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Quantum-Classical Separations in Shallow-Circuit-Based Learning with and without Noises
In addition, for quantum devices with constant noise strength, we prove that no super-polynomial classical-quantum separation exists for any classification task defined by shallow Clifford circuits, independent of the structures of the circuits that specify the learning models.
Efficient Pauli channel estimation with logarithmic quantum memory
Prior work (Chen et al., 2022) proved no-go theorems for this task in the practical regime where one has a limited amount of quantum memory, e. g. any protocol with $\le 0. 99n$ ancilla qubits of quantum memory must make exponentially many measurements, provided it is non-concatenating.
Enhancing Quantum Adversarial Robustness by Randomized Encodings
To address this issue, we propose a general scheme to protect quantum learning systems from adversarial attacks by randomly encoding the legitimate data samples through unitary or quantum error correction encoders.
Weighted Quantum Channel Compiling through Proximal Policy Optimization
We propose a general and systematic strategy to compile arbitrary quantum channels without using ancillary qubits, based on proximal policy optimization -- a powerful deep reinforcement learning algorithm.
Universal Adversarial Examples and Perturbations for Quantum Classifiers
Through concrete examples involving classifications of real-life images and quantum phases of matter, we show that there exist universal adversarial examples that can fool a set of different quantum classifiers.
