Resource frugal optimizer for quantum machine learning
arXiv:2211.04965 · doi:10.1088/2058-9565/acef55
Abstract
Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms have the potential to solve practical problems on real hardware, particularly when involving quantum data. However, training these algorithms can be challenging and calls for tailored optimization procedures. Specifically, QML applications can require a large shot-count overhead due to the large datasets involved. In this work, we advocate for simultaneous random sampling over both the dataset as well as the measurement operators that define the loss function. We consider a highly general loss function that encompasses many QML applications, and we show how to construct an unbiased estimator of its gradient. This allows us to propose a shot-frugal gradient descent optimizer called Refoqus (REsource Frugal Optimizer for QUantum Stochastic gradient descent). Our numerics indicate that Refoqus can save several orders of magnitude in shot cost, even relative to optimizers that sample over measurement operators alone.
22 pages, 6 figures - extra quantum autoencoder results added - extra affiliation
References in corpus (7)
- An introduction to quantum machine learning
- The quest for a Quantum Neural Network
- Hybrid quantum-classical algorithms and quantum error mitigation
- Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
- Analyzing variational quantum landscapes with information content
- Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks
- Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm