Software Library

This page contains the software packages developed as part of the project.

OfflineQRL

Hölle, M., Wiedmann, M., Periyasamy, M.

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An offline quantum reinforcement learning framework which includes methods such as BCQQ, CQ2L

Guided-SPSA

Periyasamy, M., Plinge, A., Mutschler, C., D. Scherer, D., Mauerer, W.

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Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule. A novel gradient estimation method for VQCs with fewer circuit estimations

Deep Reinforcement Learning with Variational Quantum Circuits

Franz, M., Wolf, L., Periyasamy, M., Ufrecht, C., Scherer, D. D., Plinge, A., ... & Mauerer, W. (2022)

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Uncovering instabilities in variational-quantum deep q-networks first appeared in Journal of The Franklin Institute.
→ Supplemental Material

Peel | Pile? {Cross}-Framework Portability of Quantum Software

Schönberger, M., Franz, M., Scherzinger, S., & Mauerer, W. (2022, March)

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Peel| pile? Cross-framework portability of quantum software first appeared in 2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C) (pp. 164-169). IEEE.

Ready to Leap (by co-design)? Join Order Optimisation on Quantum Hardware

Schönberger, M., Scherzinger, S., & Mauerer, W. (2023)

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Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware. In SIGMOD'23: proceedings of the 2023 International Conference on Management of Data, June 18–23, 2023, Seattle, WA, USA. ACM.

Quantum-Inspired Digital Annealing for Join Ordering

Schönberger, M., Trummer, I., & Mauerer, W. (2023)

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"Quantum-Inspired Digital Annealing for Join Ordering" has been submitted to VLDB 2024

Industrial Benchmark

Hein, D., Depeweg, S., Tokic, M., Udluft, S., Hentschel, A., Runkler, T. A., & Sterzing, V. (2017)

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A realistic benchmark for quantum variants of online and offline reinforcement learning. It is used to find those quantum reinforcement learning algorithms that are best suited for real-world applications in industrial environments.

Quantum Policy Iteration via Amplitude Estimation and Grover Search – Towards Quantum Advantage for Reinforcement Learning

Wiedemann, S., Hein, D., Udluft, S., and Mendl, C. (2022)

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This is the code companying the above mentioned publication in Transactions on Machine Learning Research (2022).

Quantum Reservoir Computing Surrogate

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Code allowing for the construction and analysis of Quantum Reservoir models. Designed for the aim of simulating time series data from a Chylla-Haase Reactor.

Qneat: Natural Evolution of Variational Quantum Circuit Architecture

Giovagnoli, A., Ma, Y., & Tresp, V. (2023)

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A genetic algorithm for quantum architecture optimization inspired by traditional Neuroevolution of Augmenting Topologies (NEAT). Presented in the Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2023.

Differentiable quantum architecture search for quantum reinforcement learning

Sun, Y., Ma, Y., & Tresp, V. (2023)

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A differentiable quantum architecture search framework for better solving reinforcement learning problems. Detailed in the arXiv preprint arXiv:2309.10392 (2023).

Differentiable quantum architecture search for Job Shop Scheduling Problem (JSSP)

Sun, Y., Liu, J., Ma, Y., & Tresp, V. (2024)

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A differentiable quantum architecture search framework for better solving industry-relevant job scheduling problems. Specific focus on the Job Shop Scheduling Problem (JSSP).