
We present modular and optimal architectures for implementing arbitrary discrete unitary transformations on light. These architectures are based on systematically combining smaller Mmode linear optical interferometers together to implement a larger Nmode transformation.
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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 quantum elements.
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Gaussian Boson Sampling (GBS) is a nearterm platform for photonic quantum computing. Recent efforts have led to the discovery of GBS algorithms with applications to graphbased problems, point processes, and molecular vibronic spectra in chemistry.
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We experimentally demonstrate stimulated fourwave mixing in two linearly uncoupled integrated Si3N4 microresonators. In our structure the resonance combs of each resonator can be tuned independently…
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A duality at the heart of Gaussian boson sampling
October 2019
Gaussian boson sampling (GBS) is a nearterm quantum computation framework that is believed to be classically intractable, but yet rich of potential applications.
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Encoding a qubit in the continuous degrees of freedom of an oscillator is a significant pursuit of quantum computation. One advantageous way to achieve this is through the GottesmanKitaevPreskill (GKP) grid states, whose symmetries allow for the correction of any small continuous error on the oscillator.
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Quantum Natural Gradient
September 2019
A quantum generalization of Natural Gradient Descent is presented as part of a generalpurpose optimization framework for variational quantum circuits.
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We introduce an exact classical algorithm for simulating Gaussian Boson Sampling (GBS). The complexity of the algorithm is exponential in the number of photons detected, which is itself a random variable. For a fixed number of modes, the complexity is in fact equivalent to that of calculating output probabilities, up to constant prefactors.
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Random point patterns are ubiquitous in nature, and statistical models such as point processes, i.e., algorithms that generate stochastic collections of points, are commonly used to simulate and interpret them.
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Stochastic models are highly relevant tools in science, engineering, and society. Recent work suggests emerging quantum computing technologies can substantially decrease the memory requirements for simulating stochastic models.
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A device called a `Gaussian Boson Sampler’ has initially been proposed as a nearterm demonstration of classically intractable quantum computation. As recently shown, it can also be used to decide whether two graphs are isomorphic.
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As a promising candidate for exhibiting quantum computational supremacy, Gaussian boson sampling (GBS) is designed to exploit the ease of experimental preparation of Gaussian states. In this work, we establish sufficient conditions for efficient approximate simulation of GBS under the effect of errors such as photon losses and dark counts.
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