IBM Proves Quantum Circuits Outperform LLMs on Two Problems
On September 15, 2026, IBM Research published an article proving that shallow quantum circuits outperform large language models (LLMs) in two specific problem domains: functional problems and sampling problems. The research was conducted by Srinivasan Arunachalam, Arkopal Dutt, Hari Krovi, Rik Sengupta, and Ryan Mandelbaum. They focused on transformer-based LLMs, which are a common architecture…
Key points
- IBM Research proves quantum circuits outperform LLMs on functional and sampling problems
- Quantum circuits use one classical AND gate to solve iterated index function problem
- LLMs cannot efficiently sample distributions produced by shallow quantum circuits
IBM Research Proves Quantum Circuits Outperform LLMs on Two Problems
Unite.AI · 15 September 2026
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