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This study establishes an unconditional quantum advantage for sampling tasks using constant-depth circuits, proving that specific probability distributions require logarithmically greater computational depth on classical hardware. The findings confirm that near-term quantum processors can solve certain sampling problems with fewer resources than even optimized classical algorithms, marking a significant milestone in demonstrating practical quantum speedup without relying on noise assumptions.

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New proof shows quantum advantage with shallow circuits

Quantum ZeitgeistAugust 31, 20266 min read

New research demonstrates unconditional quantum advantage for sampling, using constant-depth quantum circuits.

Researchers Adam Bene Watts at the Institute for Quantum Computing, University of Waterloo, and Natalie Parham of Columbia University and Perimeter Institute for Theoretical Physics have demonstrated a clear advantage for quantum circuits when sampling from specific probability distributions. This establishes a logarithmic difference in computational depth, even when classical circuits utilize a limited number of input bits; specifically, k times n plus n to the power of delta i.i.d. Bernoulli random variables with entropy 1/k. This work directly addresses a challenge posed by Bravyi, Gosset, and Koenig, confirming quantum advantage extends to input-independent sampling tasks. The researchers published their findings in Quantum, volume 10, page 2188, with DOI 10.22331/q-2026-.…

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