Quartic Quantum Speedups for Planted Inference

We describe a quantum algorithm for the Planted Noisy kXOR Problem (also known as Sparse Learning Parity with Noise) that achieves a nearly quartic (fourth-power) speedup over the best known classical algorithm while using exponentially less space. Our work generalizes and simplifies prior work of H...

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Main Authors: Alexander Schmidhuber, Ryan O’Donnell, Robin Kothari, Ryan Babbush
Format: Article
Language:English
Published: American Physical Society 2025-06-01
Series:Physical Review X
Online Access:http://doi.org/10.1103/PhysRevX.15.021077
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author Alexander Schmidhuber
Ryan O’Donnell
Robin Kothari
Ryan Babbush
author_facet Alexander Schmidhuber
Ryan O’Donnell
Robin Kothari
Ryan Babbush
author_sort Alexander Schmidhuber
collection DOAJ
description We describe a quantum algorithm for the Planted Noisy kXOR Problem (also known as Sparse Learning Parity with Noise) that achieves a nearly quartic (fourth-power) speedup over the best known classical algorithm while using exponentially less space. Our work generalizes and simplifies prior work of Hastings [Quantum 4, 237 (2020)2521-327X10.22331/q-2020-02-27-237], by building on his quantum algorithm for the tensor principal component analysis (PCA) problem. We achieve our quantum speedup using a general framework based on the Kikuchi method (recovering the quartic speedup for Tensor PCA), and we anticipate it will yield similar speedups for further planted inference problems. These speedups rely on the fact that planted inference problems naturally instantiate the guided sparse Hamiltonian problem. Since the Planted Noisy kXOR Problem has been used as a component of certain cryptographic constructions, our work suggests that some of these are susceptible to superquadratic quantum attacks.
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spelling doaj-art-de0cd23638ae4a728dbb6308a9d0a1e62025-08-20T01:58:41ZengAmerican Physical SocietyPhysical Review X2160-33082025-06-0115202107710.1103/PhysRevX.15.021077Quartic Quantum Speedups for Planted InferenceAlexander SchmidhuberRyan O’DonnellRobin KothariRyan BabbushWe describe a quantum algorithm for the Planted Noisy kXOR Problem (also known as Sparse Learning Parity with Noise) that achieves a nearly quartic (fourth-power) speedup over the best known classical algorithm while using exponentially less space. Our work generalizes and simplifies prior work of Hastings [Quantum 4, 237 (2020)2521-327X10.22331/q-2020-02-27-237], by building on his quantum algorithm for the tensor principal component analysis (PCA) problem. We achieve our quantum speedup using a general framework based on the Kikuchi method (recovering the quartic speedup for Tensor PCA), and we anticipate it will yield similar speedups for further planted inference problems. These speedups rely on the fact that planted inference problems naturally instantiate the guided sparse Hamiltonian problem. Since the Planted Noisy kXOR Problem has been used as a component of certain cryptographic constructions, our work suggests that some of these are susceptible to superquadratic quantum attacks.http://doi.org/10.1103/PhysRevX.15.021077
spellingShingle Alexander Schmidhuber
Ryan O’Donnell
Robin Kothari
Ryan Babbush
Quartic Quantum Speedups for Planted Inference
Physical Review X
title Quartic Quantum Speedups for Planted Inference
title_full Quartic Quantum Speedups for Planted Inference
title_fullStr Quartic Quantum Speedups for Planted Inference
title_full_unstemmed Quartic Quantum Speedups for Planted Inference
title_short Quartic Quantum Speedups for Planted Inference
title_sort quartic quantum speedups for planted inference
url http://doi.org/10.1103/PhysRevX.15.021077
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