Year: 2023
Type: article
Abstract: The detection of entanglement provides a definitive proof of quantumness. Its ascertainment might be challenging for hot or macroscopic objects, where entanglement is typically weak, but nevertheless present. Here we propose a platform for measuring entanglement by connecting the objects of interest to an uncontrolled quantum network, whose emission (readout) is processed to recognize the state of the former, and hence also the amount of entanglement. First, we demonstrate the platform and its features with generic quantum systems. As the network effectively learns to recognize quantum states, it is possible to sense the amount of entanglement after training with only nonentangled states. Furthermore, by taking into account measurement errors, we demonstrate entanglement sensing with precision that scales beyond the standard quantum limit and outperforms measurements performed directly on the objects. Finally, we utilize our platform for sensing gravity-induced entanglement between two masses and predict an improvement of two orders of magnitude in the precision of entanglement estimation compared to existing techniques.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G154481267, G2460800214, G2967200528, EPSRC-SFI: Non-Equilibrium Steady-States of Quantum many-body systems: uncovering universality and thermodynamics (QuamNESS), G3843968587, EPSRC-SFI: Non-Equilibrium Steady-States of Quantum many-body systems: uncovering universality and thermodynamics (QuamNESS), G434337789, G4667608353, G4870751369, G4921042621, G5437716650, G693734671, G7105070590, Testing the Large-Scale Limit of Quantum Mechanics, G8717077664
OpenAlex ID: W4367049890