Quantum circuit representation of Bayesian networks

Borujeni, Sima E.; Nannapaneni, Saideep; Nguyen, Nam; Behrman, Elizabeth; Steck, James E.

doi:10.1016/j.eswa.2021.114768

Cited by 41 publications

(47 citation statements)

References 37 publications

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“…To verify that the result is correct, we proceed to design the equivalent Bayesian network to the digital quantum twin [ 40 ] represented in Figure 6 . The error percentage found when comparing both quantum digital twin and the classical Bayesian network is less than 2%, which is acceptable in the context of quantum simulations.…”

Section: Resultsmentioning

confidence: 99%

“…The quantum digital twin circuit that resembles the sensor network in the computer numerical control machine of Figure 1 is shown in Figure 4 . Following the recommendations from [ 40 , 55 ], we build the quantum digital twin with a number of qubits equal to the number of sensors and one ancilla qubit that serves to perform the appropriate rotations, giving a total of 6 qubits in this case. In addition, as we have previously indicated in [ 41 ], after the proper qubit initialization, we perform a series of qubit rotation operations that allow us to simulate the conditional probabilities between the respective sensors.…”

Section: Case Study Quantum Jidokamentioning

confidence: 99%

“…However, even this approach faces limitations due to the computational power required and leads to delays in feedback to the worker [ 38 ]. Quantum simulations shows relevant advantages to accelerate this feedback significantly [ 39 , 40 ]. Hence the interest in studying the possibilities of quantum computing to implement the described JIDOKA (自働化) methodology more efficiently and responsively.…”

Section: Introductionmentioning

confidence: 99%

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Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization

Villalba-Díez

Gutiérrez

Grijalvo

et al. 2021

Sensors

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With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes.

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Section: Resultsmentioning

confidence: 99%

Section: Case Study Quantum Jidokamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization

Villalba-Díez

Gutiérrez

Grijalvo

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…BN, otherwise known as belief or bayes network is a probabilistic graphical model that represents variable sets whose conditional probability relationship are represented as a directed acyclic graph with nodes and edges [119,120]. BNs have been explored in varieties of SCADA security studies.…”

Section: Bayesian Network (Bn)mentioning

confidence: 99%

“…Likewise, the scenarios have the effect of tripping circuit breakers. Despite the numerous successes achieved using BN for SCADA security, Borujeni et al [119] argued that their implementations involve huge computational requirements, especially in fields, such as modern SCADA/ICS systems as there are vast number of nodes involved. BN are known to perform poorly with high dimensional datasets.…”

Section: Bayesian Network (Bn)mentioning

confidence: 99%

A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

et al. 2021

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Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.

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