no code implementations • 28 Jun 2023 • Casper Gyurik, Vedran Dunjko
Despite significant effort, the quantum machine learning community has only demonstrated quantum learning advantages for artificial cryptography-inspired datasets when dealing with classical data.
no code implementations • 31 May 2023 • Sofiene Jerbi, Casper Gyurik, Simon C. Marshall, Riccardo Molteni, Vedran Dunjko
Quantum machine learning is often highlighted as one of the most promising uses for a quantum computer to solve practical problems.
no code implementations • 12 Aug 2022 • Casper Gyurik, Vedran Dunjko
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.
no code implementations • 25 Mar 2022 • Simon C. Marshall, Casper Gyurik, Vedran Dunjko
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.
no code implementations • 12 May 2021 • Casper Gyurik, Dyon van Vreumingen, Vedran Dunjko
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.
no code implementations • NeurIPS 2021 • Sofiene Jerbi, Casper Gyurik, Simon C. Marshall, Hans J. Briegel, Vedran Dunjko
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.
no code implementations • 6 May 2020 • Casper Gyurik, Chris Cade, Vedran Dunjko
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.