Certified randomness using a trapped-ion quantum processor
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Abstract (arXiv)
While quantum computers have the potential to perform a wide range of practically important tasks beyond the capabilities of classical computers, realizing this potential remains a challenge. One such task is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy. Certified randomness has many applications but is fundamentally impossible to achieve solely by classical computation. In this work, we demonstrate the generation of certifiably random bits using the 56-qubit Quantinuum H2-1 trapped-ion quantum computer accessed over the internet. Our protocol leverages the classical hardness of recent random circuit sampling demonstrations: a client generates quantum "challenge" circuits using a small randomness seed, sends them to an untrusted quantum server to execute, and verifies the server's results. We analyze the security of our protocol against a restricted class of realistic near-term adversaries. Using classical verification with measured combined sustained performance of 1.1×1018 floating-point operations per second across multiple supercomputers, we certify 71,313 bits of entropy under this restricted adversary and additional assumptions. Our results demonstrate a step towards the practical applicability of today's quantum computers.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Mar 2025.
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networkscited by 2
- 2025Sample-efficient quantum error mitigation via classical learning surrogatescited by 0
- 2026The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discoverycited by 0
- 2026The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery18 Mar 2026
- 2025Sample-efficient quantum error mitigation via classical learning surrogates10 Nov 2025
- 2025A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks17 Apr 2025