Quartic Quantum Speedups for Planted Inference
Alexander Schmidhuber, Ryan O’Donnell, Robin Kothari, Ryan Babbush · Physical Review X · 2025
We describe a quantum algorithm for the Planted Noisy k XOR Problem (also known as Sparse Learning Parity with Noise) that achieves a nearly (fourth-power) speedup over the best known classical algorithm while using exponentially less space. Our work generalizes and simplifies prior work of Hastings [], 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 k XOR Problem has been used as a component of certain cryptographic constructions, our work suggests that some of these are susceptible to superquadratic quantum attacks.