Exponential sensitivity revival of noisy non-Hermitian quantum sensing with two-photon drives

• URL: http://arxiv.org/abs/2303.16575v2
• Date: Tue, 28 May 2024 23:44:02 GMT
• Title: Exponential sensitivity revival of noisy non-Hermitian quantum sensing with two-photon drives
• Abstract summary: We analytically characterize and highlight the impact of loss and gain on the sensitivity revival and stability of non-Hermitian sensors. Our paper opens a way to significantly enhance the sensitivity by proactively tuning the loss and gain.
• Score: 3.031137751464259
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unique properties of multimode non-Hermitian lattice dynamics can be utilized to construct exponentially sensitive sensors. However, the impact of noise remains unclear, which may severely degrade their sensitivity. We analytically characterize and highlight the impact of loss and gain on the sensitivity revival and stability of non-Hermitian sensors. Defying the general belief that the superiority of quantum sensing will vanish in the presence of loss, we find that by proactively tuning the loss, the exponential sensitivity can be surprisingly regained when the sensing dynamics is stable. Furthermore, we prove that gain is crucial to fully revive the ideally exponential sensitivity and to ensure the stability of non-Hermitian sensing by making a balanced loss and gain. Our paper opens a way to significantly enhance the sensitivity by proactively tuning the loss and gain, which may promote future quantum sensing and quantum engineering.

Related papers

• Quantum-enhanced sensing in a driven-dissipative system via chiral waveguide [0.040150524643488644]
  Quantum sensors can achieve precision unattainable by classical sensors.<n>Dissipation coming from interaction with the environment can greatly degrade the performance of quantum sensors.<n>We study a system of two-level systems driven coherently at a detuned frequency.
  arXiv  Detail & Related papers  (2026-08-24T16:08:45Z)
• Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integration [50.18488308004802]
  Sen-Cap is a 3D motion capture framework that integrates multi-modal data from LiDAR and camera.<n>Sen-Cap achieves state-of-the-art performance on major metrics on Human-M3 and FreeMotion.<n>This combination of flexibility and robustness opens new opportunities for motion capture in real-world scenarios.
  arXiv  Detail & Related papers  (2026-08-03T14:16:18Z)
• When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift [64.37959940809633]
  We study robustness of Proximal Policy Optimization (PPO) under temporally persistent sensor failures.<n>We show Transformer-based sequence policies substantially outperform, RNN, and SSMs in robustness, maintaining high returns even when large fractions of sensors are unavailable.
  arXiv  Detail & Related papers  (2026-03-04T22:21:54Z)
• Acoustic Sensing for Universal Jamming Grippers [42.48594146171026]
  Sound propagates through the gripper and object, encoding object properties, which are then reconstructed via machine learning.<n>Our sensor achieves high spatial resolution in sensing object size (2.6 mm error) and orientation (0.6 deg error)<n>We validate the sensor in a realistic tactile object sorting task, achieving 53 minutes of uninterrupted grasping and sensing.
  arXiv  Detail & Related papers  (2026-02-27T22:31:05Z)
• Sensitivity threshold defines the optimal spin subset for ensemble quantum sensing [2.328370332280478]
  We derive an analytic expression of ensemble sensitivity for inhomogeneous spin sensors.<n>We introduce sensitivity thresholds that reveal the optimal spin subset.<n>Our framework imposes no fundamental trade-offs and extends quantum sensing to heterogeneous sensing environments.
  arXiv  Detail & Related papers  (2025-12-11T11:30:22Z)
• Noradrenergic-inspired gain modulation attenuates the stability gap in joint training [44.99833362998488]
  Studies in continual learning have identified a transient drop in performance on mastered tasks when assimilating new ones, known as the stability gap.<n>We argue that it reflects an imbalance between rapid adaptation and robust retention at task boundaries.<n>Inspired by locus coeruleus mediated noradrenergic bursts, we propose uncertainty-modulated gain dynamics.
  arXiv  Detail & Related papers  (2025-07-18T16:34:06Z)
• Quantum sensing of displacements with stabilized GKP states [41.94295877935867]
  We show how protocols for the stabilization of Gottesman-Kitaev-Preskill states can be used for the estimation of two-quadrature displacement sensing.<n>Thanks to the stabilization, this sensor is backaction evading and can function continuously without reset, making it well suited for the detection of itinerant signals.
  arXiv  Detail & Related papers  (2025-06-25T17:18:50Z)
• Enhancement of sensitivity near exceptional points in dissipative qubit-resonator systems [10.877863413220087]
  We propose and demonstrate a protocol for realizing non-Hermitian quantum sensors for probing the coupling between a qubit and a resonator subjecting to energy dissipations. The excitation-number conversion associated with the no-jump evolution trajectory enables removal of the noisy outcomes with quantum jumps. The sensitivity enhancement near the exceptional point is confirmed by both theoretical calculation and experimental measurement.
  arXiv  Detail & Related papers  (2025-01-27T04:36:57Z)
• Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration [49.03824084306578]
  We propose to incorporate a physical inductive bias into the neural network calibration architecture to enhance the robustness and the trustworthiness of the AI target application. We pave the way for a trustworthy uncertainty representation and for a holistic verification strategy of the perception chain.
  arXiv  Detail & Related papers  (2024-12-18T10:36:46Z)
• Quantum Sensing with Driven-Dissipative Su-Schrieffer-Heeger Lattices [0.0]
  The sensitivity of non-Hermitian systems has been extensively studied and stimulated ideas about developing new types of sensors. In this paper, we examine a chain of parametrically driven resonators governed by the squeezed Su-Schrieffer-Heeger model.
  arXiv  Detail & Related papers  (2024-12-17T19:00:00Z)
• Noisy Stark probes as quantum-enhanced sensors [0.0]
  Wannier-Stark localization has been proven to be a resource for quantum-enhanced sensitivity with super-Heisenberg scaling. We address the issue of decoherence that occurs during evolution and characterize how that affects the sensing performance.
  arXiv  Detail & Related papers  (2024-11-04T18:26:21Z)
• Synthetically enhanced sensitivity using higher-order exceptional point and coherent perfect absorption [1.677791484038981]
  Sensors play a crucial role in advanced apparatuses and it is persistently pursued to improve their sensitivities. Recent singularity of a non-Hermitian system, known as the exceptional point (EP), has drawn much attention for this goal. We realize this synthetically enhanced sensor using a pseudo-Hermitian cavity magnonic system composed of two yttrium iron garnet spheres and a microwave cavity.
  arXiv  Detail & Related papers  (2024-01-03T08:31:18Z)
• Parametrically enhancing sensor sensitivity at an exceptional point [0.0]
  We propose a scheme to enhance the sensitivity of Non-Hermitian optomechanical mass-sensors. The sensitivity of both sensing schemes is greatly improved, yielding to a better performance of the sensor.
  arXiv  Detail & Related papers  (2023-12-08T14:25:18Z)
• How are Prompts Different in Terms of Sensitivity? [50.67313477651395]
  We present a comprehensive prompt analysis based on the sensitivity of a function. We use gradient-based saliency scores to empirically demonstrate how different prompts affect the relevance of input tokens to the output. We introduce sensitivity-aware decoding which incorporates sensitivity estimation as a penalty term in the standard greedy decoding.
  arXiv  Detail & Related papers  (2023-11-13T10:52:01Z)
• Criticality-enhanced Electric Field Gradient Sensor with Single Trapped Ions [0.7499722271664147]
  We propose and analyze a driven-dissipative quantum sensor that is continuously monitored close to a dissipative critical point. The device achieves highly precise sensing of oscillating electric field gradients at a criticality-enhanced precision scaling beyond the standard quantum limit.
  arXiv  Detail & Related papers  (2023-04-04T18:02:05Z)
• On the Relation between Sensitivity and Accuracy in In-context Learning [41.27837171531926]
  In-context learning (ICL) suffers from oversensitivity to the prompt, making it unreliable in real-world scenarios. We study the sensitivity of ICL with respect to multiple perturbation types. We propose textscSenSel, a few-shot selective prediction method that abstains from sensitive predictions.
  arXiv  Detail & Related papers  (2022-09-16T00:52:34Z)
• A background-free optically levitated charge sensor [50.591267188664666]
  We introduce a new technique to model and eliminate dipole moment interactions limiting the performance of sensors employing levitated objects. As a demonstration, this is applied to the search for unknown charges of a magnitude much below that of an electron. As a by-product of the technique, the electromagnetic properties of the levitated objects can also be measured on an individual basis.
  arXiv  Detail & Related papers  (2021-12-20T08:16:28Z)
• ReSkin: versatile, replaceable, lasting tactile skins [28.348982687106883]
  ReSkin is a tactile soft sensor that leverages machine learning and magnetic sensing to offer a low-cost, diverse and compact solution for long-term use. Our self-supervised learning algorithm enables finer performance enhancement with small, inexpensive data collection procedures.
  arXiv  Detail & Related papers  (2021-10-29T20:21:37Z)
• Bayesian Autoencoders for Drift Detection in Industrial Environments [69.93875748095574]
  Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift)
  arXiv  Detail & Related papers  (2021-07-28T10:19:58Z)
• Fundamental Sensitivity Bounds for Quantum Enhanced Optical Resonance Sensors Based on Transmission and Phase Estimation [1.6230648949593154]
  We study optical resonance sensors, which detect a change in a parameter of interest through a resonance shift. We show that the fundamental sensitivity results from an interplay between the QCRB and the transfer function of the system. We also study the effect of losses external to the sensor on the degree of quantum enhancement.
  arXiv  Detail & Related papers  (2021-06-14T20:23:12Z)
• Balancing Robustness and Sensitivity using Feature Contrastive Learning [95.86909855412601]
  Methods that promote robustness can hurt the model's sensitivity to rare or underrepresented patterns. We propose Feature Contrastive Learning (FCL) that encourages a model to be more sensitive to the features that have higher contextual utility.
  arXiv  Detail & Related papers  (2021-05-19T20:53:02Z)
• The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? [57.366931129764815]
  We collect more than 9,000 grasping trials using a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Our experimental results indicate that incorporating tactile readings substantially improve grasping performance.
  arXiv  Detail & Related papers  (2017-10-16T05:32:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.