OQC's Mullally: quantum's 'ChatGPT moment' lands 2028–2031

Mullally's framing places quantum where large language models sat a decade ago: a fledgling technology with a wide range of plausible applications that boards are now being asked to see around the corner. The technical difference he draws is that classical bits hold only 0 or 1, while qubits can hold a mixture of both, called superposition, which lets them run many more tasks at high speed. The catch is reliability. Qubits can be erratic, and small shifts in temperature or atmospheric pressure can cause malfunctions, which is why expensive isolation chambers are standard.

The commercial path he describes runs through pattern-spotting rather than general-purpose speed. Classical compute can struggle to identify relationships between data, whereas quantum moves that data to a higher dimensional plane—like a sphere rather than two points—making relationships easier to see. That is why anomaly detection, fraud detection and anti-money laundering come first in financial services, alongside vulnerability analysis and threat detection on networks. OQC already has applications helping data centers operate more efficiently.

The milestone Mullally points to is step count: average quantum algorithms can regularly execute a thousand steps before errors overwhelm them, and he expects that to reach millions in 2028 to 2031, then billions and trillions of quantum operations. He also raises the encryption risk, noting a scenario where quantum could eat through classical encryption defenses is not outside the realms of possibility, possibly in as little as five years—which is why he argues the good guys need to get there first. Against a backdrop of executives who have spent heavily on AI experiments with flat bottom lines, his pitch is that quantum is a different kind of problem-solving mechanism, one that could also help identify the anomalous behavior of rogue agents.