Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization

• URL: http://arxiv.org/abs/2607.00284v1
• Date: Wed, 01 Jul 2026 00:17:40 GMT
• Title: Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization
• Abstract summary: We present an active learning framework based on Bayesian optimization with a Gaussian Process surrogate to find the optimal parameter set.<n>The addition of active learning accelerates the discovery of the optimal parameter set with speed and final fidelity dependent on the quantum projection noise of the data.
• Score: 2.0939612492993476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The fidelity of a quantum gate is sensitive to small deviations in the physical control parameters. Unfortunately, it is generally difficult to exactly model the implemented Hamiltonian for a set of user-defined parameters, necessitating on-device calibration. Here, we present an active learning framework based on Bayesian optimization with a Gaussian Process surrogate to find the optimal parameter set. We validate the technique through numerical calibration of the laser amplitude and frequencies that implement the trapped-ion Mølmer Sørensen gate. We show that a Gaussian process can model the Hamiltonian dynamics. The addition of active learning accelerates the discovery of the optimal parameter set with speed and final fidelity dependent on the quantum projection noise of the data. These results establish the utility of active learning and surrogate models for quantum calibration and control.

Related papers

• Optimizing Hyperparameters for Quantum Data Re-Uploaders in Calorimetric Particle Identification [11.099632666738177]
  We present an application of a single-qubit Data Re-Uploading (QRU) quantum model for particle classification in calorimetric experiments.<n>This model requires minimal qubits while delivering strong classification performance.
  arXiv  Detail & Related papers  (2024-12-16T23:10:00Z)
• Automatic pulse-level calibration by tracking observables using iterative learning [3.7845912794054337]
  In quantum computing, gate errors due to inaccurate models can be efficiently polished if the control is limited to a few parameters. We propose an automated model-based framework for calibrating quantum optimal controls called Learning Iteratively for Feasible Tracking (LIFT) LIFT achieves high-fidelity controls despite parasitic model discrepancies by precisely tracking feasible trajectories of quantum observables.
  arXiv  Detail & Related papers  (2023-04-24T15:22:24Z)
• Sample-efficient Model-based Reinforcement Learning for Quantum Control [0.2999888908665658]
  We propose a model-based reinforcement learning (RL) approach for noisy time-dependent gate optimization. We show an order of magnitude advantage in the sample complexity of our method over standard model-free RL. Our algorithm is well suited for controlling partially characterised one and two qubit systems.
  arXiv  Detail & Related papers  (2023-04-19T15:05:19Z)
• Sharp Calibrated Gaussian Processes [58.94710279601622]
  State-of-the-art approaches for designing calibrated models rely on inflating the Gaussian process posterior variance. We present a calibration approach that generates predictive quantiles using a computation inspired by the vanilla Gaussian process posterior variance. Our approach is shown to yield a calibrated model under reasonable assumptions.
  arXiv  Detail & Related papers  (2023-02-23T12:17:36Z)
• Support Vector Machine for Determining Euler Angles in an Inertial Navigation System [55.41644538483948]
  The paper discusses the improvement of the accuracy of an inertial navigation system created on the basis of MEMS sensors using machine learning (ML) methods. The proposed algorithm based on MO has demonstrated its ability to correctly classify in the presence of noise typical for MEMS sensors.
  arXiv  Detail & Related papers  (2022-12-07T10:01:11Z)
• Fast Quantum Calibration using Bayesian Optimization with State Parameter Estimator for Non-Markovian Environment [11.710177724383954]
  We propose a real-time optimal estimator of qubit states, which utilizes weak measurements and Bayesian optimization to find the optimal control pulses for gate design. Our numerical results demonstrate a significant reduction in the calibration process, obtaining a high gate fidelity.
  arXiv  Detail & Related papers  (2022-05-25T17:31:15Z)
• Analytical and experimental study of center line miscalibrations in M\o lmer-S\o rensen gates [51.93099889384597]
  We study a systematic perturbative expansion in miscalibrated parameters of the Molmer-Sorensen entangling gate. We compute the gate evolution operator which allows us to obtain relevant key properties. We verify the predictions from our model by benchmarking them against measurements in a trapped-ion quantum processor.
  arXiv  Detail & Related papers  (2021-12-10T10:56:16Z)
• Experimental Bayesian calibration of trapped ion entangling operations [48.43720700248091]
  We develop and characterize an efficient calibration protocol to automatically estimate and adjust experimental parameters of the widely used Molmer-Sorensen entangling gate operation. We experimentally demonstrate a median gate infidelity of $1.3(1)cdot10-3$, requiring only $1200pm500$ experimental cycles, while completing the entire gate calibration procedure in less than one minute.
  arXiv  Detail & Related papers  (2021-12-02T16:59:00Z)
• Accurate methods for the analysis of strong-drive effects in parametric gates [94.70553167084388]
  We show how to efficiently extract gate parameters using exact numerics and a perturbative analytical approach. We identify optimal regimes of operation for different types of gates including $i$SWAP, controlled-Z, and CNOT.
  arXiv  Detail & Related papers  (2021-07-06T02:02:54Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Hybrid quantum-classical approach to enhanced quantum metrology [13.1056933958821]
  We introduce adjustable controls into the encoding process and then utilize a hybrid quantum-classical approach to automatically optimize the controls online. We report the first experimental demonstration of this promising scheme for the task of finding optimal probes for frequency estimation on a nuclear magnetic resonance (NMR) processor.
  arXiv  Detail & Related papers  (2020-08-14T17:05:03Z)
• Optimal non-classical correlations of light with a levitated nano-sphere [34.82692226532414]
  Nonclassical correlations provide a resource for many applications in quantum technology. Optomechanical systems can generate nonclassical correlations between the mechanical mode and a mode of travelling light. We propose automated optimization of the production of quantum correlations in such a system.
  arXiv  Detail & Related papers  (2020-06-26T15:27:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.