Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data

Dixit, Vivek; Selvarajan, Raja; Aldwairi, Tamer; Koshka, Yaroslav; Novotny, M. A.; Humble, Travis S.; Kais, Sabre

doi:10.1109/tetci.2021.3074916

Cited by 27 publications

(12 citation statements)

References 36 publications

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“…Equation (5) for such a circuit ansatz can be re-expressed in terms of the quantum Fisher information (QFI) and the gradient of Hamiltonian expectation with respect to the current state. In classical probability, the Fisher information characterizes how much a probability distribution varies by changing a parameter that characterizes a distribution.…”

Section: Methodsmentioning

confidence: 99%

“…Computations are performed on quantum states that make use of superposition and entanglement to allow for speedups. Future potential applications include cryptography 1 , search problems 2 , simulation of quantum systems 3 , quantum annealing 4 , machine learning 5 , computation biology 6 , quantum materials 7 , and problems in optimization 8 . Refer 9 for an extensive introduction into the field of quantum computation.…”

Section: Prime Factorization Using Quantum Variational Imaginary Time...mentioning

confidence: 99%

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Prime factorization using quantum variational imaginary time evolution

Selvarajan

Dixit

Cui

et al. 2021

Sci Rep

Self Cite

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The road to computing on quantum devices has been accelerated by the promises that come from using Shor’s algorithm to reduce the complexity of prime factorization. However, this promise hast not yet been realized due to noisy qubits and lack of robust error correction schemes. Here we explore a promising, alternative method for prime factorization that uses well-established techniques from variational imaginary time evolution. We create a Hamiltonian whose ground state encodes the solution to the problem and use variational techniques to evolve a state iteratively towards these prime factors. We show that the number of circuits evaluated in each iteration scales as $$O(n^{5}d)$$ O ( n 5 d ) , where n is the bit-length of the number to be factorized and d is the depth of the circuit. We use a single layer of entangling gates to factorize 36 numbers represented using 7, 8, and 9-qubit Hamiltonians. We also verify the method’s performance by implementing it on the IBMQ Lima hardware to factorize 55, 65, 77 and 91 which are greater than the largest number (21) to have been factorized on IBMQ hardware.

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Section: Methodsmentioning

confidence: 99%

Section: Prime Factorization Using Quantum Variational Imaginary Time...mentioning

confidence: 99%

Prime factorization using quantum variational imaginary time evolution

Selvarajan

Dixit

Cui

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Considering recent advancements in both quantum computing and machine learning, the combination of the two techniques -quantum machine learning -is expected to be a promising application of quantum computer in the near future. Many quantum machine learning algorithms were proposed in the past few years [23,24,25,26,27]. Moreover, researchers have succeeded to apply quantum machine learning algorithms to various systems such as superconducting circuits [28] and photonic systems [29], which leads to enormous enthusiasm applying quantum algorithms into various areas [30,31,31,32,33,34,35].…”

Section: Introductionmentioning

confidence: 99%

Quantum Cluster Algorithm for Data classification

Li¹,

Kais²

2021

Preprint

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We present a quantum algorithm for data classification based on the nearest-neighbor learning algorithm. The classification algorithm is divided into two steps: Firstly, data in the same class is divided into smaller groups with sublabels assisting building boundaries between data with different labels. Secondly we construct a quantum circuit for classification that contains multi control gates. The algorithm is easy to implement and efficient in predicting the labels of test data. To illustrate the power and efficiency of this approach, we construct the phase transition diagram for the metalinsulator transition of V O2, using limited trained experimental data, where V O2 is a typical strongly correlated electron materials, and the metallic-insulating phase transition has drawn much attention in condensed matter physics. Moreover, we demonstrate our algorithm on the classification of randomly generated data and the classification of entanglement for various Werner states, where the training sets can not be divided by a single curve, instead, more than one curves are required to separate them apart perfectly. Our preliminary result shows considerable potential for various classification problems, particularly for constructing different phases in materials.

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“…Computations are performed on quantum states that make use of superposition and entanglement to allow for speedups. Future potential applications include cryptography [1], search problems [2], simulation of quantum systems [3], quantum annealing [4], machine learning [5], computation biology [6], quantum materials [7], and problems in optimization [8]. Refer [9] for an extensive introduction into the field of quantum computation.…”

Section: Introductionmentioning

confidence: 99%

Prime Factorization Using Quantum Variational Imaginary Time Evolution

Selvarajan¹,

Dixit²,

Cui³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The road to computing on quantum devices has been accelerated by the promises that come from using Shor's algorithm to reduce the complexity of prime factorization. However, this promise hast not yet been realized due to noisy qubits and lack of robust error correction schemes. Here we explore a promising, alternative method for prime factorization that uses well-established techniques from variational imaginary time evolution. We create a Hamiltonian whose ground state encodes the solution to the problem and use variational techniques to evolve a state iteratively towards these prime factors. We show that the number of circuits evaluated in each iteration scales as O(n 5 d), where n is the bit-length of the number to be factorized and d is the depth of the circuit. We use a single layer of entangling gates to factorize several numbers represented using 7, 8, and 9-qubit Hamiltonians. We also verify the method's performance by implementing it on the IBMQ Lima hardware. * This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data

Cited by 27 publications

References 36 publications

Prime factorization using quantum variational imaginary time evolution

Prime factorization using quantum variational imaginary time evolution

Quantum Cluster Algorithm for Data classification

Prime Factorization Using Quantum Variational Imaginary Time Evolution

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