Adding controlled noise from a simulated time crystal improved fitting accuracy for two quantum neural network variants while degrading quantum reservoir computing, in small numerical tests.
Generalized quantum subspace expansion
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
One of the major challenges for erroneous quantum computers is undoubtedly the control over the effect of noise. Considering the rapid growth of available quantum resources that are not fully fault-tolerant, it is crucial to develop practical hardware-friendly quantum error mitigation (QEM) techniques to suppress unwanted errors. Here, we propose a novel generalized quantum subspace expansion method which can handle stochastic, coherent, and algorithmic errors in quantum computers. By fully exploiting the substantially extended subspace, we can efficiently mitigate the noise present in the spectra of a given Hamiltonian, without relying on any information of noise. The performance of our method is discussed under two highly practical setups: the quantum subspaces are mainly spanned by powers of the noisy state $\rho^m$ and a set of error-boosted states, respectively. We numerically demonstrate in both situations that we can suppress errors by orders of magnitude, and show that out protocol inherits the advantages of previous error-agnostic QEM techniques as well as overcoming their drawbacks.
citation-role summary
citation-polarity summary
fields
quant-ph 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
The effect of Quantum Time Crystal Computing to Quantum Machine Learning methods
Adding controlled noise from a simulated time crystal improved fitting accuracy for two quantum neural network variants while degrading quantum reservoir computing, in small numerical tests.