Retrieving Quantum Information with Active Learning

Ding, Yongcheng; Martín-Guerrero, José D.; Sanz, Mikel; Magdalena‐Benedito, Rafael; Chen, Xi; Solano, E.

doi:10.1103/physrevlett.124.140504

Cited by 25 publications

(22 citation statements)

References 36 publications

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“…Hence, the perturbed system after weak measurement cannot be analytically calculated by Eq. ( 3) [41]. Here we extend it to the case of the density matrix.…”

Section: A Physical System and Taskmentioning

confidence: 99%

Quantum Stream Learning

Ding¹,

Chen²,

Magdalena‐Benedito³

et al. 2021

Preprint

Self Cite

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The exotic nature of quantum mechanics makes machine learning (ML) be different in the quantum realm compared to classical applications. ML can be used for knowledge discovery using information continuously extracted from a quantum system in a broad range of tasks. The model receives streaming quantum information for learning and decision-making, resulting in instant feedback on the quantum system. As a stream learning approach, we present a deep reinforcement learning on streaming data from a continuously measured qubit at the presence of detuning, dephasing, and relaxation. We also investigate how the agent adapts to another quantum noise pattern by transfer learning. Stream learning provides a better understanding of closed-loop quantum control, which may pave the way for advanced quantum technologies.

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“…Hence, the perturbed system after weak measurement cannot be analytically calculated by Eq. ( 3) [41]. Here we extend it to the case of the density matrix.…”

Section: A Physical System and Taskmentioning

confidence: 99%

Quantum Stream Learning

Ding¹,

Chen²,

Magdalena‐Benedito³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…where V(y i ) denotes the votes from the committee of the size Ṽ for the label y i . Although QBC shows its advantage on performance over USAMP in the previous binary quantum information classification task [39], we focus on USAMP in this work since different query strategies can be investigated by multinomial classification problems in physics with only a single model, saving massive computational resources.…”

Section: Active Learning Theorymentioning

confidence: 99%

“…The key hypothesis of AL is that a model trained on a subset of adequately selected samples to be labeled can achieve a similar performance as the one trained with all samples labeled [37,38]. It is verified that AL achieves an accurate binary classification of quantum information by selecting the quantum states with the maximal information for labeling by measurements [39]. In this paradigm, the cost of labeling is the fidelity loss induced by measurement, which depends on the measurement strength and feedback.…”

Section: Introductionmentioning

confidence: 97%

“…We focus on multinomial cases, where different sampling strategies are no longer equivalent. Starting from a nontrivial extension of the framework proposed in [39], we propose quantum information retrieval in qutrit systems, allowing an experimental implementation with six entangled photons. Another example comes from many-body physics, where AL manages to predict the boundaries of the exotic phases with less than 2% of samples labeled by phase detections.…”

Section: Introductionmentioning

confidence: 99%

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Active Learning for the Optimal Design of Multinomial Classification in Physics

Ding¹,

Martín-Guerrero²,

Song³

et al. 2021

Preprint

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Optimal design for model training is a critical topic in machine learning. Active Learning aims at obtaining improved models by querying samples with maximum uncertainty according to the estimation model for artificially labeling; this has the additional advantage of achieving successful performances with a reduced number of labeled samples. We analyze its capability as an assistant for the design of experiments, extracting maximum information for learning with the minimal cost in fidelity loss, or reducing total operation costs of labeling in the laboratory. We present two typical applications as quantum information retrieval in qutrits and phase boundary prediction in many-body physics. For an equivalent multinomial classification problem, we achieve the correct rate of 99% with less than 2% samples labeled. We reckon that active-learning-inspired physics experiments will remarkably save budget without loss of accuracy.

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“…Algorithmic optimization of quantum systems plays a key role in quantum computing, simulation, and sensing (e.g. see [1][2][3][4][5][6][7][8][9][10]), as well as for quantum system characterization [11][12][13][14]. Yet, there has been little effort on algorithmic optimization of quantum communications and networks [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Sample-efficient adaptive calibration of quantum networks using Bayesian optimization

Cortes¹,

Lefèbvre²,

Lauk³

et al. 2021

Preprint

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Indistinguishable photons are imperative for advanced quantum communication networks. Indistinguishability is difficult to obtain because of environment-induced photon transformations and loss imparted by communication channels, especially in noisy scenarios. Strategies to mitigate these transformations often require hardware or software overhead that is restrictive (e.g. adding noise), infeasible (e.g. on a satellite), or time-consuming for deployed networks. Here we propose and develop resource-efficient Bayesian optimization techniques to rapidly and adaptively calibrate the indistinguishability of individual photons for quantum networks using only information derived from their measurement. To experimentally validate our approach, we demonstrate the optimization of Hong-Ou-Mandel interference between two photons-a central task in quantum networking-finding rapid, efficient, and reliable convergence towards maximal photon indistinguishability in the presence of high loss and shot noise. We expect our resource-optimized and experimentally friendly methodology will allow fast and reliable calibration of indistinguishable quanta, a necessary task in distributed quantum computing, communications, and sensing, as well as for fundamental investigations.

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Retrieving Quantum Information with Active Learning

Cited by 25 publications

References 36 publications

Quantum Stream Learning

Quantum Stream Learning

Active Learning for the Optimal Design of Multinomial Classification in Physics

Sample-efficient adaptive calibration of quantum networks using Bayesian optimization

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