More Like this

1. Low-Fidelity Gradient Updates for High-Fidelity Reprogrammable Iterative Learning Control

   https://doi.org/10.23919/ACC53348.2022.9867601  

   Tseng, Kuan-Yu; Shamma, Jeff S.; Dullerud, Geir E. (June 2022, IEEE)
2. Benchmarking and Fidelity Response Theory of High-Fidelity Rydberg Entangling Gates

   https://doi.org/10.1103/PRXQuantum.6.010331  

   Tsai, Richard Bing-Shiun; Sun, Xiangkai; Shaw, Adam L; Finkelstein, Ran; Endres, Manuel (February 2025, PRX Quantum)

   The fidelity of entangling operations is a key figure of merit in quantum information processing, especially in the context of quantum error correction. High-fidelity entangling gates in neutral atoms have seen remarkable advancement recently. A full understanding of error sources and their respective contributions to gate infidelity will enable the prediction of fundamental limits on quantum gates in neutral atom platforms with realistic experimental constraints. In this work, we implement the time-optimal Rydberg controlled-Z (CZ) gate, design a circuit to benchmark its fidelity, and achieve a fidelity, averaged over symmetric input states, of $0.9971\left(5\right)$, downward corrected for leakage error, which together with our recent work [Nature 634, 321–327 (2024)] forms a new state of the art for neutral atoms. The remaining infidelity is explained by an error model, consistent with our experimental results over a range of gate speeds, with varying contributions from different error sources. Further, we develop a fidelity response theory to efficiently predict infidelity from laser noise with nontrivial power spectral densities and derive scaling laws of infidelity with gate speed. Besides its capability of predicting gate fidelity, we also utilize the fidelity response theory to compare and optimize gate protocols, to learn laser frequency noise, and to study the noise response for quantum simulation tasks. Finally, we predict that a CZ gate fidelity of $\gtrsim 0.999$is feasible with realistic experimental upgrades. Published by the American Physical Society2025 

   more » « less
3. Multi-fidelity Bayesian Optimization with Multiple Information Sources of Input-dependent Fidelity

   Fan, Mingzhou; Yoon, Byung-Jun; Dougherty, Edward; Urban, Nathan; Alexander, Francis; Arróyave, Raymundo; Qian, Xiaoning (April 2024, UAI 2024 Conference)

   By querying approximate surrogate models of different fidelity as available information sources, Multi-Fidelity Bayesian Optimization (MFBO) aims at optimizing unknown functions that are costly or infeasible to evaluate. Existing MFBO methods often assume that approximate surrogates have consistently high or low fidelity across the input domain. However, approximate evaluations from the same surrogate can have different fidelity at different input regions due to data availability and model constraints, especially when considering machine learning surrogates. In this work, we investigate MFBO when multi-fidelity approximations have input-dependent fidelity. By explicitly capturing input dependency for multi-fidelity queries in a Gaussian Process (GP), our new input-dependent MFBO (iMFBO) with learnable noise models better captures the fidelity of each information source in an intuitive way. We further design a new acquisition function for iMFBO and prove that the queries selected by iMFBO have higher quality than those by naive MFBO methods, with a derived sub-linear regret bound. Experiments on both synthetic and real-world data demonstrate its superior empirical performance. 

   more » « less
4. Multi-fidelity Bayesian Optimization with Multiple Information Sources of Input-dependent Fidelity

   Fan, Mingzhou; Yoon, Byung-Jun; Dougherty, Edward R; Urban, Nathan; Alexander, Francis J; Arroyave, Raymundo; Qian, Xiaoning (July 2024, International Conference on Uncertainty in Artificial Intelligence, 2024)
5. Multi-fidelity Bayesian Optimization with Multiple Information Sources of Input-dependent Fidelity

   Fan, Mingzhou; Yoon, Byung-Jun; Dougherty, Edward; Urban, Nathan; Alexander, Francis; Arroyave, Raymundo; Qian, Xiaoning (July 2024, Proceedings of Machine Learning Research)