A variational toolbox for quantum multi-parameter estimation
Abstract With an ever-expanding ecosystem of noisy and intermediate-scale quantum devices, exploring their possible applications is a rapidly growing field of quantum information science. In this work, we demonstrate that variational quantum algorithms feasible on such devices address a challenge ce...
Main Authors: | Johannes Jakob Meyer, Johannes Borregaard, Jens Eisert |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2021-06-01
|
Series: | npj Quantum Information |
Online Access: | https://doi.org/10.1038/s41534-021-00425-y |
Similar Items
-
Exploiting Symmetry in Variational Quantum Machine Learning
by: Johannes Jakob Meyer, et al.
Published: (2023-03-01) -
Quantum memory assisted observable estimation
by: Liubov A. Markovich, et al.
Published: (2025-03-01) -
Encoding-dependent generalization bounds for parametrized quantum circuits
by: Matthias C. Caro, et al.
Published: (2021-11-01) -
EVOVAQ: EVOlutionary algorithms-based toolbox for VAriational Quantum circuits
by: Giovanni Acampora, et al.
Published: (2024-05-01) -
Potential and limitations of random Fourier features for dequantizing quantum machine learning
by: Ryan Sweke, et al.
Published: (2025-02-01)