Robust sparse IQP sampling in constant depth
Louis Paletta, Anthony Leverrier, Alain Sarlette, Mazyar Mirrahimi,, Christophe Vuillot

TL;DR
This paper presents a fault-tolerant scheme for sparse IQP quantum circuits that achieves a provable superpolynomial quantum advantage with minimal error correction, constant depth, and efficient state preparation.
Contribution
It introduces the tetrahelix code enabling fault-tolerant, constant-depth implementation of sparse IQP circuits with minimal overhead and classical feed-forward.
Findings
Achieves superpolynomial quantum advantage under complexity conjectures.
Provides a fault-tolerant, constant-depth implementation of sparse IQP circuits.
Demonstrates efficient state preparation with classical feed-forward.
Abstract
Between NISQ (noisy intermediate scale quantum) approaches without any proof of robust quantum advantage and fully fault-tolerant quantum computation, we propose a scheme to achieve a provable superpolynomial quantum advantage (under some widely accepted complexity conjectures) that is robust to noise with minimal error correction requirements. We choose a class of sampling problems with commuting gates known as sparse IQP (Instantaneous Quantum Polynomial-time) circuits and we ensure its fault-tolerant implementation by introducing the tetrahelix code. This new code is obtained by merging several tetrahedral codes (3D color codes) and has the following properties: each sparse IQP gate admits a transversal implementation, and the depth of the logical circuit can be traded for its width. Combining those, we obtain a depth-1 implementation of any sparse IQP circuit up to the preparation…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum-Dot Cellular Automata · Low-power high-performance VLSI design
