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Found 11 papers, 1 papers with code
Quantum Adversarial Learning for Kernel Methods
We show that hybrid quantum classifiers based on quantum kernel methods and support vector machines are vulnerable against adversarial attacks, namely small engineered perturbations of the input data can deceive the classifier into predicting the wrong result.
Accuracy vs Memory Advantage in the Quantum Simulation of Stochastic Processes
Many inference scenarios rely on extracting relevant information from known data in order to make future predictions.
Statistical Complexity of Quantum Learning
Recent years have seen significant activity on the problem of using data for the purpose of learning properties of quantum systems or of processing classical or quantum data via quantum computing.
Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating
Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNN), as it facilitates retention of past information necessary to predict the future, providing a mechanism that preserves invariance to time warping transformations.
Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels
Simulating quantum channels is a fundamental primitive in quantum computing, since quantum channels define general (trace-preserving) quantum operations.
Generalization in Quantum Machine Learning: a Quantum Information Perspective
Here we establish a link between quantum machine learning classification and quantum hypothesis testing (state and channel discrimination) and then show that the accuracy and generalization capability of quantum classifiers depend on the (R\'enyi) mutual informations $I(C{:}Q)$ and $I_2(X{:}Q)$ between the quantum state space $Q$ and the classical parameter space $X$ or class space $C$.
Ultimate Limits of Thermal Pattern Recognition
Quantum Channel Discrimination (QCD) presents a fundamental task in quantum information theory, with critical applications in quantum reading, illumination, data-readout and more.
Quantum-enhanced barcode decoding and pattern recognition
Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of binary photonic protocols, e. g., for target detection or memory cell readout.
Noise-Resilient Variational Hybrid Quantum-Classical Optimization
Variational hybrid quantum-classical optimization represents one of the most promising avenue to show the advantage of nowadays noisy intermediate-scale quantum computers in solving hard problems, such as finding the minimum-energy state of a Hamiltonian or solving some machine-learning tasks.
Supervised learning of time-independent Hamiltonians for gate design
We show how the same conditions can be used to solve the problem numerically, via supervised learning techniques.
Quantum Physics
Supervised quantum gate "teaching" for quantum hardware design
We show how to train a quantum network of pairwise interacting qubits such that its evolution implements a target quantum algorithm into a given network subset.
