
The excitation of vibrational modes in molecules affects the outcome of chemical reactions, for example by providing molecules with sufficient energy to overcome activation barriers. In this work, we introduce a quantum algorithm for simulating molecular vibrational excitations during vibronic transitions. We discuss how a specialpurpose quantum computer can be programmed with molecular data to […]
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Parametrized quantum optical circuits are a class of quantum circuits in which the carriers of quantum information are photons and the gates are optical transformations. Optimizing these circuits is challenging due to the infinite dimensionality of the photon number vector space that is associated to each optical mode. Truncating the space dimension is unavoidable, and […]
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Gaussian Boson Sampling (GBS) is a nearterm platform for photonic quantum computing. Applications have been developed which rely on directly programming GBS devices, but the ability to train and optimize circuits has been a key missing ingredient for developing new algorithms. In this work, we derive analytical gradient formulas for the GBS distribution, which can […]
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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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