01 Quantum Patents AI That Works On Encrypted Data. The Government Use Case Is Obvious. The Math Is Harder Than The Headline.
Toronto-based 01 Quantum Inc., listed on TSXV as ONE and OTCQB as OONEF, received a US patent covering AI inference on data that remains encrypted throughout processing. The patent scope also includes an AI marketplace designed to protect both sensitive data and proprietary models for government, enterprise, and defence applications. The company plans to demonstrate quantum-safe encrypted AI analytics at GCXpo 2026, hosted by Area X.O and operated by Invest Ottawa.
The principle at work is homomorphic-adjacent computation: performing operations on ciphertext without decrypting it first, so that neither the compute provider nor the model vendor ever sees the raw input. The mechanism matters because it eliminates the trust problem at the heart of cloud AI, where you currently must hand over plaintext data to a third party to get a prediction. The lesson is that the next frontier of AI infrastructure is not raw capability but confidentiality, and the organisations that solve the trust gap will capture the most sensitive workloads.
01 Quantum Inc., a Toronto-based cybersecurity company focused on the quantum computing era, holds the patent and will showcase the technology at GCXpo 2026, hosted by Area X.O and operated by Invest Ottawa.
- Open a free consumer encryption tool such as VeraCrypt and create an encrypted volume on your computer. This demonstrates the baseline concept of keeping data unreadable to anyone without the key.
- Store a simple text file inside the encrypted volume, then close and dismount it. Confirm the file is inaccessible without mounting with your password.
- Search online for a demo of homomorphic encryption, such as the Microsoft SEAL library's browser examples, to see the principle of computation on encrypted values visualised in action. You will not run production AI this way, but you will understand the gap between today's encryption and what 01 Quantum's patent addresses.