References
Consolidated Academic Citations for the AI-Quantum Symbiosis Series
1. Foundational Quantum Computing
1. Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information (10th Anniversary Edition). Cambridge University Press.
2. Preskill, J. (2018). "Quantum Computing in the NISQ era and beyond." Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79
3. Arute, F., et al. (2019). "Quantum supremacy using a programmable superconducting processor." Nature, 574(7779), 505-510. https://doi.org/10.1038/s41586-019-1666-5
4. Zhong, H. S., et al. (2020). "Quantum computational advantage using photons." Science, 370(6523), 1460-1463. https://doi.org/10.1126/science.abe8770
5. Acharya, R., et al. (2024). "Quantum error correction below the surface code threshold." Nature, 634, 315-320. https://doi.org/10.1038/s41586-024-08449-y
2. Quantum Error Correction
6. Gottesman, D. (2010). "An introduction to quantum error correction and fault-tolerant quantum computation." Quantum Information Science and Its Contributions to Mathematics, 68, 13-58.
7. Fowler, A. G., Mariantoni, M., Martinis, J. M., & Cleland, A. N. (2012). "Surface codes: Towards practical large-scale quantum computation." Physical Review A, 86(3), 032324.
8. Terhal, B. M. (2015). "Quantum error correction for quantum memories." Reviews of Modern Physics, 87(2), 307.
9. Lidar, D. A., & Brun, T. A. (Eds.). (2013). Quantum Error Correction. Cambridge University Press.
3. Machine Learning for Quantum Error Correction
10. Torlai, G., & Melko, R. G. (2017). "Neural decoder for topological codes." Physical Review Letters, 119(3), 030501. https://doi.org/10.1103/PhysRevLett.119.030501
11. Varsamopoulos, S., Criger, B., & Bertels, K. (2018). "Decoding surface codes with deep learning." Quantum Science and Technology, 3(3), 035004.
12. Chamberland, C., & Ronagh, P. (2018). "Deep neural decoders for near term fault-tolerant experiments." Quantum Science and Technology, 3(4), 044002.
13. Nautrup, H. P., et al. (2019). "Optimizing quantum error correction codes with reinforcement learning." Quantum, 3, 215. https://doi.org/10.22331/q-2019-12-16-215
14. Baireuther, P., et al. (2018). "Machine-learning-assisted correction of correlated qubit errors in a topological code." Quantum, 2, 48.
15. Sweke, R., et al. (2020). "Reinforcement learning for quantum error correction." arXiv:2003.05252.
16. Fitzek, D., et al. (2020). "Deep Q-learning decoder for depolarizing noise." Machine Learning: Science and Technology, 1(3), 035004.
17. Maskara, N., et al. (2019). "Advantages of versatile neural-network decoding for topological codes." Physical Review A, 99(5), 052351.
18. Andreasson, P., Johansson, J., Liljestrand, S., & Granath, M. (2019). "Quantum error correction for the toric code using deep reinforcement learning." Quantum, 3, 183.
4. Quantum Control
19. Khaneja, N., Reiss, T., Kehlet, C., Schulte-Herbrüggen, T., & Glaser, S. J. (2005). "Optimal control of coupled spin dynamics: Design of NMR pulse sequences by gradient ascent algorithms." Journal of Magnetic Resonance, 172(2), 296-305.
20. Wilhelm, F. K., et al. (2020). "An introduction to optimal control for quantum systems." arXiv:2003.10132.
21. Devoret, M. H., & Schoelkopf, R. J. (2013). "Superconducting circuits for quantum information: An outlook." Science, 339(6124), 1169-1174.
22. Blais, A., Grimsmo, A. L., Girvin, S. M., & Wallraff, A. (2021). "Circuit quantum electrodynamics." Reviews of Modern Physics, 93(2), 025005.
5. Machine Learning for Quantum Control
23. Niu, M. Y., et al. (2019). "Universal quantum control through deep reinforcement learning." npj Quantum Information, 5(1), 33. https://doi.org/10.1038/s41534-019-0141-3
24. Baum, Y., et al. (2021). "Experimental deep reinforcement learning for error-robust gate-set design on a superconducting quantum computer." PRX Quantum, 2(4), 040324.
25. Wittler, N., et al. (2021). "Integrated tool set for control, calibration, and characterization of quantum devices applied to superconducting qubits." Physical Review Applied, 15(3), 034080.
26. Kelly, J., et al. (2018). "Physical qubit calibration on a directed acyclic graph." arXiv:1803.03226.
27. Klimov, P. V., et al. (2020). "Fluctuations of energy-relaxation times in superconducting qubits." Physical Review Letters, 121(9), 090502.
28. Werninghaus, M., et al. (2021). "Leakage reduction in fast superconducting qubit gates via optimal control." npj Quantum Information, 7(1), 14.
29. Rol, M. A., et al. (2019). "Fast, high-fidelity conditional-phase gate exploiting leakage interference in weakly anharmonic superconducting qubits." Physical Review Letters, 123(12), 120502.
30. Schäfer, F., et al. (2020). "A differentiable programming method for quantum control." Machine Learning: Science and Technology, 1(3), 035009.
6. Circuit Optimization
31. Khatri, S., et al. (2019). "Quantum-assisted quantum compiling." Quantum, 3, 140. https://doi.org/10.22331/q-2019-05-13-140
32. Li, G., et al. (2019). "Tackling the qubit mapping problem for NISQ-era quantum devices." ASPLOS, 1001-1014.
33. Zhu, E. Y., et al. (2020). "An adaptive optimizer for measurement-frugal variational algorithms." Quantum, 4, 263.
34. Cincio, L., et al. (2018). "Learning the quantum algorithm for state overlap." New Journal of Physics, 20(11), 113022.
35. Fösel, T., et al. (2021). "Quantum circuit optimization with deep reinforcement learning." arXiv:2103.07585.
36. Murali, P., et al. (2019). "Noise-adaptive compiler mappings for noisy intermediate-scale quantum computers." ASPLOS, 1015-1029.
37. Venturelli, D., et al. (2018). "Compiling quantum circuits to realistic hardware architectures using temporal planners." Quantum Science and Technology, 3(2), 025004.
38. Nash, B., et al. (2020). "Quantum circuit optimizations for NISQ architectures." Quantum Science and Technology, 5(2), 025010.
39. Itoko, T., Raymond, R., Imamichi, T., & Matsuo, A. (2020). "Optimization of quantum circuit mapping using gate transformation and commutation." Integration, 70, 43-50.
7. Materials Discovery
40. Schmidt, J., et al. (2019). "Recent advances and applications of machine learning in solid-state materials science." npj Computational Materials, 5(1), 83. https://doi.org/10.1038/s41524-019-0221-0
41. Butler, K. T., et al. (2018). "Machine learning for molecular and materials science." Nature, 559(7715), 547-555. https://doi.org/10.1038/s41586-018-0337-2
42. Choudhary, K., et al. (2022). "Recent advances and applications of deep learning methods in materials science." npj Computational Materials, 8(1), 59.
43. Sanchez-Lengeling, B., & Aspuru-Guzik, A. (2018). "Inverse molecular design using machine learning: Generative models for matter engineering." Science, 361(6400), 360-365.
44. Noh, J., et al. (2019). "Inverse design of solid-state materials via a continuous representation." Matter, 1(5), 1370-1384.
45. Kim, K., et al. (2021). "Machine learning-assisted design of superconducting circuits." Physical Review Applied, 16(1), 014032.
46. Ren, Z., et al. (2018). "Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments." Science Advances, 4(4), eaaq1566.
8. Quantum Machine Learning
47. Biamonte, J., et al. (2017). "Quantum machine learning." Nature, 549(7671), 195-202. https://doi.org/10.1038/nature23474
48. Cerezo, M., et al. (2021). "Variational quantum algorithms." Nature Reviews Physics, 3(9), 625-644.
49. Huang, H. Y., et al. (2022). "Quantum advantage in learning from experiments." Science, 376(6598), 1182-1186.
50. Liu, Y., et al. (2021). "Rigorous and robust quantum speedup in supervised machine learning." Nature Physics, 17(9), 1013-1017.
51. Abbas, A., et al. (2021). "The power of quantum neural networks." Nature Computational Science, 1(6), 403-409.
52. Schuld, M., & Petruccione, F. (2021). Machine Learning with Quantum Computers (2nd ed.). Springer.
9. Quantum Memory and QRAM
53. Giovannetti, V., Lloyd, S., & Maccone, L. (2008). "Quantum random access memory." Physical Review Letters, 100(16), 160501.
54. Arunachalam, S., Gheorghiu, V., Jochym-O'Connor, T., Mosca, M., & Srinivasan, V. (2015). "On the robustness of bucket brigade quantum RAM." New Journal of Physics, 17(12), 123010.
55. Hann, C. T., et al. (2021). "Resilience of quantum random access memory to generic noise." PRX Quantum, 2(2), 020311.
10. Variational Algorithms
56. McClean, J. R., et al. (2016). "The theory of variational hybrid quantum-classical algorithms." New Journal of Physics, 18(2), 023023.
57. Peruzzo, A., et al. (2014). "A variational eigenvalue solver on a photonic quantum processor." Nature Communications, 5(1), 4213.
58. Farhi, E., Goldstone, J., & Gutmann, S. (2014). "A quantum approximate optimization algorithm." arXiv:1411.4028.
59. McClean, J. R., et al. (2018). "Barren plateaus in quantum neural network training landscapes." Nature Communications, 9(1), 4812.
11. Hardware Platforms
Superconducting
60. Koch, J., et al. (2007). "Charge-insensitive qubit design derived from the Cooper pair box." Physical Review A, 76(4), 042319.
61. Krantz, P., et al. (2019). "A quantum engineer's guide to superconducting qubits." Applied Physics Reviews, 6(2), 021318.
Trapped Ion
62. Bruzewicz, C. D., Chiaverini, J., McConnell, R., & Sage, J. M. (2019). "Trapped-ion quantum computing: Progress and challenges." Applied Physics Reviews, 6(2), 021314.
63. Monroe, C., et al. (2021). "Programmable quantum simulations of spin systems with trapped ions." Reviews of Modern Physics, 93(2), 025001.
Photonic
64. O'Brien, J. L., Furusawa, A., & Vučković, J. (2009). "Photonic quantum technologies." Nature Photonics, 3(12), 687-695.
65. Madsen, L. S., et al. (2022). "Quantum computational advantage with a programmable photonic processor." Nature, 606(7912), 75-81.
Neutral Atom
66. Saffman, M. (2016). "Quantum computing with atomic qubits and Rydberg interactions: progress and challenges." Journal of Physics B, 49(20), 202001.
67. Henriet, L., et al. (2020). "Quantum computing with neutral atoms." Quantum, 4, 327.
12. Surveys and Reviews
68. Bharti, K., et al. (2022). "Noisy intermediate-scale quantum algorithms." Reviews of Modern Physics, 94(1), 015004.
69. Endo, S., Cai, Z., Benjamin, S. C., & Yuan, X. (2021). "Hybrid quantum-classical algorithms and quantum error mitigation." Journal of the Physical Society of Japan, 90(3), 032001.
70. Alexeev, Y., et al. (2021). "Quantum computer systems for scientific discovery." PRX Quantum, 2(1), 017001.
Citation Format
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How to Cite This Series
If you use concepts from this research series, please cite:
@misc{ai_quantum_symbiosis_2026,
author = {[Your Name]},
title = {AI-Quantum Symbiosis: How AI Accelerates Quantum Computing},
year = {2026},
howpublished = {Research Series},
note = {Available at: Documents/Learning/ai\_quantum\_symbiosis/}
}References compiled for: AI-Quantum Symbiosis Research Series
Location: Documents/Learning/ai_quantum_symbiosis/