Robust quantum classifier with minimal overhead

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.

arXiv comment7 pages, 6 figures

References in corpus (17)

• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum Machine Learning
• Quantum algorithm for solving linear systems of equations
• Supervised learning with quantum enhanced feature spaces
• Quantum support vector machine for big data classification
• Quantum machine learning in feature Hilbert spaces
• Quantum Error Correction for Quantum Memories
• Quantum random access memory
• Quantum machine learning: a classical perspective
• Benchmarking an 11-qubit quantum computer
• Efficient Simulation of Quantum Systems by Quantum Computers
• Implementing a distance-based classifier with a quantum interference circuit
• Circuit-Based Quantum Random Access Memory for Classical Data
• The theory of the quantum kernel-based binary classifier
• Circuit-based quantum random access memory for classical data with continuous amplitudes
• Parallel quantum trajectories via forking for sampling without redundancy

Cited by in corpus (3)

• A Multi-Class SWAP-Test Classifier
• Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey
• Multi-channel convolutional neural quantum embedding