Reinforcement studying with realized devices to sort out arduous quantum issues on actual {hardware}

Shor, P. W. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum laptop. SIAM Rev. 41, 303–332 (1999).

Google Scholar 

Grover, L. Okay. A quick quantum mechanical algorithm for database search. In Proceedings of the twenty-eighth annual ACM Symposium on Theory of Computing, 212–219 (1996).

Monz, T. et al. Realization of a scalable Shor algorithm. Science 351, 1068–1070 (2016).

Google Scholar 

Mandviwalla, A., Ohshiro, Okay. & Ji, B. Implementing Grover’s algorithm on the IBM quantum computer systems. In 2018 IEEE International Conference on Big Data (Big Data), 2531–2537 (IEEE, 2018).

Grimsley, H. R., Economou, S. E., Barnes, E. & Mayhall, N. J. An adaptive variational algorithm for precise molecular simulations on a quantum laptop. Nat. Commun. 10, 3007 (2019).

Google Scholar 

Tang, H. L. et al. qubit-adapt-vqe: an adaptive algorithm for developing hardware-efficient ansätze on a quantum processor. PRX Quantum 2, 020310 (2021).

Google Scholar 

Feniou, C. et al. Overlap-adapt-vqe: sensible quantum chemistry on quantum computer systems by way of overlap-guided compact ansätze. Commun. Phys. 6, 192 (2023).

Google Scholar 

Zhou, L., Wang, S.-T., Choi, S., Pichler, H. & Lukin, M. D. Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term gadgets. Phys. Rev. X 10, 021067 (2020).

Google Scholar 

Zhu, L. et al. Adaptive quantum approximate optimization algorithm for fixing combinatorial issues on a quantum laptop. Phys. Rev. Res. 4, 033029 (2022).

Google Scholar 

Cheng, L., Chen, Y.-Q., Zhang, S.-X. & Zhang, S. Quantum approximate optimization by way of learning-based adaptive optimization. Commun. Phys. 7, 83 (2024).

Google Scholar 

Kundu, A., Botelho, L. & Glos, A. Hamiltonian-oriented homotopy quantum approximate optimization algorithm. Phys. Rev. A 109, 022611 (2024).

Google Scholar 

Krenn, M., Landgraf, J., Foesel, T. & Marquardt, F. Artificial intelligence and machine studying for quantum applied sciences. Phys. Rev. A 107, 010101 (2023).

Google Scholar 

Bang, J., Ryu, J., Yoo, S., Pawłowski, M. & Lee, J. A method for quantum algorithm design assisted by machine studying. N. J. Phys. 16, 073017 (2014).

Google Scholar 

Ostaszewski, M., Trenkwalder, L. M., Masarczyk, W., Scerri, E. & Dunjko, V. Reinforcement studying for optimization of variational quantum circuit architectures. Adv. Neural Inf. Process. Syst. 34, 18182–18194 (2021).

Google Scholar 

Kundu, A. Reinforcement learning-assisted quantum structure seek for variational quantum algorithms. Preprint at (2024).

Mnih, V. et al. Playing Atari with deep reinforcement studying. Preprint at (2013).

Ye, E. & Chen, S. Y.-C. Quantum structure search by way of continuous reinforcement studying. Preprint at (2021).

Fösel, T., Niu, M. Y., Marquardt, F. & Li, L. Quantum circuit optimization with deep reinforcement studying. Preprint at (2021).

Patel, Y. J. et al. Curriculum reinforcement studying for quantum structure search underneath {hardware} errors. In The Twelfth International Conference on Learning Representations (2024).

Tang, W. et al. Alpharouter: Quantum circuit routing with reinforcement studying and tree search. In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Vol. 1, 930–940 (IEEE, 2024).

Moflic, I. & Paler, A. Cost explosion for environment friendly reinforcement studying optimisation of quantum circuits. In 2023 IEEE International Conference on Rebooting Computing (ICRC), 1–5 (IEEE, 2023).

Patel, Y. J., Jerbi, S., Bäck, T. & Dunjko, V. Reinforcement studying assisted recursive QAOA. EPJ Quantum Technol. 11, 6 (2024).

Google Scholar 

Sadhu, A., Sarkar, A. & Kundu, A. A quantum info theoretic evaluation of reinforcement learning-assisted quantum structure search. Quantum Mach. Intell. 6, 49 (2024).

Google Scholar 

Altmann, P. et al. Challenges for reinforcement studying in quantum circuit design. In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Vol. 1, 1600–1610 (IEEE, 2024).

Alur, R. et al. Syntax-guided Synthesis (IEEE, 2013).

Srivastava, S., Gulwani, S. & Foster, J. S. From program verification to program synthesis. In Proceedings of the thirty seventh Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, 313–326 (2010).

Deng, H., Tao, R., Peng, Y. & Wu, X. A case for synthesis of recursive quantum unitary applications. Proc. ACM Program. Lang. 8, 1759–1788 (2024).

Google Scholar 

Sarra, L., Ellis, Okay. & Marquardt, F. Discovering quantum circuit parts with program synthesis. Mach. Learn.: Sci. Technol. 5, 025029 (2024).

Google Scholar 

Ellis, Okay. et al. Dreamcoder: rising generalizable, interpretable information with wake-sleep Bayesian program studying. Philos. Trans. R. Soc. A. (2020).

Dechter, E., Malmaud, J., Adams, R. P. & Tenenbaum, J. B. Bootstrap studying by way of modular idea discovery. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI ’13, 1302–1309 (AAAI Press, 2013).

Ruiz, F. J. R. et al. Quantum circuit optimization with alphatensor. Nat. Mach. Intell. 7, 374–385 (2025).

Trenkwalder, L. M., López-Incera, A., Nautrup, H. P., Flamini, F. & Briegel, H. J. Automated gadget discovery within the quantum area. Mach. Learn.: Sci. Technol. 4, 035043 (2023).

Google Scholar 

Wille, R. & Burgholzer, L. Mqt qmap: environment friendly quantum circuit mapping. In Proceedings of the 2023 International Symposium on Physical Design, 198–204 (2023).

Botea, A., Kishimoto, A. & Marinescu, R. On the complexity of quantum circuit compilation. In Proceedings of the International Symposium on Combinatorial Search, Vol. 9, 138–142 (2018).

Kundu, A., Sarkar, A. & Sadhu, A. KANQAS: Kolmogorov-Arnold community for quantum structure search. EPJ Quantum Technol. 11, 76 (2024).

Google Scholar 

Pierce, B. C.Types and Programming Languages (MIT Press, 2002).

Bravyi, S. & Hastings, M. On complexity of the quantum Ising mannequin. Commun. Math. Phys. 349, 1–45 (2017).

Google Scholar 

Sumeet, Hörmann, M. & Schmidt, Okay. Hybrid quantum-classical algorithm for the transverse-field Ising mannequin within the thermodynamic restrict. Phys. Rev. B 110, 155128 (2024).

Google Scholar 

Curro, N., Danesh, Okay. & Singh, R. R. Quantum criticality within the infinite-range transverse area Ising mannequin. Phys. Rev. B 110, 075112 (2024).

Google Scholar 

Pfeuty, P. The one-dimensional Ising mannequin with a transverse area. ANNALS Phys. 57, 79–90 (1970).

Google Scholar 

Sünkel, L. et al. Quantum circuit building and optimization by means of hybrid evolutionary algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference, 934–942 (2025).

Kandala, A. et al. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. nature 549, 242–246 (2017).

Google Scholar 

Camps, D. et al. An algebraic quantum circuit compression algorithm for Hamiltonian simulation. SIAM J. Matrix Anal. Appl. 43, 1084–1108 (2022).

Google Scholar 

Niu, S. & Todri-Sanial, A. How parallel circuit execution may be helpful for NISQ computing? In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1065–1070 (IEEE, 2022).

Zhu, M. et al. ECMAS: Efficient circuit mapping and scheduling for floor code. In 2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), 158–169 (IEEE, 2024).

Zen, R. et al. Quantum circuit discovery for fault-tolerant logical state preparation with reinforcement studying. Phys. Rev. X 15, 041012 (2025).

Google Scholar 

McCaskey, A. J. et al. Quantum chemistry as a benchmark for near-term quantum computer systems. NPJ Quantum Inf. 5, 99 (2019).

Google Scholar 

de Gracia Triviño, J. A., Delcey, M. G. & Wendin, G. Complete energetic area strategies for NISQ gadgets: the significance of canonical orbital optimization for accuracy and noise resilience. J. Chem. Theory Comput. 19, 2863–2872 (2023).

Google Scholar 

Giovagnoli, A., Tresp, V., Ma, Y. & Schubert, M. Qneat: Natural evolution of variational quantum circuit structure. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 647–650 (2023).