Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
arXiv:2311.11412 · doi:10.1103/PhysRevA.110.022411
Abstract
Quantum embedding is a fundamental prerequisite for applying quantum machine learning techniques to classical data, and has substantial impacts on performance outcomes. In this study, we present Neural Quantum Embedding (NQE), a method that efficiently optimizes quantum embedding beyond the limitations of positive and trace-preserving maps by leveraging classical deep learning techniques. NQE enhances the lower bound of the empirical risk, leading to substantial improvements in classification performance. Moreover, NQE improves robustness against noise. To validate the effectiveness of NQE, we conduct experiments on IBM quantum devices for image data classification, resulting in a remarkable accuracy enhancement from 0.52 to 0.96. In addition, numerical analyses highlight that NQE simultaneously improves the trainability and generalization performance of quantum neural networks, as well as of the quantum kernel method.
18 pages, 13 figures
References in corpus (43)
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum Machine Learning
- Variational Quantum Algorithms
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Supervised learning with quantum enhanced feature spaces
- Quantum computational advantage using photons
- Barren plateaus in quantum neural network training landscapes
- Quantum support vector machine for big data classification
- Quantum machine learning in feature Hilbert spaces
- Quantum Convolutional Neural Networks
- Quantum fingerprinting
- Parameterized quantum circuits as machine learning models
- Circuit-centric quantum classifiers
- Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
- Quantum Computational Supremacy
- The effect of data encoding on the expressive power of variational quantum machine learning models
- Power of data in quantum machine learning
- Challenges and Opportunities in Quantum Machine Learning
- Connecting ansatz expressibility to gradient magnitudes and barren plateaus
- A rigorous and robust quantum speed-up in supervised machine learning
- Transfer learning in hybrid classical-quantum neural networks
- Quantum convolutional neural network for classical data classification
- Robust data encodings for quantum classifiers
- Quantum state discrimination and its applications
- Average-case complexity versus approximate simulation of commuting quantum computations
- Quantum machine learning beyond kernel methods
- Exploiting symmetry in variational quantum machine learning
- Quantum Sampling Problems, BosonSampling and Quantum Supremacy
- A divide-and-conquer algorithm for quantum state preparation
- Group-Invariant Quantum Machine Learning
- Training Quantum Embedding Kernels on Near-Term Quantum Computers
- Quantum singular value decomposition of non-sparse low-rank matrices
- Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection
- Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
- Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
- Classical-to-quantum convolutional neural network transfer learning
- Analysis and synthesis of feature map for kernel-based quantum classifier
- A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage
- Circuit-based quantum random access memory for classical data with continuous amplitudes
- Configurable sublinear circuits for quantum state preparation
- Variational Quantum Approximate Support Vector Machine with Inference Transfer
- Interpolating between positive and completely positive maps: a new hierarchy of entangled states