Shot Optimization in Quantum Machine Learning Architectures to Accelerate Training

TL;DR

This paper introduces shot optimization techniques for quantum machine learning models that significantly reduce the number of quantum measurements needed, thereby accelerating training with minimal impact on accuracy.

Contribution

It proposes adaptive shot allocation methods, including linear and step functions, to optimize quantum measurement shots during training, improving efficiency over traditional fixed shot approaches.

Findings

Training full datasets yields 5-6% higher accuracy but requires 10X more shots.

Adaptive shot reduction causes minimal accuracy loss (~4%) with up to 100X fewer shots.

Step function shot reduction provides the most stable results in quantum energy estimation.

Abstract

In this paper, we propose shot optimization method for QML models at the expense of minimal impact on model performance. We use classification task as a test case for MNIST and FMNIST datasets using a hybrid quantum-classical QML model. First, we sweep the number of shots for short and full versions of the dataset. We observe that training the full version provides 5-6% higher testing accuracy than short version of dataset with up to 10X higher number of shots for training. Therefore, one can reduce the dataset size to accelerate the training time. Next, we propose adaptive shot allocation on short version dataset to optimize the number of shots over training epochs and evaluate the impact on classification accuracy. We use a (a) linear function where the number of shots reduce linearly with epochs, and (b) step function where the number of shots reduce in step with epochs. We note around 0.01 increase in loss and around 4% (1%) reduction in testing accuracy for reduction in shots by up to 100X (10X) for linear (step) shot function compared to conventional constant shot function for MNIST dataset, and 0.05 increase in loss and around 5-7% (5-7%) reduction in testing accuracy with similar reduction in shots using linear (step) shot function on FMNIST dataset. For comparison, we also use the proposed shot optimization methods to perform ground state energy estimation of different molecules and observe that step function gives the best and most stable ground state energy prediction at 1000X less number of shots.