Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

Importance of kernel bandwidth in quantum machine learning

Journal Article · · Physical Review A
 [1];  [2]
  1. Global Technology Applied Research, New York, NY (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
  2. Argonne National Laboratory (ANL), Argonne, IL (United States)

Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. Identifying hyperparameters controlling the inductive bias of quantum machine learning models is expected to be crucial given the central role hyperparameters play in determining the performance of classical machine learning methods. In this work we introduce the hyperparameter controlling the bandwidth of a quantum kernel and show that it controls the expressivity of the resulting model. We use extensive numerical experiments with multiple quantum kernels and classical data sets to show consistent change in the model behavior from underfitting (bandwidth too large) to overfitting (bandwidth too small), with optimal generalization in between. We draw a connection between the bandwidth of classical and quantum kernels and show analogous behavior in both cases. Furthermore, we show that optimizing the bandwidth can help mitigate the exponential decay of kernel values with qubit count, which is the cause behind recent observations that the performance of quantum kernel methods decreases with qubit count. Here, we reproduce these negative results and show that if the kernel bandwidth is optimized, the performance instead improves with growing qubit count and becomes competitive with the best classical methods.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
AC02-05CH11231; AC02-06CH11357
OSTI ID:
2001238
Journal Information:
Physical Review A, Journal Name: Physical Review A Journal Issue: 4 Vol. 106; ISSN 2469-9926
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

References (15)

Qiskit: An Open-source Framework for Quantum Computing software January 2019
The Elements of Statistical Learning book January 2009
Computing a nearest symmetric positive semidefinite matrix journal May 1988
Kernel Methods for Pattern Analysis book January 2011
Kernel Methods and Machine Learning book January 2014
Power of data in quantum machine learning journal May 2021
Machine learning of high dimensional data on a noisy quantum processor journal November 2021
A rigorous and robust quantum speed-up in supervised machine learning journal July 2021
Supervised learning with quantum-enhanced feature spaces journal March 2019
Quantum supremacy using a programmable superconducting processor journal October 2019
Floating point representations in quantum circuit synthesis journal September 2013
Generalization in Quantum Machine Learning: A Quantum Information Standpoint journal November 2021
Effect of data encoding on the expressive power of variational quantum-machine-learning models journal March 2021
Quantum Machine Learning in Feature Hilbert Spaces journal February 2019
Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer journal January 1999

Similar Records

Numerical evidence against advantage with quantum fidelity kernels on classical data
Journal Article · Tue Jun 20 00:00:00 EDT 2023 · Physical Review A · OSTI ID:2008310

Exponential concentration in quantum kernel methods
Journal Article · Tue Jun 18 00:00:00 EDT 2024 · Nature Communications · OSTI ID:2483516