Variational quantum algorithm for enhanced continuous variable optical phase sensing

• URL: http://arxiv.org/abs/2312.13870v1
• Date: Thu, 21 Dec 2023 14:11:05 GMT
• Title: Variational quantum algorithm for enhanced continuous variable optical phase sensing
• Authors: Jens A. H. Nielsen, Mateusz Kicinski, Tummas N. Arge, Kannan Vijayadharan, Jonathan Foldager, Johannes Borregaard, Johannes Jakob Meyer, Jonas S. Neergaard-Nielsen, Tobias Gehring and Ulrik L. Andersen
• Abstract summary: Variational quantum algorithms (VQAs) are hybrid quantum-classical approaches used for tackling a wide range of problems on noisy quantum devices. We implement a variational algorithm designed for optimized parameter estimation on a continuous variable platform based on squeezed light.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational quantum algorithms (VQAs) are hybrid quantum-classical approaches used for tackling a wide range of problems on noisy intermediate-scale quantum (NISQ) devices. Testing these algorithms on relevant hardware is crucial to investigate the effect of noise and imperfections and to assess their practical value. Here, we implement a variational algorithm designed for optimized parameter estimation on a continuous variable platform based on squeezed light, a key component for high-precision optical phase estimation. We investigate the ability of the algorithm to identify the optimal metrology process, including the optimization of the probe state and measurement strategy for small-angle optical phase sensing. Two different optimization strategies are employed, the first being a gradient descent optimizer using Gaussian parameter shift rules to estimate the gradient of the cost function directly from the measurements. The second strategy involves a gradient-free Bayesian optimizer, fine-tuning the system using the same cost function and trained on the data acquired through the gradient-dependent algorithm. We find that both algorithms can steer the experiment towards the optimal metrology process. However, they find minima not predicted by our theoretical model, demonstrating the strength of variational algorithms in modelling complex noise environments, a non-trivial task.

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