Quantum learning robust against noise for Physical Review A - AMO by Andrew W. Cross et al.
In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and ...
We have introduced new concepts in quantum sensing of the power spectral density of a signal, able to considerably speed up the sensing protocols and make them more robust against noise affecting the probe and the signal. In detail, we have introduced the concept of filter orthogonalization allowi...
Initialization of composite quantum systems into highly entangled states is usually a must to enable their use for quantum technologies. However, unavoidable noise in the preparation stage makes the system state mixed, hindering this goal. Here, we addre
It has also been hypothesized that they may be more robust to hardware noise than conventional algorithms due to their hybrid nature. However, the effect of training quantum machine learning models under the influence of hardware-induced noise has not yet been extensively studied. In this work, ...
Using Qiskit Pulse and Q-CTRL’s Boulder Opal to run error-robust quantum gates on a five-qubit IBM Quantum Canary processor delivering better value for users.
Atos is teaming up with the UK’s Science & Technology Facilities Council (SFTC) Hartree Centre to offer cloud access to theAtos Quantum Learning Machine. This is a high performance classically based simulator that can simulate up to 38 qubits and can include quantum noise models to understand ...
Inspired lightweight robust quantum Q-learning for smart generation control of power systems Linfei Yin, Xinghui Cao December 2022 Article 109804 select article An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell ...
It has been shown that under suitable conditions, distribution of entanglement via separable state has advantages in the presence of noise [69]. Recently, this method has been extended to distribute EPR steering [70], which is stronger than entanglement and has also been identified as a valuable...
A. Quantum learning robust against noise. Phys. Rev. A 92, 012327, https://doi.org/10.1103/PhysRevA.92.012327 (2015). Article ADS CAS Google Scholar Ristè, D. et al. Demonstration of quantum advantage in machine learning. npj Quantum Information 3, 16, https://doi.org/10.1038/s41534...