To verify our sensing mechanism, we construct a mass sensor and a gyroscope with concrete physical implementations. Our work will deepen the understanding of EP-based sensing and inspire designing various high-sensitivity sensors in different physical systems.

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“…Stochastic resonance has been observed in various macroscopic systems, such as the cyclic recurrence of ice ages, [11] the receptors in crickets [12] and crayfish, [13] as well as some artificial physical devices. [14][15][16] Recently, stochastic resonance was applied to microscopic systems [17][18][19][20][21][22] to show the possibility of phase noise metrology [23] or coherent signal transmission. [24] The unique observation in the present work due to stochastic resonance is from the strong asymmetry in the SET, which originates from different layouts and fabrication characteristics.…”

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confidence: 99%

Stochastic Resonance in a Single-Ion Nonlinear Mechanical Oscillator

Cui

Yuan

et al. 2023

Chinese Phys. Lett.

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Stochastic resonance is a counterintuitive phenomenon amplifying the weak periodic signal by application of external noise. We demonstrate the enhancement of a weak periodic signal by stochastic resonance in a trapped-ion oscillator when the oscillator is excited to the nonlinear regime and subject to an appropriate noise. Under the full control of the radio-frequency drive voltage, this amplification originates from the nonlinearity due to asymmetry of the trapping potential, which can be described by a forced Duffing oscillator model. Our scheme and results provide an interesting possibility to make use of controllable nonlinearity in the trapped ion, and pave the way toward a practical atomic sensor for sensitively detecting weak periodic signals from real noisy environment.

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Stochastic Exceptional Points for Noise-Assisted Sensing

Cited by 22 publications

References 34 publications

Exceptional-point-enhanced phase sensing

Exceptional-point-enhanced phase sensing

Enhanced Sensing Mechanism Based on Shifting an Exceptional Point

Stochastic Resonance in a Single-Ion Nonlinear Mechanical Oscillator

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