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DOE OSTI · 3017786

Efficient online quantum circuit learning with no upfront training

O’Leary, Thomas [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Univ. of Oxford (United Kingdom). Clarendon Laboratory] (ORCID:0009000320651695)·Czarnik, Piotr [Jagiellonian Univ., Krakow (Poland)]·Pelofske, Elijah Autumn Rain [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000000032673796X)·Sornborger, Andrew Tyler [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Quantum Science Center (QSC)] (ORCID:0000000180366624)·McKerns, Michael [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); The Uncertainty Quantification Foundation, Wilmington, DE (United States)] (ORCID:0000000183423778)·Cincio, Lukasz [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Quantum Science Center (QSC)] (ORCID:0000000267584376)
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Abstract

Optimization is a promising candidate for studying the utility of variational quantum algorithms (VQAs). However, evaluating cost functions using quantum hardware introduces runtime overheads that limit exploration. Surrogate-based methods can reduce calls to a quantum computer, yet existing approaches require hyperparameter pre-training and have been tested only on small problems. Here, we show that surrogate-based methods can enable successful optimization at scale, without pre-training, by using radial basis function interpolation (RBF) to construct an adaptive, hyperparameter-free surrogate. Using the surrogate as an acquisition function drives hardware queries to the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut instances with the Quantum Approximate Optimization Algorithm (QAOA), our method outperforms state-of-the-art approaches, without considering their upfront training costs. Furthermore, we successfully optimize QAOA circuits for 127-qubit random Ising models on an IBM processor using 10 4 −10 5 measurements. Strong empirical performance demonstrates the promise of automated surrogate-based learning for large-scale VQA applications.

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97 MATHEMATICS AND COMPUTING99 GENERAL AND MISCELLANEOUSQAOAQuantum AlgorithmsQuantum Approximate Optimization AlgorithmQuantum ComputingSurrogate OptimizationSurrogate Optimization AlgorithmsVariational Quantum Algorithms

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O’Leary, Thomas [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Univ. of Oxford (United Kingdom). Clarendon Laboratory] (ORCID:0009000320651695), Czarnik, Piotr [Jagiellonian Univ., Krakow (Poland)], Pelofske, Elijah Autumn Rain [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000000032673796X), Sornborger, Andrew Tyler [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Quantum Science Center (QSC)] (ORCID:0000000180366624), McKerns, Michael [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); The Uncertainty Quantification Foundation, Wilmington, DE (United States)] (ORCID:0000000183423778), Cincio, Lukasz [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Quantum Science Center (QSC)] (ORCID:0000000267584376). 2025-11-20. Efficient online quantum circuit learning with no upfront training. https://doi.org/10.1038/s42005-025-02423-4

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