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Optical and Quantum Sensing and Precision Metrology

Shot-by-shot estimation of quantum measurement accuracy via quantum Bayesian inference

Sergey V. Polyakov, Ivan A. Burenkov, M. V. Jabir, Fajar R. Annafianto, Abdella Battou

Published Mar 4, 2021• 0 citations
Publisher

Abstract

Ordinarily, quantum-measurement based state discrimination methods use Bayesian inference to determine the maximally likely optical input state out of N possible inputs. Each practical single-shot measurement yields a unique vector of N likelihood values (one per each possible outcome) whose significance is not appreciated. Here we show theoretically and experimentally that measured Bayesian likelihood values are equal to observed probabilities of a successful state discrimination. Thus, the vector of Bayesian likelihoods provides significantly more information about the input state than is available in a typical state discrimination measurement.

DOI
10.1117/12.2578972

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Ordinarily, quantum-measurement based state discrimination methods use Bayesian inference to determine the maximally likely optical input state out of N possible inputs. Each practical single-shot measurement yields a unique vector of N likelihood values (one per each possible outcome) whose significance is not appreciated. Here we show theoretically and experimentally that measured Bayesian likelihood values are equal to observed probabilities of a successful state discrimination. Thus, the vector of Bayesian likelihoods provides significantly more information about the input state than is available in a typical state discrimination measurement.
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We investigate how accuracy information available in quantum measurement can be used to enhance error correction codes using experimentally obtained state identification data. We test conventional and novel error correction protocols and find a significant reduction of errors in user data transmission. The error reduction and the energy use of error correcting codes compare favorably to that of quantum receivers without error correction. Ordinarily, quantum receivers identify the maximally likely input state out of M possible states. We take advantage of the full vector of Bayesian probabilities available for each act of measurement that supplements the conventional state discrimination.
Quantum state vector reconstruction is a fundamental problem in quantum information theory, crucial for tasks like quantum state tomography and quantum error correction. Traditional methods often struggle with noisy measurements and lack a principled framework for uncertainty quantification. This paper introduces a novel algorithm, Quantum State Vector Reconstruction via Bayesian Inference, that leverages Bayesian inference to reconstruct a quantum state vector from a set of measurements, enhancing accuracy and offering a more robust approach compared to existing methods. We detail the core mechanism, emphasizing the integration of Bayesian estimation for uncertainty quantification and refinement of the reconstruction process. The algorithm incorporates a probabilistic approach to estimate the state vector distribution, improving reconstruction by incorporating uncertainty estimates directly into the reconstruction process. This results in a more reliable and accurate state vector reconstruction, particularly when dealing with noisy measurements. This work addresses the limitations of conventional methods by providing a principled Bayesian framework for state vector reconstruction, offering a significant advancement in this field.
This paper investigates the application of Bayesian inference for optimizing quantum measurement schemes. Traditional quantum measurement strategies often rely on heuristic approaches or specific system constraints, potentially leading to suboptimal outcomes. We propose a framework where the quantum measurement process is modeled as a Bayesian inference problem. This allows us to systematically explore the measurement landscape, incorporating prior knowledge and iteratively refining the measurement strategy based on observed outcomes. The core of this approach lies in formulating the measurement problem as a probabilistic model and utilizing Bayesian updating to determine the optimal measurement basis or parameters. We demonstrate, through theoretical analysis and conceptual design, that Bayesian optimization can lead to improved measurement accuracy and efficiency compared to conventional methods. The key benefits include reduced measurement errors, enhanced signal-to-noise ratios, and potentially the ability to extract more information from quantum systems. This work presents a novel methodology for quantum measurement, bridging the gap between classical Bayesian optimization and the unique properties of quantum mechanics.
This paper explores a novel approach to quantum metrology leveraging the principles of entanglement and Bayesian inference. Traditional metrological techniques are frequently hampered by shot noise, fundamentally limiting their precision. This work proposes a methodology that utilizes entangled photon pairs to mitigate this noise, thereby enhancing measurement accuracy. The core innovation lies in the integration of Bayesian inference, a statistical framework, to not only improve the measurement outcome but also to rigorously quantify and manage the associated uncertainties. The theoretical framework presented details the mathematical relationships involved, including the impact of entanglement on signal-to-noise ratios (SNR) and the application of Bayes' theorem for parameter estimation. The combination of these two powerful concepts – quantum entanglement and Bayesian inference – offers a potentially transformative pathway toward achieving unprecedented precision in various measurement applications. The paper concludes with a discussion of potential challenges and future research directions.