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20172026
most citedMachine Learning for Estimation and Control of Quantum Systems

11 citations · 18 across the 13 of their papers we have counts for

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12 papers · 1 filter

quant-ph2026

Optimal filtering for a giant cavity in waveguide QED systems

Guangpu Wu, Shibei Xue, Yuting Zhu +2

In waveguide quantum electrodynamics (QED) systems, a giant cavity can be engineered to interact with quantum fields by multiple distant coupling points so that its non-Markovian d…

quant-ph2025

Tractable Infinite-Horizon Stochastic Model Predictive Control for Quantum Filtering via Eigenstate Reduction

Yunyan Lee, Ian R. Petersen, Daoyi Dong

Model predictive control has shown potential to enhance the robustness of quantum control systems. In this work, we propose a tractable Stochastic Model Predictive Control (SMPC) f…

quant-ph2025

Generalized collective quantum tomography: algorithm design, optimization, and validation

Shuixin Xiao, Yuanlong Wang, Zhibo Hou +6

Quantum tomography is a fundamental technique for characterizing, benchmarking, and verifying quantum states and devices. It plays a crucial role in advancing quantum technologies…

quant-ph2025

Unified formalism and adaptive algorithms for optimal quantum state, detector and process tomography

Shuixin Xiao, Xiangyu Wang, Yuanlong Wang +8

Quantum tomography is a standard technique for characterizing, benchmarking and verifying quantum systems/devices and plays a vital role in advancing quantum technology and underst…

quant-ph2025

Time-Optimal Control of Finite Dimensional Open Quantum Systems via a Model Predictive Strategy

Yunyan Lee, Ian R. Petersen, Daoyi Dong

To mitigate dissipative effects from environmental interactions and efficiently stabilize quantum states, time-optimal control has emerged as an effective strategy for open quantum…

quant-ph2025

Robust Variational Ground-State Solvers via Dissipative Quantum Feedback Models

Yunyan Lee, Ian R. Petersen, Daoyi Dong

We propose a variational framework for solving ground-state problems of open quantum systems governed by quantum stochastic differential equations (QSDEs). This formulation natural…

quant-ph2025★ 11 cited

Machine Learning for Estimation and Control of Quantum Systems

Hailan Ma, Bo Qi, Ian R. Petersen +3

The development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation, and sensing.…

quant-ph2025

Simultaneous estimations of quantum state and detector through multiple quantum processes

Shuixin Xiao, Weichao Liang, Yuanlong Wang +3

The estimation of all the parameters in an unknown quantum state or measurement device, commonly known as quantum state tomography (QST) and quantum detector tomography (QDT), is c…

quant-ph2024

Arbitrary State Transition of Open Qubit System Based on Switching Control

Guangpu Wu, Shibei Xue, Shan Ma +3

We present a switching control strategy based on Lyapunov control for arbitrary state transitions in open qubit systems. With coherent vector representation, we propose a switching…

quant-ph2024

A two-stage solution to quantum process tomography: error analysis and optimal design

Shuixin Xiao, Yuanlong Wang, Jun Zhang +4

Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage soluti…

quant-ph2024

Robust Quantum Control via a Model Predictive Control Strategy

Yunyan Lee, Ian R. Petersen, Daoyi Dong

This article presents a robust control strategy using Time-Optimal Model Predictive Control (TOMPC) for a two-level quantum system subject to bounded uncertainties. In this method,…

quant-ph2023★ 7 cited

Quantum autoencoders using mixed reference states

Hailan Ma, Gary J. Mooney, Ian R. Petersen +2

One of the fundamental tasks in quantum information theory is quantum data compression, which can be realized via quantum autoencoders that first compress quantum states to low-dim…