Realizing Quantum Kernel Models at Scale with Matrix Product State Simulation

TL;DR

This paper demonstrates scalable quantum kernel modeling using Matrix Product State simulation, enabling classification with large feature sets and training data, and compares CPU and GPU performance for quantum simulations.

Contribution

It introduces a scalable MPS-based quantum kernel framework and provides the first evidence of quantum model performance at large scale.

Findings

Quantum kernel performance improves with larger feature dimensions.

GPU implementation outperforms CPU beyond a certain qubit interaction distance.

Successful classification with 165 features and 6400 data points.

Abstract

Data representation in quantum state space offers an alternative function space for machine learning tasks. However, benchmarking these algorithms at a practical scale has been limited by ineffective simulation methods. We develop a quantum kernel framework using a Matrix Product State (MPS) simulator and employ it to perform a classification task with 165 features and 6400 training data points, well beyond the scale of any prior work. We make use of a circuit ansatz on a linear chain of qubits with increasing interaction distance between qubits. We assess the MPS simulator performance on CPUs and GPUs and, by systematically increasing the qubit interaction distance, we identify a crossover point beyond which the GPU implementation runs faster. We show that quantum kernel model performance improves as the feature dimension and training data increases, which is the first evidence of quantum model performance at scale.