Implications of Quantum Computing for Artificial Intelligence alignment research

TL;DR

This paper argues that understanding quantum computing is unlikely to significantly aid AI alignment efforts, as quantum features do not address core bottlenecks and may introduce new complexities.

Contribution

It clarifies the implications of quantum computing for AI alignment, emphasizing that quantum advantages do not resolve existing challenges in alignment research.

Findings

Quantum computing leads to compute overhang, not algorithmic overhang.

Measurement difficulties in quantum states do not undermine AI alignment assumptions.

Certain issues like tripwiring and adversarial blinding may still pose challenges.

Abstract

We explain some key features of quantum computing via three heuristics and apply them to argue that a deep understanding of quantum computing is unlikely to be helpful to address current bottlenecks in Artificial Intelligence Alignment. Our argument relies on the claims that Quantum Computing leads to compute overhang instead of algorithmic overhang, and that the difficulties associated with the measurement of quantum states do not invalidate any major assumptions of current Artificial Intelligence Alignment research agendas. We also discuss tripwiring, adversarial blinding, informed oversight and side effects as possible exceptions.