© 2026 Aqora Quantum S.A.S.

Terms Privacy Legal Notice

arXiv:1812.01032 · Quantum Science and Technology

Designing quantum experiments with a genetic algorithm

Rosanna Nichols, Lana Mineh, Jesús Rubio, Jonathan C. F. Matthews, Paul Knott

Published Oct 1, 2019• 43 citations

Abstract

Abstract We introduce a genetic algorithm that designs quantum optics experiments for engineering quantum states with specific properties. Our algorithm is powerful and flexible, and can easily be modified to find methods of engineering states for a range of applications. Here we focus on quantum metrology. First, we consider the noise-free case, and use the algorithm to find quantum states with a large quantum Fisher information (QFI). We find methods, which only involve experimental elements that are available with current or near-future technology, for engineering quantum states with up to a 100 fold improvement over the best classical state, and a 20 fold improvement over the optimal Gaussian state. Such states are a superposition of the vacuum with a large number of photons (around 80), and can hence be seen as Schrödinger-cat-like states. We then apply the two most dominant noise sources in our setting—photon loss and imperfect heralding—and use the algorithm to find quantum states that still improve over the optimal Gaussian state with realistic levels of noise. This will open up experimental and technological work in using exotic non-Gaussian states for quantum-enhanced phase measurements. Finally, we use the Bayesian mean square error to look beyond the regime of validity of the QFI, finding quantum states with precision enhancements over the alternatives even when the experiment operates in the regime of limited data.

DOI: 10.1088/2058-9565/ab4d89

Related papers

A hybrid machine learning algorithm for designing quantum experiments

L. O’Driscoll, R. Nichols, Paul A. Knott

Abstract We introduce a hybrid machine learning algorithm for designing quantum optics experiments to produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrödinger cat states and cubic phase states, all to a fidelity of over 96%. Here, we specifically focus on designing realistic experiments, and hence all of the algorithm’s designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search, we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon number distributions.

Quantum Machine Intelligence•Mar 27, 2019•58 citations

A hybrid machine-learning algorithm for designing quantum experiments

L. O'Driscoll, Nichols, R., Paul A. Knott

We introduce a hybrid machine-learning algorithm for designing quantum optics experiments that produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrödinger cat states and cubic phase states, all to a fidelity of over $96\%$. Here we specifically focus on designing realistic experiments, and hence all of the algorithm's designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon-number distributions.

arXiv:1812.03183•Dec 7, 2018•1 citation

Towards practical quantum metrology

Rosanna Nichols

This thesis aims to help in bridging the gap between the ideals of theoretical quantum metrology and its practical application. We take three major approaches to achieving this aim: to investigate the effect of noise on metrological schemes, to study the role of resources and to devise ways to engineer effective probe states from experimentally feasible elements. We find that noise is detrimental to measurement precision but that this may be overcome by thinking outside of the asymptotic regime. Considering resources, whilst entanglement may provide an advantage, it is only useful in limited circumstances and even in this case a very small entangled state may perform as well as a large one. This is also true in multiparameter metrology, where we find that it is possible for the simultaneous estimation of phase and noise to be advantageous, even when the parameters are not fully compatible. We discuss strategies for multiparameter estimation in both discrete and continuous variable formalisms. In the continuous variable formalism, we go beyond the consideration of entanglement as a resource and construct a generalised resource theory for Gaussian states and operations which has particular cases in entanglement, steerability and squeezing. We provide a measure for this resource as well as a no-go theorem for its distillation. Finally, we use a genetic algorithm to devise state engineering schemes to produce highly sensitive states for quantum metrology.

•Jul 18, 2019•0 citations

A search algorithm for quantum state engineering and metrology

P A Knott

In this paper we present a search algorithm that finds useful optical quantum states which can be created with current technology. We apply the algorithm to the field of quantum metrology with the goal of finding states that can measure a phase shift to a high precision. Our algorithm efficiently produces a number of novel solutions: we find experimentally ready schemes to produce states that show significant improvements over the state-of-the-art, and can measure with a precision that beats the shot noise limit by over a factor of 4. Furthermore, these states demonstrate a robustness to moderate/high photon losses, and we present a conceptually simple measurement scheme that saturates the Cramér–Rao bound.

arXiv:1511.05327•New Journal of Physics•Jul 15, 2016•53 citations

A search algorithm for quantum state engineering and metrology

Paul A. Knott

In this paper we present a search algorithm that finds useful optical quantum states which can be created with current technology. We apply the algorithm to the field of quantum metrology with the goal of finding states that can measure a phase shift to a high precision. Our algorithm efficiently produces a number of novel solutions: we find experimentally-ready schemes to produce states that show significant improvements over the state-of-the-art, and can measure with a precision that beats the shot noise limit by over a factor of 4. Furthermore, these states demonstrate a robustness to moderate/high photon losses, and we present a conceptually simple measurement scheme that saturates the Cramer-Rao bound.

•Jul 15, 2016•22 citations

Quantum metrology with a continuous-variable system

Matteo Fadel, Noah Roux, Manuel Gessner

As one of the main pillars of quantum technologies, quantum metrology aims to improve measurement precision using techniques from quantum information. The two main strategies to achieve this are the preparation of nonclassical states and the design of optimized measurement observables. We discuss precision limits and optimal strategies in quantum metrology and sensing with a single mode of quantum continuous variables. We focus on the practically most relevant cases of estimating displacements and rotations and provide the sensitivities of the most important classes of states that includes Gaussian states and superpositions of Fock states or coherent states. Fundamental precision limits that are obtained from the quantum Fisher information are compared to the precision of a simple moment-based estimation strategy based on the data obtained from possibly sub-optimal measurement observables, including homodyne, photon number, parity and higher moments. Finally, we summarize some of the main experimental achievements and present emerging platforms for continuous-variable sensing. These results are of particular interest for experiments with quantum light, trapped ions, mechanical oscillators, and microwave resonators.

arXiv:2411.04122•Reports on Progress in Physics•Aug 29, 2025•17 citations

Abstract We introduce a genetic algorithm that designs quantum optics experiments for engineering quantum states with specific properties. Our algorithm is powerful and flexible, and can easily be modified to find methods of engineering states for a range of applications. Here we focus on quantum metrology. First, we consider the noise-free case, and use the algorithm to find quantum states with a large quantum Fisher information (QFI). We find methods, which only involve experimental elements that are available with current or near-future technology, for engineering quantum states with up to a 100 fold improvement over the best classical state, and a 20 fold improvement over the optimal Gaussian state. Such states are a superposition of the vacuum with a large number of photons (around 80), and can hence be seen as Schrödinger-cat-like states. We then apply the two most dominant noise sources in our setting—photon loss and imperfect heralding—and use the algorithm to find quantum states that still improve over the optimal Gaussian state with realistic levels of noise. This will open up experimental and technological work in using exotic non-Gaussian states for quantum-enhanced phase measurements. Finally, we use the Bayesian mean square error to look beyond the regime of validity of the QFI, finding quantum states with precision enhancements over the alternatives even when the experiment operates in the regime of limited data.

Abstract We introduce a hybrid machine learning algorithm for designing quantum optics experiments to produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrödinger cat states and cubic phase states, all to a fidelity of over 96%. Here, we specifically focus on designing realistic experiments, and hence all of the algorithm’s designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search, we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon number distributions.

We introduce a hybrid machine-learning algorithm for designing quantum optics experiments that produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrödinger cat states and cubic phase states, all to a fidelity of over

96\%

. Here we specifically focus on designing realistic experiments, and hence all of the algorithm's designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon-number distributions.

This thesis aims to help in bridging the gap between the ideals of theoretical quantum metrology and its practical application. We take three major approaches to achieving this aim: to investigate the effect of noise on metrological schemes, to study the role of resources and to devise ways to engineer effective probe states from experimentally feasible elements. We find that noise is detrimental to measurement precision but that this may be overcome by thinking outside of the asymptotic regime. Considering resources, whilst entanglement may provide an advantage, it is only useful in limited circumstances and even in this case a very small entangled state may perform as well as a large one. This is also true in multiparameter metrology, where we find that it is possible for the simultaneous estimation of phase and noise to be advantageous, even when the parameters are not fully compatible. We discuss strategies for multiparameter estimation in both discrete and continuous variable formalisms. In the continuous variable formalism, we go beyond the consideration of entanglement as a resource and construct a generalised resource theory for Gaussian states and operations which has particular cases in entanglement, steerability and squeezing. We provide a measure for this resource as well as a no-go theorem for its distillation. Finally, we use a genetic algorithm to devise state engineering schemes to produce highly sensitive states for quantum metrology.

In this paper we present a search algorithm that finds useful optical quantum states which can be created with current technology. We apply the algorithm to the field of quantum metrology with the goal of finding states that can measure a phase shift to a high precision. Our algorithm efficiently produces a number of novel solutions: we find experimentally ready schemes to produce states that show significant improvements over the state-of-the-art, and can measure with a precision that beats the shot noise limit by over a factor of 4. Furthermore, these states demonstrate a robustness to moderate/high photon losses, and we present a conceptually simple measurement scheme that saturates the Cramér–Rao bound.

In this paper we present a search algorithm that finds useful optical quantum states which can be created with current technology. We apply the algorithm to the field of quantum metrology with the goal of finding states that can measure a phase shift to a high precision. Our algorithm efficiently produces a number of novel solutions: we find experimentally-ready schemes to produce states that show significant improvements over the state-of-the-art, and can measure with a precision that beats the shot noise limit by over a factor of 4. Furthermore, these states demonstrate a robustness to moderate/high photon losses, and we present a conceptually simple measurement scheme that saturates the Cramer-Rao bound.

As one of the main pillars of quantum technologies, quantum metrology aims to improve measurement precision using techniques from quantum information. The two main strategies to achieve this are the preparation of nonclassical states and the design of optimized measurement observables. We discuss precision limits and optimal strategies in quantum metrology and sensing with a single mode of quantum continuous variables. We focus on the practically most relevant cases of estimating displacements and rotations and provide the sensitivities of the most important classes of states that includes Gaussian states and superpositions of Fock states or coherent states. Fundamental precision limits that are obtained from the quantum Fisher information are compared to the precision of a simple moment-based estimation strategy based on the data obtained from possibly sub-optimal measurement observables, including homodyne, photon number, parity and higher moments. Finally, we summarize some of the main experimental achievements and present emerging platforms for continuous-variable sensing. These results are of particular interest for experiments with quantum light, trapped ions, mechanical oscillators, and microwave resonators.