Robust quantum classifier with minimal overhead

TL;DR

This paper presents a resource-efficient quantum binary classifier that minimizes quantum circuit overhead, optimizes data encoding, and operates reliably under noise without error correction, advancing practical quantum machine learning.

Contribution

It introduces methods to reduce quantum data encoding repetitions, achieves optimal variance with simple measurements, and demonstrates noise robustness for quantum classification.

Findings

Optimal number of repetitions for expectation value estimation calculated.

Single-qubit measurement suffices for kernel-based classification.

Reliable classification possible under certain noise models without error correction.

Abstract

To witness quantum advantages in practical settings, substantial efforts are required not only at the hardware level but also on theoretical research to reduce the computational cost of a given protocol. Quantum computation has the potential to significantly enhance existing classical machine learning methods, and several quantum algorithms for binary classification based on the kernel method have been proposed. These algorithms rely on estimating an expectation value, which in turn requires an expensive quantum data encoding procedure to be repeated many times. In this work, we calculate explicitly the number of repetition necessary for acquiring a fixed success probability and show that the Hadamard-test and the swap-test circuits achieve the optimal variance in terms of the quantum circuit parameters. The variance, and hence the number of repetition, can be further reduced only via optimization over data-related parameters. We also show that the kernel-based binary classification can be performed with a single-qubit measurement regardless of the number and the dimension of the data. Finally, we show that for a number of relevant noise models the classification can be performed reliably without quantum error correction. Our findings are useful for designing quantum classification experiments under limited resources, which is the common challenge in the noisy intermediate-scale quantum era.