Anomaly detection in reconstructed quantum states using a machine-learning technique

Hara, Satoshi; Ono, Takafumi; Okamoto, Ryo; Washio, Takashi; Takeuchi, Shigeki

doi:10.1103/physreva.89.022104

Cited by 11 publications

(9 citation statements)

References 12 publications

(21 reference statements)

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“…We applied the proposed method to anomaly detection in quantum systems (Hara et al, 2014(Hara et al, , 2016. In quantum informatics, we sometimes face unknown errors in the given quantum state.…”

Section: Application To Anomaly Detection In Quantum Systemsmentioning

confidence: 99%

“…In order to confirm the performance of the proposed method, we experimentally obtained various density matrices of qubits. In the experiment, we used a two-photon polarization entangled state for the "normal state" (Hara et al, 2014). For the erroneous states, we prepared several quantum states where the amplitude of the off-diagonal elements of the density matrices slightly vary from the normal state.…”

Section: B Quantum Data: Experimental Setupmentioning

confidence: 99%

“…, where H and V represent horizontally and vertically polarized photons, respectively, and a and b denote spatial modes (Hara et al, 2014(Hara et al, , 2016. The measurement outcome of different 16 measurement bases, to each of which approximately 1,000 photon pairs contributed, is converted into density matrix using the conventional QST method (James et al, 2001).…”

Section: B Quantum Data: Experimental Setupmentioning

confidence: 99%

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Consistent Nonparametric Different-Feature Selection via the Sparsest $k$-Subgraph Problem

Hara,

Katsuki,

Yanagisawa

et al. 2017

Preprint

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Two-sample feature selection is the problem of finding features that describe a difference between two probability distributions, which is a ubiquitous problem in both scientific and engineering studies. However, existing methods have limited applicability because of their restrictive assumptions on data distributoins or computational difficulty. In this paper, we resolve these difficulties by formulating the problem as a sparsest k-subgraph problem. The proposed method is nonparametric and does not assume any specific parametric models on the data distributions. We show that the proposed method is computationally efficient and does not require any extra computation for model selection. Moreover, we prove that the proposed method provides a consistent estimator of features under mild conditions. Our experimental results show that the proposed method outperforms the current method with regard to both accuracy and computation time.

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Section: Application To Anomaly Detection In Quantum Systemsmentioning

confidence: 99%

Section: B Quantum Data: Experimental Setupmentioning

confidence: 99%

Section: B Quantum Data: Experimental Setupmentioning

confidence: 99%

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Consistent Nonparametric Different-Feature Selection via the Sparsest $k$-Subgraph Problem

Hara,

Katsuki,

Yanagisawa

et al. 2017

Preprint

Self Cite

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“…Since machine learning algorithms have proved successful for anomaly detection in classical data, a natural question arises if there exist quantum machine algorithms used for detecting anomalies in quantum systems. While classical anomaly detection techniques for quantum states can be used, they are only possible by first probing the classical descriptions of these states which require state tomography, requiring a large number of measurements [14,15]. Thus, it would also be advantageous to reduce these resource overheads by using a quantum algorithm.…”

Section: Introductionmentioning

confidence: 99%

Quantum machine learning for quantum anomaly detection

2018

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Anomaly detection is used for identifying data that deviate from 'normal' data patterns. Its usage on classical data finds diverse applications in many important areas like fraud detection, medical diagnoses, data cleaning and surveillance. With the advent of quantum technologies, anomaly detection of quantum data, in the form of quantum states, may become an important component of quantum applications. Machine learning algorithms are playing pivotal roles in anomaly detection using classical data. Two widely-used algorithms are kernel principal component analysis and oneclass support vector machine. We find corresponding quantum algorithms to detect anomalies in quantum states. We show that these two quantum algorithms can be performed using resources logarithmic in the dimensionality of quantum states. For pure quantum states, these resources can also be logarithmic in the number of quantum states used for training the machine learning algorithm. This makes these algorithms potentially applicable to big quantum data applications. * Electronic address: nana.liu@quantumlah.org † Electronic address: patrick@xanadu.ai arXiv:1710.07405v1 [quant-ph]

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“…Existing research works for anomaly ensembles either combine intermediate outcomes(e.g. network characteristic values) from all constituent detectors [10][11][12][13], or induce diversity among their detectors to increase the chance that they make independent errors [14][15][16][17]. However, as mentioned in Ref.…”

mentioning

confidence: 99%

Anomaly Detection of Complex Networks Based on Intuitionistic Fuzzy Set Ensemble

Wang¹,

Liu²,

Zhang³

et al. 2018

Chinese Phys. Lett.

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Ensemble learning for anomaly detection of data structured into complex network has been barely studied due to the inconsistent performance of complex network characteristics and lack of inherent objective function. In this paper, we propose the IFSAD, a new two-phase ensemble method for anomaly detection based on intuitionistic fuzzy set, and applies it to the abnormal behavior detection problem in temporal complex networks. First, it constructs the intuitionistic fuzzy set of single network characteristic which quantifies the degree of membership, non-membership and hesitation of each of network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose an Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistent of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally we carried out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.

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Anomaly detection in reconstructed quantum states using a machine-learning technique

Cited by 11 publications

References 12 publications

Consistent Nonparametric Different-Feature Selection via the Sparsest $k$-Subgraph Problem

Consistent Nonparametric Different-Feature Selection via the Sparsest $k$-Subgraph Problem

Quantum machine learning for quantum anomaly detection

Anomaly Detection of Complex Networks Based on Intuitionistic Fuzzy Set Ensemble

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