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Found 7 papers, 1 papers with code
Sample complexity of quantum hypothesis testing
By making use of the wealth of knowledge that already exists in the literature on quantum hypothesis testing, we characterize the sample complexity of binary quantum hypothesis testing in the symmetric and asymmetric settings, and we provide bounds on the sample complexity of multiple quantum hypothesis testing.
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
Efficient verification of continuous-variable quantum states and devices without assuming identical and independent operations
To successfully demonstrate these protocols, an essential step is the certification of multimode continuous-variable quantum states and quantum devices.
Quantum Physics
Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing
This link leads to a tight robustness condition which puts constraints on the amount of noise a classifier can tolerate, independent of whether the noise source is natural or adversarial.
Quantum noise protects quantum classifiers against adversaries
This robustness property is intimately connected with an important security concept called differential privacy which can be extended to quantum differential privacy.
Data Sanity Check for Deep Learning Systems via Learnt Assertions
But the statistical nature of DL makes it quite vulnerable to invalid inputs, i. e., those cases that are not considered in the training phase of a DL model.
Vulnerability of quantum classification to adversarial perturbations
Furthermore, this leads to a trade-off between the security of the classification algorithm against adversarial attacks and quantum advantages we expect for high-dimensional problems.
Quantum Physics
