Measurement-driven quantum advantages in shallow circuits
Chenfeng Cao, Jens Eisert

TL;DR
This paper demonstrates that mid-circuit measurements enable efficient sampling and quantum advantages in shallow circuits, surpassing previous depth limitations and supporting quantum speedups on practical hardware.
Contribution
It introduces a measurement-driven approach for shallow quantum circuits that achieves quantum advantage by bypassing depth constraints and enabling complex entanglement.
Findings
Efficient sampling from structured quantum states using shallow circuits.
Measurement-driven feature maps distinguish quantum phases in machine learning.
Demonstrates quantum advantage on hardware with bounded-degree topology.
Abstract
Quantum advantage schemes probe the boundary between classically simulatable and classically intractable quantum dynamics. We explore the impact of mid-circuit measurements on the computational power of quantum circuits. To this effect, we focus on quantum sampling and introduce a constant-depth measurement-driven approach for efficiently sampling from a broad class of commuting diagonal quantum circuits and associated structured phase states, previously requiring polynomial-depth unitary circuits. By interleaving mid-circuit measurements with feed-forward in randomized "fan-out staircases", our dynamical circuits bypass Lieb-Robinson light-cone constraints, enabling global entanglement with flexible auxiliary qubit usage on bounded-degree lattices (e.g., two-dimensional grids). The generated phase states exhibit random-matrix statistics and anti-concentration comparable to fully random…
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