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Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics

Robustness of learning-assisted adaptive quantum-enhanced metrology in the presence of noise

Pantita Palittapongarnpim, Péter Wittek, Barry C. Sanders

Published Oct 1, 2017• 5 citations
Publisher

Abstract

Reinforcement learning algorithms have been shown to generate procedures for executing quantum control tasks to desired performances. Although reinforcement learning has been effective, its robustness in generating quantum control procedures under general noise condition needs to be tested. Here we consider adaptive quantum-enhanced interferometric phase estimation as a case study in quantum feedback control. The algorithm is determined to deliver a robust quantum-metrological procedure under various phase-noise models when the imprecision surpasses the standard quantum limit. The robustness against unknown environmental variations positions the reinforcement learning as a practical approach for devising quantum control procedures.

DOI
10.1109/smc.2017.8122618

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Reinforcement learning algorithms have been shown to generate procedures for executing quantum control tasks to desired performances. Although reinforcement learning has been effective, its robustness in generating quantum control procedures under general noise condition needs to be tested. Here we consider adaptive quantum-enhanced interferometric phase estimation as a case study in quantum feedback control. The algorithm is determined to deliver a robust quantum-metrological procedure under various phase-noise models when the imprecision surpasses the standard quantum limit. The robustness against unknown environmental variations positions the reinforcement learning as a practical approach for devising quantum control procedures.
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We develop a reinforcement-learning algorithm to construct a feedback policy that delivers quantum-enhanced interferometric-phase estimation up to 100 photons in a noisy environment. We ensure scalability of the calculations by distributing the workload in a cluster and by vectorizing time-critical operations. We also improve running time by introducing accept-reject criteria to terminate calculation when a successful result is reached. Furthermore, we make the learning algorithm robust to noise by fine-tuning how the objective function is evaluated. The results show the importance and relevance of well-designed classical machine learning algorithms in quantum physics problems.
We aim to construct tests for evaluating whether policies for adaptive quantum-enhanced metrology are robust against unknown phase noise and are tractable to construct and to execute. Specifically, one of our tests determines scaling of phase-estimate precision with respect to photon number; the other two tests concern resource complexity with respect to the training time required to construct the policy and the execution time for the policy so constructed. The robustness test is performed on quantum-enhanced adaptive phase estimation by simulating the scheme under four phase-noise models corresponding to normal-distribution noise, random-telegraph noise, skew-normal-distribution noise, and log-normal-distribution noise. Control policies are devised either by an evolutionary algorithm under the same noisy conditions, albeit ignorant of its properties, or a Bayesian-based feedback method that assumes no noise. We have introduced an approach to evaluating quantum-control policies for metrology that relies on testing against unusual phase-noise models and accepting that the policies are robust only if scaling of phase-estimate precision beats the standard quantum limit and policy-design resource complexity, in the presence of phase noise, is polynomial in the number of photons.
This paper investigates the application of reinforcement learning (RL) for adaptive quantum control. Traditional quantum control methods often rely on pre-designed pulse shapes optimized for specific scenarios, lacking adaptability to variations in system parameters or environmental noise. This work proposes a novel framework where an RL agent learns to dynamically adjust quantum control pulses in real-time, maximizing the fidelity of quantum operations. The agent receives feedback from the quantum system, typically measured through state variables, and utilizes this information to optimize the control pulse sequence. This adaptive approach addresses the limitations of static control schemes and promises enhanced robustness and performance in complex quantum systems. The core claim centers on utilizing reinforcement learning to design optimal quantum control pulses. The central mechanism involves a trained controller dynamically adjusting pulses based on system feedback. The presented methodology offers a new approach to quantum control, particularly relevant in noisy environments and systems with parameter variations. The research demonstrates the potential of RL for achieving superior quantum control outcomes compared to conventional techniques. ---
This paper presents a novel feedback learning algorithm for adaptive quantum control. The algorithm leverages reinforcement learning principles combined with quantum control theory to dynamically adjust control strategies based on the observed state and environment of a quantum system. The core mechanism involves constructing an adaptive quantum control feedback learning algorithm that enables autonomous optimization of the quantum system. The algorithm utilizes a state representation of the quantum system, a reward function reflecting the desired outcome, and a control policy updated through a reinforcement learning process. Mathematical formulations are provided to describe the system dynamics, the control policy, and the learning process. The proposed algorithm aims to overcome the limitations of traditional fixed-parameter quantum control methods by adapting to uncertainties and complex dynamics, ultimately leading to improved control performance and robustness. The theoretical framework presented here offers a foundation for developing advanced quantum control strategies in various applications, including quantum computation, quantum sensing, and quantum metrology.