Variational Quantum Pulse Learning

TL;DR

This paper introduces variational quantum pulses (VQP), a new approach that directly trains quantum pulses for machine learning, offering more control and improved accuracy over traditional variational quantum circuits, especially on noisy quantum hardware.

Contribution

The paper proposes VQP, a novel paradigm for training quantum pulses directly, enhancing flexibility and noise robustness compared to existing variational quantum algorithms.

Findings

VQP achieves up to 11% higher accuracy than VQC on noisy simulators.

VQP demonstrates stable and reliable results under noise conditions.

VQP outperforms VQC in binary classification tasks on real quantum hardware.

Abstract

Quantum computing is among the most promising emerging techniques to solve problems that are computationally intractable on classical hardware. A large body of existing works focus on using variational quantum algorithms on the gate level for machine learning tasks, such as the variational quantum circuit (VQC). However, VQC has limited flexibility and expressibility due to limited number of parameters, e.g. only one parameter can be trained in one rotation gate. On the other hand, we observe that quantum pulses are lower than quantum gates in the stack of quantum computing and offers more control parameters. Inspired by the promising performance of VQC, in this paper we propose variational quantum pulses (VQP), a novel paradigm to directly train quantum pulses for learning tasks. The proposed method manipulates variational quantum pulses by pulling and pushing the amplitudes of pulses in an optimization framework. Similar to variational quantum algorithms, our framework to train pulses maintains the robustness to noise on Noisy Intermediate-Scale Quantum (NISQ) computers. In an example task of binary classification, VQP learning achieves up to 11% and 9% higher accuracy compared with VQC learning on the qiskit noise simulators (with noise model from real machine) and ibmq-jarkata, respectively, demonstrating its effectiveness and feasibility. Stability for VQP to obtain reliable results has also been verified in the presence of noise.