Professor Paul Griffin, Assistant Director of the Master of Information Technology in Business at Singapore Management University (SMU), has been engaged with quantum computing since the late 1990s. With recent advances in quantum hardware, he develops quantum algorithms to address real-world business challenges. His research with industry partners explores the potential of hybrid quantum/classical neural networks in areas like credit risk assessment for SMEs and decentralized consensus mechanisms. His work has been presented at Techinnovation conferences and published in Nature Scientific Reports. Paul’s Current projects using quantum Monte Carlo and quantum optimization are showing promising results, with publications in progress. He is also developing improved visualizations for quantum circuits and neural networks, and remains driven by the potential of quantum computing to deliver practical business value.
Blending quantum science with real-world impact, Prof. Paul Griffin shares inspiring insights with the SheQuantum Global Quantum Community:
Here’s my thoughts: Now is a great time to start a career in quantum computing. Whilst there are many experts in quantum physics and computer science, there are really no experts in quantum computing as it is such a new field, so anyone has a chance to become one. The field is still emerging, and those who start now can help shape its foundations and applications, building meaningful expertise ahead of the curve.
While the quantum physics required to build quantum processing units (QPUs) and the quantum information theory behind algorithm design may be at a PhD level, actually using quantum algorithms can be surprisingly accessible. Much like how we rely on statistical Python packages such as SciPy without deeply understanding their internal mechanics, quantum software libraries can abstract away the underlying complexity. What really matters is that we understand how to use them effectively — specifically, knowing what kind of data to input, how to interpret the outputs, and recognizing the behaviour, limitations, and performance of an algorithm on different types of QPUs.
To make practical use of quantum computing, it’s essential to first understand how quantum processing differs from classical computing. This means grasping how phenomena like superposition, entanglement, and interference contribute to computational advantages. It’s also important to appreciate the current limitations of quantum hardware, such as noise, decoherence, and limited qubit counts, along with the challenges of integrating quantum systems into existing workflows.
Equally vital is understanding the landscape of available QPUs and simulators. Different platforms—whether gate-based systems, annealers, or photonic processors—have unique characteristics and are suited to different problem types. Knowing when to use a real quantum device versus a simulator can make a big difference, especially in early-stage experimentation and prototyping.
Once the fundamentals are in place, the best way to learn is to get hands-on. Try running quantum algorithms, experiment with toy problems, and observe how the results behave. This is the most effective way to bridge the gap between theory and practice. As Richard Feynman famously said, “If you can’t build it, you don’t understand it.” I’d add, “If you can’t use it, you don’t understand the value.” Practical engagement leads to true understanding.
Currently, most quantum computing jobs focus on algorithm development and are relatively scarce although those with that knowledge are in high demand. However, the field is expanding quickly. Just as demand for AI developers exploded over the last decade, I believe quantum computing will follow a similar path. Preparing now—by learning, experimenting, and building intuition—will place you at the forefront of this transformation.
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