A classical program run twice on the same input gives the same output. A quantum program does not. Its output is a set of measurement samples drawn from a probability distribution, and that distribution is shaped by the algorithm, by how the program was compiled for a particular device, and by that device’s noise on the day it ran.
For research this is familiar territory. For organisations that want to rely on quantum results in decisions, audits or regulated settings, it raises a practical question: how do we know an answer is right, and how could someone else confirm it?
What a checkable result needs
- The exact program as submitted, and the compiled circuit the hardware actually ran.
- Which device was used, why it was chosen, and its calibration data at the time of the run.
- The number of shots, the raw measurement results, and any error mitigation applied to them.
- Where possible, an independent check: a classical simulation for small cases, a known answer, or a repeat run on different hardware.
Why it matters now
My doctoral research examined how organisations decide whether to adopt blockchain, a technology whose appeal rests heavily on trust and verifiability. Quantum computing raises similar questions. It seems likely that organisations will adopt it more readily when they can explain and defend its results to boards, auditors and regulators.
This is also why I founded Lucivant Technologies Pty Ltd around the idea of accountable quantum computing, in which every choice of device is explained and every result is something a third party can check. The company’s work is in development, but the principle applies to anyone using quantum hardware today: keep the evidence, not just the answer.