Quantum Security, Quantum Finance

SMU professor urges Singapore to invest in quantum skills now

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September 8, 2026 by Ivy Delaney

Singapore faces a future threat to financial security that artificial intelligence does not pose; quantum computers possess the potential to break the cryptographic systems currently protecting business transactions. SMU Associate Professor Paul Griffin urges financial institutions to proactively cultivate a quantum-skilled workforce, stating that they need quantum-safe cybersecurity to protect data for its entire lifetime.

Griffin highlights Singapore’s unique advantage, with existing connections between its financial sector, universities, and technology investment, positioning the nation to nurture the professionals needed as the technology matures. He believes this preparation is essential to maintain Singapore’s standing as a financial hub.

Singapore’s Financial Sector Positioned for Quantum Cybersecurity

Quantum computers present a distinct threat to financial data security because they can break established cryptographic systems, unlike current artificial intelligence models. Singapore’s financial institutions must prioritize safeguarding sensitive information, including digital identities and payment infrastructure, not just for present needs but for its entire lifetime.

This long-term protection is critical given the extended lifespan of financial records and communications. The nation’s combination of a robust financial sector, active universities, a thriving technology ecosystem, and dedicated national investment in quantum technologies uniquely positions it to cultivate the necessary workforce; these connections will be instrumental in addressing future threats and securing the nation’s financial systems.

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Cryptography Cybersecurity financial institutions Quantum Computing Singapore
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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