D5.5 Implementation of QRL algorithm on real architecture
| Autor: | Marshall, Simon, Chatterjee, Yagnik, Rančić, Marko J., Dunjko, Vedran |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| ISSN: | 8362-2837 |
| DOI: | 10.5281/zenodo.8108460 |
| Popis: | This report is the second deliverable of Task 5.2 - QRL for the inventory management part of the NEASQC project. It describes how we successfully deployed a QRL agent on a real quantum computer to tackle a simplified version of the challenge of inventory management. We describe the setup that solved this problem and examines some interesting conclusions drawn from comparing the real device noise to simulators. We begin by introducing the reader to quantum reinforcement learning as developed within the NEASQC project, and to the inventory management problem we apply our learning algorithm to in Section 2. Section 3 highlights the key insights we gained from our experiments, particularly the importance of noise while training on a simulator and the relative unimportance of the specific noise model. Section 4 goes into the specifics of our methods and results, going into detail, both about how we achieved this learning problem and the conclusions we draw from our experiments. {"references":["[JER21] Jerbi, S., Gyurik, C., Marshall, S., Briegel, H., & Dunjko, V. (2021). Parametrized Quantum Policies for Reinforcement Learning. Advances in Neural Information Processing Systems (pp. 28362-28375). Curran Associates, Inc.","[ORO17] Oroojlooyjadid, A., Nazari, M., Snyder, L. V., & Takác, M. (2017). A deep q-network for the beer game with partial information. CoRR, abs/1708.05924.","[CHE20] Chen, S. Y. C., Yang, C. H. H., Qi, J., Chen, P. Y., Ma, X., & Goan, H. S. (2020). Variational quantum circuits for deep reinforcement learning. IEEE Access, 8, 141007-141024.","[SKO22] Skolik, A., Jerbi, S., & Dunjko, V. (2022). Quantum agents in the gym: a variational quantum algorithm for deep q-learning. Quantum, 6, 720.","[LOC20] Lockwood, O., & Si, M. (2020, October). Reinforcement learning with quantum variational circuit. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 16, No. 1, pp. 245-251)."]} |
| Databáze: | OpenAIRE |
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