The Challenges of Quantum Algorithm Innovation

This blog is directed at quantum professionals with an obligation to developtrustworthy AI andQML models, affording them with a high-level glimpse into Quantum’s impact on AI and future SI.

To date, the advancements afforded by quantum are quantified as both limitless and unknown [8]. That said, quantum algorithm innovation has remained slow to advance due to the lack of realistic Quantum Computing (QC) technology and the fragile technology’s decoherence struggles [1]. Due to environmental factors associated with Noisy Intermediate-Scale Quantum (NISQ) systems, computations on non-trivial problems typically incur a near-100 percent error rate. These limitations impede Quantum Machine Learning (QML) execution, performance, and evaluation of complex real-world algorithms.

Still, recent Quantum Generative Adversarial Network (QGAN) breakthroughs [2,3,4,5,6,7] reveal promise at using a QML-mechanized solution for the automatic detection and correction of the three most common quantum errors: bit flip errors, phase flip errors, and bit and phase flip errors. Achieving mechanized quantum error correction paves the way for fault-tolerant QC, affording quantum scale at quantum speeds [8].

By the end of 2023, IBM plans to unveil a 1,123 qubit NISQ system [9]. This QC achievement moves the quantum development community one-step closer to full-scale quantum algorithm development and achieving a true quantum advantage over classical systems. With advancements in QML and QC research on the rise, practical quantum uses will soon emerge. Once industry discovers quantum applications capable of revenue generation then QC will quickly be adopted. This increased QC demand for commercial access will drive further quantum innovation.

Once humanity has truly achieved practical quantum supremacy quantum logic will sustain Higher-Order Thinking Skills (HOTS), creating new Synthetically Intelligence (SI) systems. While AI currently attempts to mimic human thought, SI will be fully aware, capable of reasoning, critical thinking, comprehension, deduction, metacognition, and all other thought skills associated with human beings [10, 11]. Thus, quantum-SI will blur the cognitive line between human and in-human.

With the dawning of a quantum-powered SI, financial decision-making, healthcare diagnosis, and staff management could be transitioned from humans to a non-human system without people either authorizing or realizing it. This “Frankenstein paradox,” [11] where human intelligence is surpassed by the quantum-SI, presents the thought-provoking question of how to best maintain a level of regulation over science, when that science has suddenly become capable of superiority over humanity. For instance, will a quantum-SI system ensure the ecological validity of deep learning algorithms, and continue using them for societal good, even though it technically does not have to.

References:

1. W. O’Quinn and S. Mao, “Quantum Machine Learning: Recent Advances and Outlook,” in IEEE Wireless Communications Magazine, vol. 27, 2020, pp. 126–131.
2. S. E. Rasmussen and N. T. Zinner, “Multiqubit State Learning with Entangling Quantum Generative Adversarial Networks,” arxiv:2204.09689[cs], 2022.
3. M. Y. Niu, A. Zlokapa, M. Broughton, S. Boixo, M. Mohseni, V. Smelyanskyi, and H. Neven, “Entangling Quantum Generative Adversarial Networks,” arxiv:2105.00080[cs], 2021.
4. S. A. Stein, B. Baheri, D. Chen, Y. Mao, Q. Guan, A. Li, B. Fang, S. Xu. “QuGAN: A Generative Adversarial Network Through Quantum States,” arxiv:2010.09036[cs], 2020.
5. K. Huang, Z.-A. Wang, C. Song, K. Xu, H. Li, Z. Wang, Q. Guo, Z. Song, Z.-B. Liu, D. Zheng, D.-L. Deng, H. Wang, J.-G. Tian, and H. Fan, “Quantum Generative Adversarial Networks with Multiple Superconducting Qubits,” in Npj Quantum Information, vol. 7(1), 2021, Availale: https://doi.org/10.1038/s41534-021-00503-1
6. K. Huang, Z.-A. Wang, C. Song, K. Xu, H. Li, Z. Wang, Q. Guo, Z. Song, Z.-B. Liu, D. Zheng, D.-L. Deng, H. Wang, J.-G. Tian, and H. Fan, “Quantum Generative Adversarial Networks with Multiple Superconducting Qubits,” in Npj Quantum Information, vol. 7(1), 2021, Availale: https://doi.org/10.1038/s41534-021-00503-1
7. Melvin, T. (2022). High-Dimensional Signal Processing using Classical-Quantum Machine Learning Pipelines with the TensorFlow Stack, Cirq-NISQ, and Vertica. 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), Quantum Computing and Engineering (QCE), 2022 IEEE International Conference on, QCE, 793–795. https://doi.org/10.1109/QCE53715.2022.00121
8. S. Rodrigo, S. Abadal, E. Alarcon, and C. G. Almudever, “Will Quantum Computers Scale Without Inter-Chip Comms? A Structured Design Exploration to the  Monolithic vs. Distributed Architectures Quest,” in 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS), Design of Circuits and Integrated Systems (DCIS), 2020 XXXV Conference On, pp. 1–6, 2020, Available https://doi.org/10.1109/DCIS51330.2020.9268630.
9. IBM Research, “IBM’s Roadmap for Scaling Quantum Technology,” Accessed: September 15, 2020. [Online]. Available: https://research.ibm.com/blog/ibm-quantum-roadmap.
10. J. Truby, R. Brown, and A. Dahdal, “Banking on AI: Mandating a Proactive Approach to AI Regulation in the Financial Sector,” in Law & Financial Markets Review, vol. 14(2), 2020, pp. 110–120. Available: https://doi.org/10.1080/17521440.2020.1760454
11. G. Currie, K. E., Hawk, and E. M. Rohren, “Ethical Principles for the Application of Artificial Intelligence (AI) in Nuclear Medicine,” in European Journal of Nuclear Medicine & Molecular Imaging, vol. 47(4), 2020, pp. 748–752. Available: https://doi.org/10.1007/s00259-020-04678-1
12. S. J. Nawaz, S. K. Sharma, S. Wyne, M. N. Patwary, and M. Asaduzzaman, “Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future,” 2019, Available https://ieeexplore-ieeeorg.proxy1.ncu.edu/stamp/stamp.jsp?tp=&arnumber=8681450.