551 citations · 728 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Transfer learning in hybrid classical-quantum neural networks
Andrea Mari, Thomas R. Bromley, Josh Izaac +2
We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantu…
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…
Stochastic gradient descent for hybrid quantum-classical optimization
Ryan Sweke, Frederik Wilde, Johannes Meyer +4
Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of…
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…