Bibliography¶
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[grech2026] Grech, L., Krauss, M. G., Consiglio, M., Apollaro, T. J. G., Koch, C. P., Hirlaender, S., & Valentino, G. (2025). Achieving fast and robust perfect entangling gates via reinforcement learning. Quantum Science and Technology, 11(1), 15030. https://doi.org/10.1088/2058-9565/ae2c16
[mckay2016] McKay, D. C., Filipp, S., Mezzacapo, A., Magesan, E., Chow, J. M., & Gambetta, J. M. (2016). Universal Gate for Fixed-Frequency Qubits via a Tunable Bus. Physical Review Applied, 6(6). https://doi.org/10.1103/physrevapplied.6.064007
[mckay2017] McKay, D. C., Wood, C. J., Sheldon, S., Chow, J. M., & Gambetta, J. M. (2017). Efficient
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[pedersen2007] Pedersen, L. H., Møller, N. M., & Mølmer, K. (2007). Fidelity of quantum operations. Physics Letters A, 367(1-2), 47–51. https://doi.org/10.1016/j.physleta.2007.02.069
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[sung2021] Sung, Y., Ding, L., Braumüller, J., Vepsäläinen, A., Kannan, B., Kjaergaard, M., Greene, A., Samach, G. O., McNally, C., Kim, D., Melville, A., Niedzielski, B. M., Schwartz, M. E., Yoder, J. L., Orlando, T. P., Gustavsson, S., & Oliver, W. D. (2021). Realization of High-Fidelity CZ and
[ding2023] Ding, L., Hays, M., Sung, Y., Kannan, B., An, J., Di Paolo, A., Karamlou, A. H., Hazard, T. M., Azar, K., Kim, D. K., Niedzielski, B. M., Melville, A., Schwartz, M. E., Yoder, J. L., Orlando, T. P., Gustavsson, S., Grover, J. A., Serniak, K., & Oliver, W. D. (2023). High-Fidelity, Frequency-Flexible Two-Qubit Fluxonium Gates with a Transmon Coupler. Physical Review X, 13(3). https://doi.org/10.1103/physrevx.13.031035
[li2024] Li, R., Kubo, K., Ho, Y., Yan, Z., Nakamura, Y., & Goto, H. (2024). Realization of High-Fidelity CZ Gate Based on a Double-Transmon Coupler. Physical Review X, 14(4). https://doi.org/10.1103/physrevx.14.041050
[sarma2025] Sarma, B., & Hartmann, M. J. (2025). Designing fast quantum gates using optimal control with a reinforcement-learning ansatz. Physical Review Applied, 23(1). https://doi.org/10.1103/physrevapplied.23.014015
[burnett2019] Burnett, J. J., Bengtsson, A., Scigliuzzo, M., Niepce, D., Kudra, M., Delsing, P., & Bylander, J. (2019). Decoherence benchmarking of superconducting qubits. Npj Quantum Information, 5(1). https://doi.org/10.1038/s41534-019-0168-5
[schlor2019] Schlör, S., Lisenfeld, J., Müller, C., Bilmes, A., Schneider, A., Pappas, D. P., Ustinov, A. V., & Weides, M. (2019). Correlating Decoherence in Transmon Qubits: Low Frequency Noise by Single Fluctuators. Physical Review Letters, 123(19). https://doi.org/10.1103/physrevlett.123.190502
[zhang2022] Zhang, E. J., Srinivasan, S., Sundaresan, N., Bogorin, D. F., Martin, Y., Hertzberg, J. B., Timmerwilke, J., Pritchett, E. J., Yau, J.-B., Wang, C., Landers, W., Lewandowski, E. P., Narasgond, A., Rosenblatt, S., Keefe, G. A., Lauer, I., Rothwell, M. B., McClure, D. T., Dial, O. E., et al. (2022). High-performance superconducting quantum processors via laser annealing of transmon qubits. Science Advances, 8(19). https://doi.org/10.1126/sciadv.abi6690
[dhieb2025] Dhieb, C., Weber, J., Taubenberger, S., Guizan, C. M., Lang, S. J. K., Luo, Z., Music, E., Maiwald, A., Lerch, W., Nebrich, L., Tornow, M., Mayer, T., Zahn, D., Pereira, R. N., & Kutter, C. (2025). Stability studies on subtractively-fabricated CMOS-compatible superconducting transmon qubits (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2512.18037
[groszkowski2021] Groszkowski, P., & Koch, J. (2021). Scqubits: a Python package for superconducting qubits. Quantum, 5, 583. CLOCKSS. https://doi.org/10.22331/q-2021-11-17-583
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[wu2018] Wu, R.-B., Chu, B., Owens, D. H., & Rabitz, H. (2018). Data-driven gradient algorithm for high-precision quantum control. Physical Review A, 97(4). https://doi.org/10.1103/physreva.97.042122
[schuld2019] Schuld, M., Bergholm, V., Gogolin, C., Izaac, J., & Killoran, N. (2019). Evaluating analytic gradients on quantum hardware. Physical Review A, 99(3). https://doi.org/10.1103/physreva.99.032331
[kelly2018] Kelly, J., O'Malley, P., Neeley, M., Neven, H., & Martinis, J. M. (2018). Physical qubit calibration on a directed acyclic graph (Version 1). arXiv. https://doi.org/10.48550/ARXIV.1803.03226
[proctor2020] Proctor, T., Revelle, M., Nielsen, E., Rudinger, K., Lobser, D., Maunz, P., Blume-Kohout, R., & Young, K. (2020). Detecting and tracking drift in quantum information processors. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-19074-4
[klimov2024] Klimov, P. V., Bengtsson, A., Quintana, C., Bourassa, A., Hong, S., Dunsworth, A., Satzinger, K. J., Livingston, W. P., Sivak, V., Niu, M. Y., Andersen, T. I., Zhang, Y., Chik, D., Chen, Z., Neill, C., Erickson, C., Grajales Dau, A., Megrant, A., Roushan, P., et al. (2024). Optimizing quantum gates towards the scale of logical qubits. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-46623-y
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[amos2023] Amos, B. (2023). Tutorial on Amortized Optimization. Foundations and Trends® in Machine Learning, 16(5), 592–732. https://doi.org/10.1561/2200000102
[dai2021] Dai, X., Tennant, D. M., Trappen, R., Martinez, A. J., Melanson, D., Yurtalan, M. A., Tang, Y., Novikov, S., Grover, J. A., Disseler, S. M., Basham, J. I., Das, R., Kim, D. K., Melville, A. J., Niedzielski, B. M., Weber, S. J., Yoder, J. L., Lidar, D. A., & Lupascu, A. (2021). Calibration of Flux Crosstalk in Large-Scale Flux-Tunable Superconducting Quantum Circuits. PRX Quantum, 2(4). https://doi.org/10.1103/prxquantum.2.040313
[lembono2020] Lembono, T. S., Paolillo, A., Pignat, E., & Calinon, S. (2020). Memory of Motion for Warm-Starting Trajectory Optimization. IEEE Robotics and Automation Letters, 5(2), 2594–2601. https://doi.org/10.1109/lra.2020.2972893
[cheng2020] Cheng, J., Deng, H., & Qian, X. (2020). AccQOC: Accelerating Quantum Optimal Control Based Pulse Generation (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2003.00376
[kiermeyer2026] Kiermeyer, T., Heydenreich, T., Van Damme, L., Hohenemser, S., Marquardt, F., & Glaser, S. J. (2026). Uncovering Latent Structures in Robust Pulse Sequences: A Model-Based Reinforcement Learning Approach for Adaptable Quantum Control (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2606.24507
[luchi2026] Luchi, P., & Pederiva, F. (2026). Lie Algebra-Based Quantum Optimal Controls Interpolation (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2606.02014
[malarchick2026] Malarchick, R. (2026). Measuring Control-Plane Openness in Near-Term Quantum Computing: A Rubric, Its Validation, and an Application to Thirteen Vendor Stacks (Version 3). arXiv. https://doi.org/10.48550/ARXIV.2605.15233
[jandura2022] Jandura, S., & Pupillo, G. (2022). Time-Optimal Two- and Three-Qubit Gates for Rydberg Atoms. Quantum, 6, 712. CLOCKSS. https://doi.org/10.22331/q-2022-05-13-712
[evered2023] Evered, S. J., Bluvstein, D., Kalinowski, M., Ebadi, S., Manovitz, T., Zhou, H., Li, S. H., Geim, A. A., Wang, T. T., Maskara, N., Levine, H., Semeghini, G., Greiner, M., Vuletić, V., & Lukin, M. D. (2023). High-fidelity parallel entangling gates on a neutral-atom quantum computer. Nature, 622(7982), 268–272. https://doi.org/10.1038/s41586-023-06481-y
[leung2018] Leung, P. H., Landsman, K. A., Figgatt, C., Linke, N. M., Monroe, C., & Brown, K. R. (2018). Robust 2-Qubit Gates in a Linear Ion Crystal Using a Frequency-Modulated Driving Force. Physical Review Letters, 120(2). https://doi.org/10.1103/physrevlett.120.020501
[schulman2015] Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015). Trust Region Policy Optimization (Version 5). arXiv. https://doi.org/10.48550/ARXIV.1502.05477
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[sutton1990] Sutton, R. S. (1990). Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. Machine Learning Proceedings 1990, 216–224. https://doi.org/10.1016/b978-1-55860-141-3.50030-4