Swarm optimization for adaptive phase measurements with low visibility

Abstract

Adaptive feedback normally provides the greatest accuracy for optical phase measurements. New advances in nitrogen vacancy centre technology have enabled magnetometry via individual spin measurements, which are similar to optical phase measurements but with low visibility. The adaptive measurements that previously worked well with high-visibility optical interferometry break down and give poor results for nitrogen vacancy centre measurements. We use advanced search techniques based on swarm optimisation to design better adaptive measurements that can provide improved measurement accuracy with low-visibility interferometry, with applications in nitrogen vacancy centre magnetometry.

8 pages, 7 figures, comments welcome

References in corpus (11)

• High-sensitivity diamond magnetometer with nanoscale resolution
• Entanglement-free Heisenberg-limited phase estimation
• Scanning magnetic field microscope with a diamond single-spin sensor
• High sensitivity magnetic imaging using an array of spins in diamond
• Entanglement-enhanced measurement of a completely unknown phase
• Quantum-enhanced optical phase tracking
• Quantum interferometry with three-dimensional geometry
• Nanoscale magnetometry using a single spin system in diamond
• High-dynamic-range imaging of nanoscale magnetic fields using optimal control of a single qubit
• Dual-channel lock-in magnetometer with a single spin in diamond
• Spin bath narrowing with adaptive parameter estimation

Cited by in corpus (15)

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• Optimized quantum sensing with a single electron spin using real-time adaptive measurements
• Adaptive quantum computation in changing environments using projective simulation
• Bayesian estimation for quantum sensing in the absence of single-shot detection
• Adaptive tracking of a time-varying field with a quantum sensor
• Characterization of decohering quantum systems: Machine learning approach
• Feedback Ansatz for Adaptive-Feedback Quantum Metrology Training with Machine Learning
• Simplified algorithms for adaptive experiment design in parameter estimation
• Loss-resistant unambiguous phase measurement
• Adaptive identification of coherent states
• Efficient Bayesian phase estimation using mixed priors
• Sensitivity-enhanced magnetometry using nitrogen-vacancy ensembles via adaptively complete transitions overlapping
• Time-adaptive phase estimation
• Sequential Bayesian experiment design for adaptive Ramsey sequence measurements
• Dose-efficient Quantum Phase Estimation in Lossy Optical Interferometry