Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
Quantum machine learning has emerged as a potential practical application of near-term quantum devices. In this work, we study a two-layer hybrid classical-quantum classifier in which a first layer of quantum stochastic neurons implementing generalized linear models (QGLMs) is followed by a second classical combining layer. The input to the first, hidden, layer is obtained via amplitude encoding in order to leverage the exponential size of the fan-in of the quantum neurons in the number of qubits per neuron. To facilitate implementation of the QGLMs, all weights and activations are binary. While the state of the art on training strategies for this class of models is limited to exhaustive search and single-neuron perceptron-like bit-flip strategies, this letter introduces a stochastic variational optimization approach that enables the joint training of quantum and classical layers via stochastic gradient descent. Experiments show the advantages of the approach for a variety of activation functions implemented by QGLM neurons.
Code (0)
등록된 구현이 없습니다.
Tasks
Quantum Machine LearningSimilar Papers 제목 키워드 기반
Continuous Variable Quantum MNIST Classifiers
In this paper, classical and continuous variable (CV) quantum neural network hybrid multiclassifiers are presented using the MNIST dataset. The combination of cutoff dimension and probability measurement method in the CV…
Hybrid Quantum-Classical Stochastic Networks with Boltzmann Layers
Quantum Machine Learning (QML) has the potential to significantly advance the state-of-the-art in artificial intelligence, due to recent developments in quantum computing hardware and algorithm design. Particularly, an …
Quantum Machine LearningHybrid Quantum-Classical Machine Learning for Sentiment Analysis
The collaboration between quantum computing and classical machine learning offers potential advantages in natural language processing, particularly in the sentiment analysis of human emotions and opinions expressed in la…
Dimensionality ReductionSentiment AnalysisStochastic gradient descent for hybrid quantum-classical optimization
Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of parameterized quantum circuits. In this…
Hybrid Classical-Quantum Transfer Learning with Noisy Quantum Circuits
Quantum transfer learning combines pretrained classical deep learning models with quantum circuits to reuse expressive feature representations while limiting the number of trainable parameters. In this work, we introduce…
Computational EfficiencyImage ClassificationTransfer Learning