Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Frontiers in Optics

Efficient Algorithm for Optimizing Adaptive Quantum Metrology

Barry C. Sanders, Alexander Hentschel

Published Jan 1, 2011• 0 citations
Publisher

Abstract

We introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures to replace what is otherwise an inefficient problem. Our algorithm can be trained with simulated or real-world trials.
DOI
10.1364/fio.2011.fths6

Related papers

An efficient algorithm for optimizing adaptive quantum metrology processes

Barry C. Sanders, Alexander Hentschel

We introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures to replace what is otherwise an difficult and inefficient problem. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.

•Aug 1, 2011•5 citations

An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

Barry C. Sanders, Alexander Hentschel

We introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures to replace what is otherwise an difficult and inefficient problem. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.

•Jan 1, 2011•2 citations

Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

Alexander Hentschel, Barry C. Sanders

Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.

arXiv:1104.3844•Physical Review Letters•Nov 30, 2011•99 citations

Adaptive Quantum Measurement via Swarm-intelligence Machine Learning

Barry C. Sanders, Alexander Hentschel

We construct an algorithm that learns through trial-and-error training how to devise optimal feedback-based single-shot phase estimation in interferometry. Our algorithm is robust against experimental imperfections, losses and decoherence.

Research in Optical Sciences•Jan 1, 2012•0 citations

Differential Evolution for Many-Particle Adaptive Quantum Metrology

Neil B. Lovett, Cécile Crosnier de Bellaistre, Martí Perarnau-Llobet +1 more

We devise powerful algorithms based on differential evolution for adaptive many-particle quantum metrology. Our new approach delivers adaptive quantum metrology policies for feedback control that are orders-of-magnitude more efficient and surpass the few-dozen-particle limitation arising in methods based on particle-swarm optimization. We apply our method to the binary-decision-tree model for quantum-enhanced phase estimation as well as to a new problem: a decision tree for adaptive estimation of the unknown bias of a quantum coin in a quantum walk and show how this latter case can be realized experimentally.

arXiv:1304.2246•Physical Review Letters•May 28, 2013•71 citations

Feedback ansatz for adaptive-feedback quantum metrology training with machine learning

Yi Peng, Heng Fan

It is challenging to construct metrology schemes which harness quantum features such as entanglement and coherence to surpass the standard quantum limit. We propose an ansatz for devising an adaptive-feedback quantum metrology (AFQM) strategy which greatly reduces the searching space. Combined with the Markovian feedback assumption, the computational complexity for designing AFQM would be reduced from ${N}^{7}$ to ${N}^{4}$, for $N$ probing systems. The feedback scheme devising via machine learning such as particle-swarm optimization and differential evolution would thus require much less time and produce equally good imprecision scaling. We have thus devised an AFQM for the 207-partite system. The imprecision scaling would persist for $N>207$ in an admirable range when the parameter setting for the 207-partite system is employed without further training. Our ansatz indicates an built-in resilience of the feedback strategy against qubit loss. The feedback strategies designed for the noiseless scenarios have been tested against the qubit loss noise and the phase fluctuation noise. Our numerical result confirms great resilience of the feedback strategies against the two kinds of noise.

arXiv:1910.03181•Physical Review A•Feb 14, 2020•19 citations
Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.
We devise powerful algorithms based on differential evolution for adaptive many-particle quantum metrology. Our new approach delivers adaptive quantum metrology policies for feedback control that are orders-of-magnitude more efficient and surpass the few-dozen-particle limitation arising in methods based on particle-swarm optimization. We apply our method to the binary-decision-tree model for quantum-enhanced phase estimation as well as to a new problem: a decision tree for adaptive estimation of the unknown bias of a quantum coin in a quantum walk and show how this latter case can be realized experimentally.
It is challenging to construct metrology schemes which harness quantum features such as entanglement and coherence to surpass the standard quantum limit. We propose an ansatz for devising an adaptive-feedback quantum metrology (AFQM) strategy which greatly reduces the searching space. Combined with the Markovian feedback assumption, the computational complexity for designing AFQM would be reduced from N7{N}^{7}N7 to N4{N}^{4}N4, for NNN probing systems. The feedback scheme devising via machine learning such as particle-swarm optimization and differential evolution would thus require much less time and produce equally good imprecision scaling. We have thus devised an AFQM for the 207-partite system. The imprecision scaling would persist for N>207N>207N>207 in an admirable range when the parameter setting for the 207-partite system is employed without further training. Our ansatz indicates an built-in resilience of the feedback strategy against qubit loss. The feedback strategies designed for the noiseless scenarios have been tested against the qubit loss noise and the phase fluctuation noise. Our numerical result confirms great resilience of the feedback strategies against the two kinds of noise.