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KÖKEN, Özlem Salehi

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Özlem Salehi

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KÖKEN
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    A computer science-oriented approach to introduce quantum computing to a new audience
    (IEEE, 2022-02) Salehi, Özlem; Seskir, Z.; Tepe, I.; Computer Science; KÖKEN, Özlem Salehi
    Contribution: In this study, an alternative educational approach for introducing quantum computing to a wider audience is highlighted. The proposed methodology considers quantum computing as a generalized probability theory rather than a field emanating from physics and utilizes quantum programming as an educational tool to reinforce the learning process. Background: Quantum computing is a topic mainly rooted in physics, and it has been gaining rapid popularity in recent years. A need for extending the educational reach to groups outside of physics has also been becoming a necessity. Intended Outcomes: This study aims to inform academics and organizations interested in introducing quantum computing to a diverse group of participants on an educational approach. It is intended that the proposed methodology would facilitate people from diverse backgrounds to enter the field. Application Design: The introductory quantum physics content is bypassed and the quantum computing concepts are introduced through linear algebra instead. Quantum programming tasks are prepared in line with the content. Pre/post-test design method and Likert scale satisfaction surveys are utilized to measure knowledge acquisition and to evaluate the perception of the learning process by the participants. Findings: Conducted pre/post-test design survey shows that there is a statistically significant increase in the basic knowledge levels of the participants on quantum computing concepts. Furthermore, no significant difference in the gain scores is observed between the participants from different STEM-related educational backgrounds. The majority of the participants were satisfied and provided positive feedback.
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    Language inference with multi-head automata through reinforcement learning
    (IEEE, 2020) Şekerci, Alper; Köken, Özlem Salehi; Computer Science; KÖKEN, Özlem Salehi; Şekerci, Alper
    The purpose of this paper is to use reinforcement learning to model learning agents which can recognize formal languages. Agents are modeled as simple multi-head automaton, a new model of finite automaton that uses multiple heads, and six different languages are formulated as reinforcement learning problems. Two different algorithms are used for optimization. First algorithm is Q-learning which trains gated recurrent units to learn optimal policies. The second one is genetic algorithm which searches for the optimal solution by using evolution-inspired operations. The results show that genetic algorithm performs better than Q-learning algorithm in general but Q-learning algorithm finds solutions faster for regular languages.
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    Implementing quantum finite automata algorithms on noisy devices
    (Springer, 2021) Birkan, U.; Köken, Özlem Salehi; Olejar, V.; Nurlu, C.; Yakaryılmaz, A.; Computer Science; KÖKEN, Özlem Salehi
    Quantum finite automata (QFAs) literature offers an alternative mathematical model for studying quantum systems with finite memory. As a superiority of quantum computing, QFAs have been shown exponentially more succinct on certain problems such as recognizing the language MODp={aj∣j≡0modp} with bounded error, where p is a prime number. In this paper we present improved circuit based implementations for QFA algorithms recognizing the MODp problem using the Qiskit framework. We focus on the case p= 11 and provide a 3 qubit implementation for the MOD11 problem reducing the total number of required gates using alternative approaches. We run the circuits on real IBM quantum devices but due to the limitation of the real quantum devices in the NISQ era, the results are heavily affected by the noise. This limitation reveals once again the need for algorithms using less amount of resources. Consequently, we consider an alternative 3 qubit implementation which works better in practice and obtain promising results even for the problem MOD31.