Data-Driven Qubit Characterization and Optimal Control using Deep Learning

• URL: http://arxiv.org/abs/2601.18704v1
• Date: Mon, 26 Jan 2026 17:26:20 GMT
• Title: Data-Driven Qubit Characterization and Optimal Control using Deep Learning
• Authors: Paul Surrey, Julian D. Teske, Tobias Hangleiter, Hendrik Bluhm, Pascal Cerfontaine,
• Abstract summary: We propose a machine learning-based protocol to address the challenges of evaluating gradients and modeling complex system dynamics.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Sample-Efficient Reinforcement Learning of Koopman eNMPC [42.72938925647165]
  Reinforcement learning can be used to tune data-driven (economic) nonlinear model predictive controllers ((e)NMPCs) for optimal performance in a specific control task.<n>We combine a model-based RL algorithm with our published method that turns Koopman (e)NMPCs into automatically differentiable policies.
  arXiv  Detail & Related papers  (2025-03-24T15:35:16Z)
• Development of Neural Network-Based Optimal Control Pulse Generator for Quantum Logic Gates Using the GRAPE Algorithm in NMR Quantum Computer [0.0]
  We introduce a neural network to generate optimal control pulses for general single-qubit quantum logic gates.<n>By utilizing a neural network, we can efficiently implement any single-qubit quantum logic gates within a reasonable time scale.
  arXiv  Detail & Related papers  (2024-12-08T08:31:55Z)
• End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control [45.84205238554709]
  We present a method for reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We show that the end-to-end trained models outperform those trained using system identification in (e)NMPC.
  arXiv  Detail & Related papers  (2023-08-03T10:21:53Z)
• Emulation Learning for Neuromimetic Systems [0.0]
  Building on our recent research on neural quantization systems, results on learning quantized motions and resilience to channel dropouts are reported. We propose a general Deep Q Network (DQN) algorithm that can not only learn the trajectory but also exhibit the advantages of resilience to channel dropout.
  arXiv  Detail & Related papers  (2023-05-04T22:47:39Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks [101.59367762974371]
  In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments. It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications. We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
  arXiv  Detail & Related papers  (2022-10-08T04:32:58Z)
• Automatic Evolution of Machine-Learning based Quantum Dynamics with Uncertainty Analysis [4.629634111796585]
  The long short-term memory recurrent neural network (LSTM-RNN) models are used to simulate the long-time quantum dynamics. This work builds an effective machine learning approach to simulate the dynamics evolution of open quantum systems.
  arXiv  Detail & Related papers  (2022-05-07T08:53:55Z)
• Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms [59.03426963238452]
  We present Real-time Neural MPC, a framework to efficiently integrate large, complex neural network architectures as dynamics models within a model-predictive control pipeline. We show the feasibility of our framework on real-world problems by reducing the positional tracking error by up to 82% when compared to state-of-the-art MPC approaches without neural network dynamics.
  arXiv  Detail & Related papers  (2022-03-15T09:38:15Z)
• Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators [0.0]
  We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. We present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. We present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system.
  arXiv  Detail & Related papers  (2021-09-22T15:31:24Z)
• A quantum algorithm for training wide and deep classical neural networks [72.2614468437919]
  We show that conditions amenable to classical trainability via gradient descent coincide with those necessary for efficiently solving quantum linear systems. We numerically demonstrate that the MNIST image dataset satisfies such conditions. We provide empirical evidence for $O(log n)$ training of a convolutional neural network with pooling.
  arXiv  Detail & Related papers  (2021-07-19T23:41:03Z)
• Credit Assignment in Neural Networks through Deep Feedback Control [59.14935871979047]
  Deep Feedback Control (DFC) is a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss-Newton optimization for a wide range of connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing.
  arXiv  Detail & Related papers  (2021-06-15T05:30:17Z)
• Control optimization for parametric hamiltonians by pulse reconstruction [21.723487348914958]
  We propose a method to reduce the computational time required to generate the control pulse for a Hamiltonian. We use simple schemes to accurately reconstruct the control pulses from a set of pulses obtained in advance for a discrete set of predetermined parameter values.
  arXiv  Detail & Related papers  (2021-02-24T14:47:09Z)

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.