Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Bulletin of the American Physical Society

Machine learning approaches to predictive qubit-state estimation under dephasing

Riddhi Swaroop Gupta, Michael J. Biercuk

Published Mar 7, 2018• 0 citations
OpenAlex

Related papers

Machine Learning for Predictive Estimation of Qubit Dynamics Subject to Dephasing

Riddhi Swaroop Gupta, Michael J. Biercuk

Combating decoherence---the randomization of qubit values in a physical system---is a key challenge in quantum computing. Control theory provides a powerful set of tools to stabilize classical systems, but much work remains to bring its full weight to the quantum domain. Aiming to track and predict qubit-state evolution under various forms of decoherence, the authors employ machine-learning algorithms to optimize qubit-state forecasting, and adapt classical algorithms to work directly with discrete, single-shot qubit measurements. These results suggest that exciting opportunities exist for implementing real-time feedback using purely classical, hardware-agnostic techniques.

arXiv:1712.01291•Physical Review Applied•Jun 27, 2018•21 citations

Prediction and Real-Time Compensation of Qubit Decoherence Via Machine Learning (Open Access, Publisher's Version)

Sandeep Mavadia, Virginia Frey, Jarrah Sastrawan +2 more

•Jan 16, 2017•0 citations

Disentanglement of Qubit in Dephasing Model

Yang Yang, Qing Qing, Ming Ming +2 more

理论物理通讯：英文版•Jan 1, 2009•0 citations

Robotic control and machine learning for the characterization and control of qubits

Riddhi Swaroop Gupta

Decoherence remains a major challenge in Near term, Intermediate scale Noisy Quantum (NISQ) computers. This thesis proposes techniques for characterizing classical noise correlations and performance variations in NISQ devices using single-qubit projective measurements. The central tasks of qubit state estimation and prediction are considered for measurements that are either temporally or spatially correlated. Firstly, this thesis focuses on timeseries prediction for single-qubit measurement outcomes. Focusing on repeated Ramsey measurements performed on a single qubit subject to temporally correlated dephasing, the key challenge is to predict qubit state dynamics by learning temporal noise correlations in a real-time stream of incoming measurements. Autoregressive or Fourier-based Kalman Filtering (KF) protocols are investigated for maximizing the forward prediction horizon and autoregressive approaches demonstrate superior predictive capabilities. Secondly, this thesis investigates the utility of nonlinear stochastic methods to characterize quantum systems using spatially correlated single-qubit projective measurements, via particle filters. A novel likelihood function is presented and incorporated into conventional particle filters. An adaptive procedure, `NMQA', is proposed to characterize a spatially inhomogeneous dephasing field in 2D by allocating measurements on a multi-qubit array. NMQA outperforms brute-force mapping in simulations and using experimental data. Finally, spatial prediction in 2D is compared with bivariate interpolation on a geometric arrangement of points known as the Padua points. By measuring a spatially inhomogeneous dephasing field on dedicated `sensor qubits', the objective is to predict the value of the field on unmeasured, proximal `data qubits'. The number and arrangement of sensor qubits relative to a fixed lattice of data qubits is investigated, providing insights for a general purpose predictive mapping protocol.

•Jan 1, 2020•0 citations

A New Approach for Quantum Phase Estimation Based Algorithms for Machine Learning

Oumayma Ouedrhiri, Oumayma Banouar, Salah El Hadaj +1 more

Lecture notes in networks and systems•Jan 1, 2024•2 citations

Direct Fidelity Estimation of Quantum States Using Machine Learning

Xiaoqian Zhang, Maolin Luo, Zhaodi Wen +4 more

In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. In this Letter, we propose a new approach solving this problem using machine-learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with ±1% precision in a nonadversarial scenario. This machine-learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.

arXiv:2102.02369•Physical Review Letters•Sep 24, 2021•47 citations
Combating decoherence---the randomization of qubit values in a physical system---is a key challenge in quantum computing. Control theory provides a powerful set of tools to stabilize classical systems, but much work remains to bring its full weight to the quantum domain. Aiming to track and predict qubit-state evolution under various forms of decoherence, the authors employ machine-learning algorithms to optimize qubit-state forecasting, and adapt classical algorithms to work directly with discrete, single-shot qubit measurements. These results suggest that exciting opportunities exist for implementing real-time feedback using purely classical, hardware-agnostic techniques.
Decoherence remains a major challenge in Near term, Intermediate scale Noisy Quantum (NISQ) computers. This thesis proposes techniques for characterizing classical noise correlations and performance variations in NISQ devices using single-qubit projective measurements. The central tasks of qubit state estimation and prediction are considered for measurements that are either temporally or spatially correlated. Firstly, this thesis focuses on timeseries prediction for single-qubit measurement outcomes. Focusing on repeated Ramsey measurements performed on a single qubit subject to temporally correlated dephasing, the key challenge is to predict qubit state dynamics by learning temporal noise correlations in a real-time stream of incoming measurements. Autoregressive or Fourier-based Kalman Filtering (KF) protocols are investigated for maximizing the forward prediction horizon and autoregressive approaches demonstrate superior predictive capabilities. Secondly, this thesis investigates the utility of nonlinear stochastic methods to characterize quantum systems using spatially correlated single-qubit projective measurements, via particle filters. A novel likelihood function is presented and incorporated into conventional particle filters. An adaptive procedure, `NMQA', is proposed to characterize a spatially inhomogeneous dephasing field in 2D by allocating measurements on a multi-qubit array. NMQA outperforms brute-force mapping in simulations and using experimental data. Finally, spatial prediction in 2D is compared with bivariate interpolation on a geometric arrangement of points known as the Padua points. By measuring a spatially inhomogeneous dephasing field on dedicated `sensor qubits', the objective is to predict the value of the field on unmeasured, proximal `data qubits'. The number and arrangement of sensor qubits relative to a fixed lattice of data qubits is investigated, providing insights for a general purpose predictive mapping protocol.
In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. In this Letter, we propose a new approach solving this problem using machine-learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with ±1% precision in a nonadversarial scenario. This machine-learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.