A reinforcement learning approach for quantum state engineering

Mackeprang, Jelena; Dasari, Durga Bhaktavatsala Rao; Wrachtrup, Jörg

doi:10.1007/s42484-020-00016-8

Cited by 48 publications

(25 citation statements)

References 27 publications

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“…While RLNN was introduced more than 20 years ago [11,12], interest in these methods was recently rekindled by its remarkable success for Atari games [13,14]. RLNN and other machine-learning approaches have been successfully applied to a variety of problems in quantum information theory: generating error-correcting sequences [15,16], preparation of special quantum states [17][18][19], setting up experimental Bell tests [20], quantum communication [21], fault-tolerant quantum computation [22], quantum control [23][24][25][26], and nonequilibrium quantum thermodynamics [27]. Additionally, RLNN has been applied in the closely related topic of adaptive quantum metrology [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing

Brandsen

Stubbs

Pfister

2022

Quantum

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Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems, including some problems in quantum information theory. In this work, we apply RLNN to quantum hypothesis testing and determine the optimal measurement strategy for distinguishing between multiple quantum states {ρj} while minimizing the error probability. In the case where the candidate states correspond to a quantum system with many qubit subsystems, implementing the optimal measurement on the entire system is experimentally infeasible. We use RLNN to find locally-adaptive measurement strategies that are experimentally feasible, where only one quantum subsystem is measured in each round. We provide numerical results which demonstrate that RLNN successfully finds the optimal local approach, even for candidate states up to 20 subsystems. We additionally demonstrate that the RLNN strategy meets or exceeds the success probability for a modified locally greedy approach in each random trial.While the use of RLNN is highly successful for designing adaptive local measurement strategies, in general a significant gap can exist between the success probability of the optimal locally-adaptive measurement strategy and the optimal collective measurement. We build on previous work to provide a set of necessary and sufficient conditions for collective protocols to strictly outperform locally adaptive protocols. We also provide a new example which, to our knowledge, is the simplest known state set exhibiting a significant gap between local and collective protocols. This result raises interesting new questions about the gap between theoretically optimal measurement strategies and practically implementable measurement strategies.

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Section: Introductionmentioning

confidence: 99%

Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing

Brandsen

Stubbs

Pfister

2022

Quantum

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“…49,50 Optimization algorithms have been proven to be useful tools in tasks such as detection of qudit states 51 and quantum state engineering. 52,53 Machine learning and genetic algorithms have also found many uses in photonics, 54,55 including the use of generative models, 56 quantum state reconstruction, 57,58 automated design of experimental platforms, [59][60][61] quantum state and gate engineering, 52,53,[62][63][64][65] and the study of Bell nonlocality. [66][67][68] Moreover, genetic algorithms have been employed to design adaptive spatial mode sorters using random scattering processes.…”

Section: Introductionmentioning

confidence: 99%

Dynamical learning of a photonics quantum-state engineering process

Suprano,

Zia,

Polino

et al. 2022

Preprint

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Experimentally engineering high-dimensional quantum states is a crucial task for several quantum information protocols. However, a high degree of precision in the characterization of experimental noisy apparatus is required to apply existing quantum state engineering protocols. This is often lacking in practical scenarios, affecting the quality of the engineered states. Here, we implement experimentally an automated adaptive optimization protocol to engineer photonic Orbital Angular Momentum (OAM) states. The protocol, given a target output state, performs an online estimation of the quality of the currently produced states, relying on output measurement statistics, and determines how to tune the experimental parameters to optimize the state generation. To achieve this, the algorithm needs not be imbued with a description of the generation apparatus itself. Rather, it operates in a fully black-box scenario, making the scheme applicable in a wide variety of circumstances. The handles controlled by the algorithm are the rotation angles of a series of waveplates and can be used to probabilistically generate arbitrary four-dimensional OAM states. We showcase our scheme on different target states both in classical and quantum regimes, and prove its robustness to external perturbations on the control parameters. This approach represents a powerful tool for automated optimizations of noisy experimental tasks for quantum information protocols and technologies.

show abstract

“…49,50 Optimization algorithms have been proven to be useful tools in tasks such as detection of qudit states 51 and quantum state engineering. 52,53 Machine learning and genetic algorithms have also found many uses in photonics, 54,55 including the use of generative models, 56 quantum state reconstruction, 57,58 automated design of experimental platforms, [59][60][61] quantumstate and gate engineering, 52,53,[62][63][64][65] and the study of Bell nonlocality. [66][67][68] Moreover, genetic algorithms have been employed to design adaptive spatial mode sorters using random scattering processes.…”

Section: Introductionmentioning

confidence: 99%

Dynamical learning of a photonics quantum-state engineering process

et al. 2021

View full text Add to dashboard Cite

Experimental engineering of high-dimensional quantum states is a crucial task for several quantum information protocols. However, a high degree of precision in the characterization of the noisy experimental apparatus is required to apply existing quantum-state engineering protocols. This is often lacking in practical scenarios, affecting the quality of the engineered states. We implement, experimentally, an automated adaptive optimization protocol to engineer photonic orbital angular momentum (OAM) states. The protocol, given a target output state, performs an online estimation of the quality of the currently produced states, relying on output measurement statistics, and determines how to tune the experimental parameters to optimize the state generation. To achieve this, the algorithm does not need to be imbued with a description of the generation apparatus itself. Rather, it operates in a fully black-box scenario, making the scheme applicable in a wide variety of circumstances. The handles controlled by the algorithm are the rotation angles of a series of waveplates and can be used to probabilistically generate arbitrary four-dimensional OAM states. We showcase our scheme on different target states both in classical and quantum regimes and prove its robustness to external perturbations on the control parameters. This approach represents a powerful tool for automated optimizations of noisy experimental tasks for quantum information protocols and technologies.

show abstract

A reinforcement learning approach for quantum state engineering

Cited by 48 publications

References 27 publications

Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing

Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing

Dynamical learning of a photonics quantum-state engineering process

Dynamical learning of a photonics quantum-state engineering process

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