
Singlemode quadrature squeezing using dualpump fourwave mixing in an integrated nanophotonic device
January 2020
We report the generation of broadband singlemode (degenerate) quadrature squeezed vacuum from an integrated nanophotonic device based on two coupled silicon nitride microring resonators. Dualpump spontaneous fourwave mixing in one microring resonator is exploited to generate squeezed light, while unwanted singlepump parametric fluorescence and Braggscattering fourwave mixing processes are suppressed by selectively corrupting individual resonances […]
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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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Quantum embeddings for machine learning
January 2020
Quantum classifiers are trainable quantum circuits used as machine learning models. The first part of the circuit implements a quantum feature map that encodes classical inputs into quantum states, embedding the data in a highdimensional Hilbert space; the second part of the circuit executes a quantum measurement interpreted as the output of the model. Usually, […]
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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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A crucial challenge to the scaling up of linear optical interferometers is the presence of defective optical components resulting from inevitable imperfections in fabrication and packaging. This work presents a method for circumventing such defective components including lossy modes and unresponsive phase shifters and beamsplitters. The method allows for using universal linear optical interferometers with […]
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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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