Data Science and AI
Will quantum computing become more important than artificial intelligence?
Actuarial careers
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The rise of AI tools shows how quickly new technology can transform how we go about certain tasks. Quantum computing (QC) is another emerging technology that holds great promise to undertake complex actuarial modelling not feasible using classical computers. So, will QC be bigger than AI for actuaries, and should we be focusing more on QC opportunities?
From the way the fields are developing, it seems the answer will not be just one or the other - the opportunities lie at the intersection of the two technologies.
Key points:
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The convergence of QC and AI has emerged as an important area of research that promises to transform complex actuarial modelling problems. Some computationally intensive calculations are intractable on classical computers. QC offers a different computational and data paradigm and has the potential to tackle these problems [1].
Simultaneously, AI has achieved breakthroughs in pattern recognition, natural language processing and decision-making systems, yet faces computational bottlenecks in training large models and solving NP-hard optimisation problems.
The synergy between QC and AI manifests in both directions: utilising QC to enhance AI algorithms and employing AI techniques to improve QC systems [2]. This two-way relationship creates a fertile landscape for innovation that could address challenging actuarial problems, for example, complex Monte Carlo simulations, optimising investment and insurance portfolios and risk assessment.
Current quantum devices, while limited by noise and scale, are beginning to demonstrate practical utility in specific applications. The noisy intermediate-scale quantum (NISQ) era presents unique opportunities to explore quantum advantages in AI tasks that are naturally suited to QC.
Several quantum algorithms demonstrate theoretical advantages for problems central to artificial intelligence. The HHL algorithm [3] provides exponential speedup for solving linear systems, fundamental to many machine learning tasks. Quantum approximate optimisation algorithms (QAOA) offer potential advantages for combinatorial optimisation problems common in AI applications [4]. Grover's algorithm provides quadratic speedup for unstructured search, relevant to database queries and pattern matching tasks [5]. A more detailed explanation of these algorithms can be found here .
The most promising near-term opportunities lie in hybrid algorithms that leverage the strengths of both quantum and classical computing [6,7]. Variational algorithms [8], quantum-enhanced sampling methods and quantum-assisted optimisation represent practical approaches that can potentially provide advantages with current NISQ devices.
Case study: Optimising Telstra's network performance with quantum computingTelstra currently uses a combination of machine learning and AI to help predict network performance and detect changes in network patterns. These systems analyse metrics like latency and bandwidth to predict potential variances. Over the past 12 months, Telstra has been working with Silicon Quantum Computing (SQC) to test and evaluate whether SQC's quantum-enhanced machine learning system could generate quantum features that can be used in an AI model [9]. The results were significant. The quantum-enhanced model matched the performance of Telstra's current model but was trained much faster. Training and fine-tuning the quantum system took just days, delivering accuracy on a par with a deep learning model that required weeks of effort. SQC's system also operated efficiently without the GPU hardware demands of the deep learning model. As AI can be resource-intensive, technologies that reduce its cost are increasingly valuable. |
Certain problem domains may offer quantum advantages even with limited quantum devices. These include sampling from complex probability distributions, solving specific types of linear systems and optimising certain classes of objective functions that naturally align with quantum computational strengths.
The development of fault-tolerant quantum computers - expected within the next five to ten years - will unlock the full potential of quantum-enhanced AI. With millions of physical qubits and sophisticated error correction, quantum computers could provide exponential speedups for AI tasks, including training large neural networks, solving complex optimisation problems and simulating large-scale quantum systems.
The future likely involves integrated quantum-classical AI ecosystems where quantum processors handle specific computational tasks while classical systems manage data processing, user interfaces, and hybrid algorithm coordination. This distributed approach could optimise the strengths of each computing paradigm.
QC may enable entirely new AI paradigms that have no classical analogues. Quantum generative models, quantum attention mechanisms and quantum memory architectures represent potential innovations that could transform how we approach artificial intelligence.
An emerging and significant environmental concern is the energy and water consumption of AI data centres [10]. NISQ devices also use significant amounts of energy for cryogenic cooling down to temperatures close to absolute zero, requiring bulky and sophisticated cooling equipment, but in the longer-term field-deployable quantum computers, such as the diamond-based technology of Australian company Quantum Brilliance, could potentially lead to significant improvements in energy and water consumption through algorithmic efficiency, reduced infrastructure needs, and better optimisation.
The convergence of quantum computing and artificial intelligence presents extraordinary opportunities alongside significant challenges. While current NISQ devices offer limited practical advantages, targeted applications show promising near-term potential. The two-way relationship between QC and AI - where quantum systems enhance AI algorithms and AI improves quantum systems - creates a synergy that may accelerate progress in both fields.
Key opportunities include quantum-enhanced optimisation for machine learning, AI-assisted quantum error correction, hybrid algorithms that leverage both paradigms and specialised applications in finance, chemistry and materials science.
However, realising these opportunities requires overcoming substantial technical challenges, including hardware limitations, integration complexities and the fundamental question of when and where quantum advantages can be achieved.
Rewards exist for those who prepare for the arrival of QC ahead of time. Data scientists and other actuaries seeking to take advantage of the convergence of QC and AI should begin building their QC literacy and evaluating opportunities now.
Learn more about quantum computing's potential to reshape data processing, cybersecurity and complex modelling systems across industries here .
The Actuaries Institute is forming a Working Group to explore what quantum computing could mean for the profession. Whether you're deep in the technical detail or curious about what it means for your practice, your perspective matters.
References
[1] Lowe, A. & Walker, M. 2025. Introduction to Quantum Computing. Actuaries Institute.
[2] Dunjko, V., & Briegel, H. J. (2018). Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Reports on Progress in Physics, 81(7), 074001.
[3] Harrow, A. W., Hassidim, A., & Lloyd, S. (2009). Quantum algorithm for linear systems of equations. Physical Review Letters, 103(15), 150502.
[4] Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.
[5] Grover, L. (1996). A fast quantum mechanical algorithm for database search. Proceedings of the twenty-eighth annual ACM symposium on theory of computing (pp. 212-219).
[6] Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79.
[7] Perdomo-Ortiz, A., Benedetti, M., Realpe-Gómez, J., & Biswas, R. (2018). Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. Quantum Science and Technology, 3(3), 030502.
[8] McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A. (2016). The theory of variational hybrid quantum-classical algorithms. New Journal of Physics, 18(2), 023023.
[9] Telstra. (2025, October 13). Quantum meets connectivity: Telstra and SQC explore smarter network prediction [Press release]. https://www.telstra.com.au/aboutus/media/media-releases/Telstra-SQC-quantum-network
[10] Shen J. (2025). Building Tomorrow: Preparing Australia for the Age of AI. Actuaries Institute.
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