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Exponential separations between classical and quantum learners
Despite significant effort, the quantum machine learning community has only demonstrated quantum learning advantages for artificial cryptography-inspired datasets when dealing with classical data.
Shadows of quantum machine learning
Quantum machine learning is often highlighted as one of the most promising uses for a quantum computer to solve practical problems.
On establishing learning separations between classical and quantum machine learning with classical data
Despite years of effort, the quantum machine learning community has only been able to show quantum learning advantages for certain contrived cryptography-inspired datasets in the case of classical data.
High Dimensional Quantum Machine Learning With Small Quantum Computers
In an attempt to placate this limitation techniques can be applied for evaluating a quantum circuit using a machine with fewer qubits than the circuit naively requires.
Structural risk minimization for quantum linear classifiers
Firstly, using relationships to well understood classical models, we prove that two model parameters -- i. e., the dimension of the sum of the images and the Frobenius norm of the observables used by the model -- closely control the models' complexity and therefore its generalization performance.
Parametrized quantum policies for reinforcement learning
With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system is gaining increasing traction.
Towards quantum advantage via topological data analysis
Our results provide a number of useful applications for full-blown, and restricted quantum computers with a guaranteed exponential speedup over classical methods, recovering some of the potential for linear-algebraic QML to become one of quantum computing's killer applications.
