Łukasz Cincio

Unified approach to data-driven quantum error mitigation

2020 · arXiv (Cornell University)

Achieving near-term quantum advantage will require effective methods for mitigating hardware noise. Data-driven approaches to error mitigation are promising, with popular examples including zero-noise extrapolation (ZNE) and Clifford data regression (CDR). Here we propose a …

Out-of-distribution generalization for learning quantum dynamics

2022 · arXiv (Cornell University)

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data …

Resource frugal optimizer for quantum machine learning

2022 · arXiv (Cornell University)

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 …

Variational Quantum Linear Solver

2019 · arXiv (Cornell University)

Previously proposed quantum algorithms for solving linear systems of equations cannot be implemented in the near term due to the required circuit depth. Here, we propose a hybrid quantum-classical algorithm, called Variational Quantum Linear Solver …