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Quantum Lyapunov control with machine learning

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S. C. Hou, X. X. Yi

Optimization and Control · Submitted 7 Aug 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Since the sample generation and the training of neural networks are carried out in advance, the initial-state-adaptive Lyapunov control can be implemented without much increase of computational resources.

how it is stated · asserted 0.88 read the record →

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arXiv ID1808.02516 (version 1)
Pith ID5XJTDUCK, Pith's permanent record of this paper and its review
CitationS. C. Hou, X. X. Yi. "Quantum Lyapunov control with machine learning." arXiv:1808.02516 (2018). https://pith.science/paper/5XJTDUCK
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What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.31 · human participants 0.02 · controlled comparison 0.12 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.18 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Quantum state engineering is a central task in Lyapunov-based quantum control. Given different initial states, better performance may be achieved if the control parameters, such as the Lyapunov function, are individually optimized for each initial state, however, at the expense of computing resources. To tackle this issue, we propose an initial-state-adaptive Lyapunov control strategy with machine learning, specifically, artificial neural networks trained through supervised learning. Two designs are presented and illustrated where the feedforward neural network and the general regression neural network are used to select control schemes and design Lyapunov functions, respectively. Since the sample generation and the training of neural networks are carried out in advance, the initial-state-adaptive Lyapunov control can be implemented without much increase of computational resources.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Quantum state engineering is a central task in Lyapunov-based quantum control. background 1.00 0.06 0.03 asserted 1.00
s1 Given different initial states, better performance may be achieved if the control parameters, such as the Lyapunov function, are individually optimized for each initial state, however, at the expense of computing resources. background 0.85 0.11 0.06 hedged possibility 1.00
s2 To tackle this issue, we propose an initial-state-adaptive Lyapunov control strategy with machine learning, specifically, artificial neural networks trained through supervised learning. contribution 0.90 0.87 0.20 asserted 1.00
s3 Two designs are presented and illustrated where the feedforward neural network and the general regression neural network are used to select control schemes and design Lyapunov functions, respectively. method 0.82 0.90 0.08 asserted 1.00
s4 Since the sample generation and the training of neural networks are carried out in advance, the initial-state-adaptive Lyapunov control can be implemented without much increase of computational resources. interpretation 0.45 0.71 0.40 asserted 0.88

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.89.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Quantum state engineering is a central task in Lyapunov-based quantum control. Given different initial states, better performance may be achieved if the control parameters, such as the Lyapunov function, are individually optimized for each initial state, however, at the expense of computing resources. To tackle this issue, we propose an initial-state-adaptive Lyapunov control strategy with machine learning, specifically, artificial neural networks trained through supervised learning. Two designs are presented and illustrated where the feedforward neural network and the general regression neural network are used to select control schemes and design Lyapunov functions, respectively. Since the sample generation and the training of neural networks are carried out in advance, the initial-state-adaptive Lyapunov control can be implemented without much increase of computational resources.

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