We report the generation of broadband single-mode (degenerate) quadrature squeezed vacuum from an integrated nanophotonic device based on two coupled silicon nitride microring resonators. Dual-pump spontaneous four-wave mixing in one microring resonator is exploited to generate squeezed light, while unwanted single-pump parametric fluorescence and Bragg-scattering four-wave mixing processes are suppressed by selectively corrupting individual resonances […]

We present modular and optimal architectures for implementing arbitrary discrete unitary transformations on light. These architectures are based on systematically combining smaller M-mode linear optical interferometers together to implement a larger N-mode transformation.

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 high-dimensional Hilbert space; the second part of the circuit executes a quantum measurement interpreted as the output of the model. Usually, […]

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.

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 beam-splitters. The method allows for using universal linear optical interferometers with […]

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 problems, point processes, and molecular vibronic spectra in chemistry.

We experimentally demonstrate stimulated four-wave mixing in two linearly uncoupled integrated Si3N4 micro-resonators. In our structure the resonance combs of each resonator can be tuned independently…

Gaussian boson sampling (GBS) is a near-term quantum computation framework that is believed to be classically intractable, but yet rich of potential applications.

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 Gottesman-Kitaev-Preskill (GKP) grid states, whose symmetries allow for the correction of any small continuous error on the oscillator.

A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits.

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.

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.