A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

TL;DR

This paper introduces ST-VQC, a novel spatial-temporal variational quantum circuit designed to enhance deep learning capabilities on NISQ devices by integrating non-linearity and noise robustness, achieving significant accuracy improvements.

Contribution

It proposes a new quantum circuit architecture with spatial-temporal design and an automated optimization framework, addressing limitations of existing VQCs in complex, real-world datasets.

Findings

Achieves over 30% accuracy improvement on IBM quantum processors.

Outperforms existing VQCs and classical linear classifiers on synthetic datasets.

Demonstrates robustness and effectiveness of the proposed design in practical quantum computing environments.

Abstract

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits(VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What's more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely ST-VQC, to integrate non-linearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm_cairo with 27 qubits and ibmq_lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the ST-VQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.