2 citations · 3 across the 2 of their papers we have counts for
6 papers
On quantum ensembles of quantum classifiers
Amira Abbas, Maria Schuld, Francesco Petruccione
Quantum machine learning seeks to exploit the underlying nature of a quantum computer to enhance machine learning techniques. A particular framework uses the quantum property of su…
Applications of Near-Term Photonic Quantum Computers: Software and Algorithms
Thomas R. Bromley, Juan Miguel Arrazola, Soran Jahangiri +7
Gaussian Boson Sampling (GBS) is a near-term platform for photonic quantum computing. Recent efforts have led to the discovery of GBS algorithms with applications to graph-based pr…
A duality at the heart of Gaussian boson sampling
Kamil Bradler, Robert Israel, Maria Schuld +1
Gaussian boson sampling (GBS) is a near-term quantum computation framework that is believed to be classically intractable, but yet rich of potential applications. In this paper we…
A quantum hardware-induced graph kernel based on Gaussian Boson Sampling
Maria Schuld, Kamil Brádler, Robert Israel +2
A device called a 'Gaussian Boson Sampler' has initially been proposed as a near-term demonstration of classically intractable quantum computation. As recently shown, it can also b…
Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer +5
Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent…
Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin +2
An important application for near-term quantum computing lies in optimization tasks, with applications ranging from quantum chemistry and drug discovery to machine learning. In man…