Lower bounds for quantum-inspired classical algorithms via communication complexity

Quantum-inspired classical algorithms provide us with a new way to understand the computational power of quantum computers for practically-relevant problems, especially in machine learning. In the past several years, numerous efficient algorithms for various tasks have been found, while an analysis...

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Main Authors: Nikhil S. Mande, Changpeng Shao
Format: Article
Language:English
Published: Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften 2025-01-01
Series:Quantum
Online Access:https://quantum-journal.org/papers/q-2025-01-14-1593/pdf/
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author Nikhil S. Mande
Changpeng Shao
author_facet Nikhil S. Mande
Changpeng Shao
author_sort Nikhil S. Mande
collection DOAJ
description Quantum-inspired classical algorithms provide us with a new way to understand the computational power of quantum computers for practically-relevant problems, especially in machine learning. In the past several years, numerous efficient algorithms for various tasks have been found, while an analysis of lower bounds is still missing. Using communication complexity, in this work we propose the first method to study lower bounds for these tasks. We mainly focus on lower bounds for solving linear regressions, supervised clustering, principal component analysis, recommendation systems, and Hamiltonian simulations. For those problems, we prove a quadratic lower bound in terms of the Frobenius norm of the underlying matrix. As quantum algorithms are linear in the Frobenius norm for those problems, our results mean that the quantum-classical separation is at least quadratic. As a generalisation, we extend our method to study lower bounds analysis of quantum query algorithms for matrix-related problems using quantum communication complexity. Some applications are given.
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spelling doaj-art-8aeccdcfa3454b91b578bf1df64bc9da2025-08-20T02:42:00ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2025-01-019159310.22331/q-2025-01-14-159310.22331/q-2025-01-14-1593Lower bounds for quantum-inspired classical algorithms via communication complexityNikhil S. MandeChangpeng ShaoQuantum-inspired classical algorithms provide us with a new way to understand the computational power of quantum computers for practically-relevant problems, especially in machine learning. In the past several years, numerous efficient algorithms for various tasks have been found, while an analysis of lower bounds is still missing. Using communication complexity, in this work we propose the first method to study lower bounds for these tasks. We mainly focus on lower bounds for solving linear regressions, supervised clustering, principal component analysis, recommendation systems, and Hamiltonian simulations. For those problems, we prove a quadratic lower bound in terms of the Frobenius norm of the underlying matrix. As quantum algorithms are linear in the Frobenius norm for those problems, our results mean that the quantum-classical separation is at least quadratic. As a generalisation, we extend our method to study lower bounds analysis of quantum query algorithms for matrix-related problems using quantum communication complexity. Some applications are given.https://quantum-journal.org/papers/q-2025-01-14-1593/pdf/
spellingShingle Nikhil S. Mande
Changpeng Shao
Lower bounds for quantum-inspired classical algorithms via communication complexity
Quantum
title Lower bounds for quantum-inspired classical algorithms via communication complexity
title_full Lower bounds for quantum-inspired classical algorithms via communication complexity
title_fullStr Lower bounds for quantum-inspired classical algorithms via communication complexity
title_full_unstemmed Lower bounds for quantum-inspired classical algorithms via communication complexity
title_short Lower bounds for quantum-inspired classical algorithms via communication complexity
title_sort lower bounds for quantum inspired classical algorithms via communication complexity
url https://quantum-journal.org/papers/q-2025-01-14-1593/pdf/
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AT changpengshao lowerboundsforquantuminspiredclassicalalgorithmsviacommunicationcomplexity