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arXiv:2601.18704

Data-Driven Qubit Characterization and Optimal Control using Deep Learning

Paul Surrey, Julian D. Teske, Tobias Hangleiter, Hendrik Bluhm, Pascal Cerfontaine

quant-phcs.LGSubmitted Jan 26, 2026• Announced Jan 27, 2026• 0 citations
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Abstract

Quantum computing requires the optimization of control pulses to achieve high-fidelity quantum gates. We propose a machine learning-based protocol to address the challenges of evaluating gradients and modeling complex system dynamics. By training a recurrent neural network (RNN) to predict qubit behavior, our approach enables efficient gradient-based pulse optimization without the need for a detailed system model. First, we sample qubit dynamics using random control pulses with weak prior assumptions. We then train the RNN on the system's observed responses, and use the trained model to optimize high-fidelity control pulses. We demonstrate the effectiveness of this approach through simulations on a single $ST_0$ qubit.

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•Sep 3, 2026•0 citations
Quantum computing requires the optimization of control pulses to achieve high-fidelity quantum gates. We propose a machine learning-based protocol to address the challenges of evaluating gradients and modeling complex system dynamics. By training a recurrent neural network (RNN) to predict qubit behavior, our approach enables efficient gradient-based pulse optimization without the need for a detailed system model. First, we sample qubit dynamics using random control pulses with weak prior assumptions. We then train the RNN on the system's observed responses, and use the trained model to optimize high-fidelity control pulses. We demonstrate the effectiveness of this approach through simulations on a single ST0ST_0ST0​ qubit.
Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations on a superconducting transmon qubit, using detailed numerical simulations. We demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of about 99.99%.
We apply a graybox machine-learning framework to model and control a qubit undergoing Markovian and non-Markovian dynamics from environmental noise. The approach combines physics-informed equations with a lightweight transformer neural network based on the self-attention mechanism. The model is trained on simulated data and learns an effective operator that predicts observables accurately, even in the presence of memory effects. We benchmark both non-Gaussian random-telegraph noise and Gaussian Ornstein-Uhlenbeck noise and achieve low prediction errors even in challenging noise coupling regimes. Using the model as a dynamics emulator, we perform gradient-based optimal control to identify pulse sequences implementing a universal set of single-qubit gates, achieving fidelities above 99% for the lowest considered value of the coupling and remaining above 90% for the highest.
Abstract In this paper, we propose a novel method using deep neural networks (DNNs) to optimize the parameters of pulse waveforms used for manipulating qubit states, resulting in the high-fidelity implementation of qubit gates. High-fidelity quantum logic gates are crucial for scaling up current quantum computers. The proposed approach uses DNNs to model the functional relationship between amplitudes of pulse waveforms used in scheduling and the corresponding fidelities. The DNNs are trained using a dataset of amplitude and corresponding fidelities obtained through simulations of qubit gates in Qiskit. A two-stage approach is used with the trained DNNs to obtain amplitudes that yield the highest fidelity. The proposed method is evaluated by estimating the amplitude for pulse scheduling of single (Hadamard and Pauli-X) and two-qubit gates (CNOT). The results clearly indicate that the method can achieve high-fidelity implementations of single-qubit gates with fidelities of 0.999976 and 0.999923 for Hadamard and Pauli-X gates, respectively. For the CNOT gate, the best fidelity obtained is 0.695313. This can be attributed to the effects of entanglement and the need for the phase parameter to be accounted for within the predictive model.
Machine learning (ML) is a promising approach for performing challenging quantum-information tasks such as device characterization, calibration, and control.ML models can train directly on the data produced by a quantum device while remaining agnostic to the quantum nature of the learning task.However, these generic models lack physical interpretability and usually require large datasets in order to learn accurately.Here we incorporate features of quantum mechanics in the design of our ML approach to characterize the dynamics of a quantum device and learn device parameters.This physics-inspired approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data obtained from continuous weak measurement of a driven superconducting transmon qubit.This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task, thus laying the groundwork for more scalable characterization techniques.
Neural networks have emerged as a powerful way to approach many practical problems in quantum physics. In this work, we illustrate the power of deep learning to predict the dynamics of a quantum many-body system, where the training is based purely on monitoring expectation values of observables under random driving . The trained recurrent network is able to produce accurate predictions for driving trajectories entirely different than those observed during training. As a proof of principle, here we train the network on numerical data generated from spin models, showing that it can learn the dynamics of observables of interest without needing information about the full quantum state. This allows our approach to be applied eventually to actual experimental data generated from a quantum many-body system that might be open, noisy, or disordered, without any need for a detailed understanding of the system. This scheme provides considerable speedup for rapid explorations and pulse optimization. Remarkably, we show the network is able to extrapolate the dynamics to times longer than those it has been trained on, as well as to the infinite-system-size limit.
This paper presents a novel approach to quantum control optimization utilizing deep learning techniques. Traditional quantum control optimization methods often rely on complex analytical calculations or computationally intensive numerical simulations, limiting their applicability to complex quantum systems. This work proposes a framework where a deep learning model is trained to directly learn optimal control pulses for a given quantum system. The model, trained on simulated quantum dynamics, effectively learns the mapping between system states and control parameters. This automated approach offers the potential to significantly accelerate the process of quantum control design and potentially enable control of systems beyond the reach of conventional optimization methods. The core claim of this work is the automation of quantum control through deep learning. The central mechanism involves the construction of a deep learning model to simulate quantum behavior and learn optimal control pulses. The resulting system demonstrates a promising path towards efficient and adaptive quantum control.