High-speed integrated QKD system

Quantum key distribution(QKD)is nowadays a well-established method for generating secret keys at a distance in an information-theoretically secure way,as the secrecy of QKD relies on the laws of quantum physics and not on computational complexity.In order to industrialize QKD,low-cost,mass-manufactured,and practical QKD setups are required.Hence,photonic and electronic integration of the sender's and receiver's respective components is currently in the spotlight.Here we present a high-speed(2.5 GHz)integrated QKD setup featuring a transmitter chip in silicon photonics allowing for high-speed modulation and accurate state preparation,as well as a polarization-independent low-loss receiver chip in aluminum borosilicate glass fabricated by the femtosecond laser micromachining technique.Our system achieves raw bit error rates,quantum bit error rates,and secret key rates equivalent to a much more complex state-of-the-art setup based on discrete components[A.Boaron et al.,Phys.Rev.Lett.121,190502(2018)].

Deep reinforcement learning for quantum multiparameter estimation

Estimation of physical quantities is at the core of most scientific research,and the use of quantum devices promises to enhance its performances.In real scenarios,it is fundamental to consider that resources are limited,and Bayesian adaptive estimation represents a powerful approach to efficiently allocate,during the estimation process,all the available resources.However,this framework relies on the precise knowledge of the system model,retrieved with a fine calibration,with results that are often computationally and experimentally demanding.We introduce a model-free and deep-learning-based approach to efficiently implement realistic Bayesian quantum metrology tasks accomplishing all the relevant challenges,without relying on any a priori knowledge of the system.To overcome this need,a neural network is trained directly on experimental data to learn the multiparameter Bayesian update.Then the system is set at its optimal working point through feedback provided by a reinforcement learning algorithm trained to reconstruct and enhance experiment heuristics of the investigated quantum sensor.Notably,we prove experimentally the achievement of higher estimation performances than standard methods,demonstrating the strength of the combination of these two black-box algorithms on an integrated photonic circuit.Our work represents an important step toward fully artificial intelligence-based quantum metrology.

Thermally reconfigurable quantum photonic circuits at telecom wavelength by femtosecond laser micromachining

The importance of integrated quantum photonics in the telecom band is based on the possibility of interfacing with the optical network infrastructure that was developed for classical communications.In this framework,femtosecond laser-written integrated photonic circuits,which have already been assessed for use in quantum information experiments in the 800-nm wavelength range,have great potential.In fact,these circuits,being written in glass,can be perfectly mode-matched at telecom wavelength to the in/out coupling fibers,which is a key requirement for a low-loss processing node in future quantum optical networks.In addition,for several applications,quantum photonic devices must be dynamically reconfigurable.Here,we experimentally demonstrate the high performance of femtosecond laser-written photonic circuits for use in quantum experiments in the telecom band,and we demonstrate the use of thermal shifters,which were also fabricated using the same femtosecond laser,to accurately tune such circuits.State-of-the-art manipulation of single-and two-photon states is demonstrated,with fringe visibilities greater than 95%.The results of this work open the way to the realization of reconfigurable quantum photonic circuits based on this technological platform.

Path-polarization hyperentangled and cluster states of photons on a chip

Encoding many qubits in different degrees of freedom(DOFs)of single photons is one of the routes toward enlarging the Hilbert space spanned by a photonic quantum state.Hyperentangled photon states(that is,states showing entanglement in multiple DOFs)have demonstrated significant implications for both fundamental physics tests and quantum communication and computation.Increasing the number of qubits of photonic experiments requires miniaturization and integration of the basic elements,and functions to guarantee the setup stability,which motivates the development of technologies allowing the precise control of different photonic DOFs on a chip.We demonstrate the contextual use of path and polarization qubits propagating within an integrated quantum circuit.We tested the properties of four-qubit linear cluster states built on both DOFs,and we exploited them to perform the Grover's search algorithm according to the one-way quantum computation model.Our results pave the way toward the full integration on a chip of hybrid multi-qubit multiphoton states.

Particle focusing by 3D inertial microfluidics

Three-dimensional(3D)particle focusing in microfluidics is a fundamental capability with a wide range of applications,such as on-chip flow cytometry,where high-throughput analysis at the single-cell level is performed.Currently,3D focusing is achieved mainly in devices with complex layouts,additional sheath fluids,and complex pumping systems.In this work,we present a compact microfluidic device capable of 3D particle focusing at high flow rates and with a small footprint,without the requirement of external fields or lateral sheath flows,but using only a single-inlet,single-outlet microfluidic sequence of straight channels and tightly curving vertical loops.This device exploits inertial fluidic effects that occur in a laminar regime at sufficiently high flow rates,manipulating the particle positions by the combination of inertial lift forces and Dean drag forces.The device is fabricated by femtosecond laser irradiation followed by chemical etching,which is a simple two-step process enabling the creation of 3D microfluidic networks in fused silica glass substrates.The use of tightly curving three-dimensional microfluidic loops produces strong Dean drag forces along the whole loop but also induces an asymmetric Dean flow decay in the subsequent straight channel,thus producing rapid cross-sectional mixing flows that assist with 3D particle focusing.The use of out-of-plane loops favors a compact parallelization of multiple focusing channels,allowing one to process large amounts of samples.In addition,the low fluidic resistance of the channel network is compatible with vacuum driven flows.The resulting device is quite interesting for high-throughput on-chip flow cytometry.

Optimal photonic indistinguishability tests in multimode networks

Particle indistinguishability is at the heart of quantum statistics that regulates fundamental phenomena such as the electronic band structure of solids, Bose-Einstein condensation and superconductivity.Moreover, it is necessary in practical applications such as linear optical quantum computation and simulation, in particular for Boson Sampling devices.It is thus crucial to develop tools to certify genuine multiphoton interference between multiple sources.Our approach employs the total variation distance to find those transformations that minimize the error probability in discriminating the behaviors of distinguishable and indistinguishable photons.In particular, we show that so-called Sylvester interferometers are near-optimal for this task.By using Bayesian tests and inference, we numerically show that Sylvester transformations largely outperform most Haar-random unitaries in terms of sample size required.Furthermore, we experimentally demonstrate the efficacy of the transformation using an efficient 3 D integrated circuits in the single-and multiple-source cases.We then discuss the extension of this approach to a larger number of photons and modes.These results open the way to the application of Sylvester interferometers for optimal assessment of multiphoton interference experiments.

Photonic implementation of boson sampling:a review

Boson sampling is a computational problem that has recently been proposed as a candidate to obtain an unequivocal quantum computational advantage.The problem consists in sampling from the output distribution of indistinguishable bosons in a linear interferometer.There is strong evidence that such an experiment is hard to classically simulate,but it is naturally solved by dedicated photonic quantum hardware,comprising single photons,linear evolution,and photodetection.This prospect has stimulated much effort resulting in the experimental implementation of progressively larger devices.We review recent advances in photonic boson sampling,describing both the technological improvements achieved and the future challenges.We also discuss recent proposals and implementations of variants of the original problem,theoretical issues occurring when imperfections are considered,and advances in the development of suitable techniques for validation of boson sampling experiments.We conclude by discussing the future application of photonic boson sampling devices beyond the original theoretical scope.