Abstract
Nowadays, machine learning techniques are successfully applied to many problems in industrial and academic fields with classical computers. With the introduction of quantum simulators, the idea of using quantum-computing speed to solve these problems has become widespread. Many researchers are experimenting with using quantum circuits in various machine-learning methods to solve different problems. Due to the limited number of qubits, experiments are on simpler problems. In this study, a variational quantum circuit (VQC) was proposed using amplitude encoding to overcome the limited qubit number barrier and use the advantages of quantum computing more efficiently. The proposed amplitude encoding method and VQC were explained. Generalized by exemplifying how they can be applied to different problems. The proposed approach was applied to a navigation problem. The performance of the proposed approach was evaluated with the number of parameters, the number of qubits needed, and the success rate. As a result, the performance of the proposed approach has been verified.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
This article work was carried out within the scope of Niyazi Furkan Bar’s master’s thesis named “Automated Generation of Quantum Computing Models Using Deep Learning.” The supervisor of the thesis is Mehmet Karaköse.
Funding
This study was supported by the TUBITAK (The Scientific and Technological Research Council of Turkey) under Grant No: 121E439.
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Bar, N.F., Yetis, H. & Karakose, M. An efficient and scalable variational quantum circuits approach for deep reinforcement learning. Quantum Inf Process 22, 300 (2023). https://doi.org/10.1007/s11128-023-04051-9
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DOI: https://doi.org/10.1007/s11128-023-04051-9