Quantum-enhanced learning with a controllable bosonic variational sensor network
arXiv:2404.18293 · doi:10.1088/2058-9565/ad752d
Abstract
The emergence of quantum sensor networks has presented opportunities for enhancing complex sensing tasks, while simultaneously introducing significant challenges in designing and analyzing quantum sensing protocols due to the intricate nature of entanglement and physical processes. Supervised learning assisted by an entangled sensor network (SLAEN) [Phys. Rev. X 9, 041023 (2019)] represents a promising paradigm for automating sensor-network design through variational quantum machine learning. However, the original SLAEN, constrained by the Gaussian nature of quantum circuits, is limited to learning linearly separable data. Leveraging the universal quantum control available in cavity-QED experiments, we propose a generalized SLAEN capable of handling nonlinear data classification tasks. We establish a theoretical framework for physical-layer data classification to underpin our approach. Through training quantum probes and measurements, we uncover a threshold phenomenon in classification error across various tasks -- when the energy of probes exceeds a certain threshold, the error drastically diminishes to zero, providing a significant improvement over the Gaussian SLAEN. Despite the non-Gaussian nature of the problem, we offer analytical insights into determining the threshold and residual error in the presence of noise. Our findings carry implications for radio-frequency photonic sensors and microwave dark matter haloscopes.
arXiv comment13 pages, 9 figures
References in corpus (39)
- Gaussian Quantum Information
- Variational Quantum Algorithms
- Encoding a qubit in an oscillator
- General framework for estimating the ultimate precision limit in noisy quantum-enhanced metrology
- Rotation Curves of Spiral Galaxies
- A microchip optomechanical accelerometer
- Quantum advantage in learning from experiments
- A hybrid on-chip opto-nanomechanical transducer for ultra-sensitive force measurements
- A quantum-enhanced search for dark matter axions
- Encoding a qubit in a trapped-ion mechanical oscillator
- Distributed quantum sensing in a continuous variable entangled network
- The dark matter of gravitational lensing
- Cavity Optomechanical Magnetometer
- Distributed Quantum Sensing Using Continuous-Variable Multipartite Entanglement
- Distributed Quantum Metrology and the Entangling Power of Linear Networks
- Searching for Dark Matter with a Superconducting Qubit
- Fast Universal Control of an Oscillator with Weak Dispersive Coupling to a Qubit
- Distributed quantum phase estimation with entangled photons
- Generalization in Quantum Machine Learning: a Quantum Information Perspective
- Learning Quantum Systems
- Demonstration of a Reconfigurable Entangled Radiofrequency-Photonic Sensor Network
- Variational spin-squeezing algorithms on programmable quantum sensors
- Fundamental quantum interferometry bound for the squeezed-light-enhanced gravitational-wave detector GEO600
- Entanglement-Enhanced Optomechanical Sensing
- A variational toolbox for quantum multi-parameter estimation
- Distributed quantum sensing with a mode-entangled network of spin-squeezed atomic states
- Squeezed vacuum used to accelerate the search for a weak classical signal
- Out-of-distribution generalization for learning quantum dynamics
- Scrambling and Complexity in Phase Space
- Entangled sensor-networks for dark-matter searches
- Quantum-enhanced data classification with a variational entangled sensor network
- Generation of optical Gottesman-Kitaev-Preskill states with cavity QED
- Optimal and Variational Multi-Parameter Quantum Metrology and Vector Field Sensing
- Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network
- Ultimate precision limit of noise sensing and dark matter search
- Advances in Bosonic Quantum Error Correction with Gottesman-Kitaev-Preskill Codes: Theory, Engineering and Applications
- Quantum metrology of noisy spreading channels
- Nonlinear Sideband Cooling to a Cat State of Motion
- Quantum Receiver Enhanced by Adaptive Learning