arXiv subjects

Simone Piacentini

Publications and source records attributed to Simone Piacentini.

At least 19 recordsLinked to original sources

arXiv2025

Time-series forecasting with multiphoton quantum states and integrated photonics

Quantum machine learning algorithms have very recently attracted significant attention in photonic platforms. In particular, reconfigurable integrated photonic circuits offer a promising route, thanks to the possibility of implementing adaptive feedback loops, which is an essential ingredient for achieving the necessary nonlinear behavior characteristic of neural networks. Here, we implement a quantum reservoir computing protocol in which information is processed through a reconfigurable linear optical integrated photonic circuit and measured using single-photon detectors. We exploit a multiphoton-based setup for time-series forecasting tasks in a variety of scenarios, where the input signal is encoded in one of the circuit's optical phases, thus modulating the quantum reservoir state. The resulting output probabilities are used to set the feedback phases and, at the end of the computation, are fed to a classical digital layer trained via linear regression to perform predictions. We then focus on the investigation of the role of input photon indistinguishability in the reservoir's capabilities of predicting time-series. We experimentally demonstrate that two-photon indistinguishable input states lead to significantly better performance compared to distinguishable ones. This enhancement arises from the quantum correlations present in indistinguishable states, which enable the system to approximate higher-order nonlinear functions when using comparable physical resources, highlighting the importance of quantum interference and indistinguishability as a resource in photonic quantum reservoir computing.

quant-ph↗

arXiv2025

A scalable advantage in multi-photon quantum machine learning

Photons are promising candidates for quantum information technology due to their high robustness and long coherence time at room temperature. Inspired by the prosperous development of photonic computing techniques, recent research has turned attention to performing quantum machine learning on photonic platforms. Although photons possess a high-dimensional quantum feature space suitable for computation, a general understanding of how to harness it for learning tasks remains blank. Here, we establish both theoretically and experimentally a scalable advantage in quantum machine learning with multi-photon states. Firstly, we prove that the learning capacity of linear optical circuits scales polynomially with the photon number, enabling generalization from smaller training datasets and yielding lower test loss values. Moreover, we experimentally corroborate these findings through unitary learning and metric learning tasks, by performing online training on a fully programmable photonic integrated platform. Our work highlights the potential of photonic quantum machine learning and paves the way for achieving quantum enhancement in practical machine learning applications.

quant-ph↗

arXiv2025

Resource-efficient crosstalk mitigation for the high-fidelity operation of photonic integrated circuits with induced phase shifters

Photonic integrated circuits (PICs) are key platforms for the compact and stable manipulation of classical and quantum light. Imperfections arising from fabrication constraints, tolerances, and operation wavelength limit the accuracy of intended operations on light and impede the practical utility of current PICs. In particular, crosstalk between reconfigurable phase shifters is challenging to characterize due to the large number of parameters to estimate and the difficulty in isolating individual parameters. Previous studies have attempted to model crosstalk solely as an interaction between controlled phase shifters, overlooking the broader scope of this issue. We introduce the concept of induced phase shifter, arising from crosstalk on bare waveguide sections as predicted by simulations, resulting in an exhaustive description and systematic analysis of crosstalk. We characterize induced phase shifters in physical devices using a machine learning-based method and propose a mitigation framework. This framework further allows to establish a criterion certifying that a given interferometer has a sufficient number of degrees of freedom adequately laid out to fully mitigate crosstalk. Our approach is experimentally validated on a 12-mode Clements interferometer. We demonstrate the efficacy of our extended crosstalk model to accurately recover physical crosstalk properties of the PIC and cancel induced phase shifters following our mitigation framework.

physics.optics↗

arXiv2024

Variational approach to photonic quantum circuits via the parameter shift rule

In the era of noisy intermediate-scale quantum computers, variational quantum algorithms are promising approaches for solving optimization tasks by training parameterized quantum circuits with the aid of classical routines informed by quantum measurements. In this context, photonic platforms based on reconfigurable integrated optics are an ideal candidate for implementing these algorithms. Among various techniques to train variational circuits, the parameter shift rule enables the exact calculation of cost-function derivatives efficiently, facilitating gradient descent-based optimization. In this paper, we derive a formulation of the parameter shift rule for computing derivatives and integrals tailored to reconfigurable optical linear circuits and based on the Boson Sampling paradigm. This allows us to naturally embed common types of experimental noise, such as partial distinguishability and mixedness of the states, thus obtaining a resilient approach. Finally, we employ the developed approach to experimentally test variational algorithms with single-photon states processed in a reconfigurable 6-mode universal integrated interferometer. Specifically, we apply the photonic parameter shift rules to the variational implementation, on a photonic platform, of both an eigensolver and a Universal-Not gate.

quant-ph↗

arXiv2024

Modular Quantum-to-Quantum Bernoulli Factory in an Integrated Photonic Processor

Generation and manipulation of randomness is a relevant task for several applications of information technology. It has been shown that quantum mechanics offers some advantages for this type of task. A promising model for randomness manipulation is provided by the Bernoulli factories, protocols capable of changing the bias of Bernoulli random processes in a controlled way. At first, this framework was proposed and investigated in a fully classical regime. Recent extensions of this model to the quantum case showed the possibility of implementing a wider class of randomness manipulation functions. We propose a Bernoulli factory scheme with quantum states as input and output, using a photonic path-encoding approach. Our scheme is modular, universal, and its functioning is truly oblivious of the input bias, characteristics that were missing in earlier work. We report on experimental implementations using an integrated and fully programmable photonic platform, thus demonstrating the viability of our approach. These results open new paths for randomness manipulation with integrated quantum technologies.

quant-ph↗

arXiv2024

Demonstration of Hardware Efficient Photonic Variational Quantum Algorithm

Quantum computing has brought a paradigm change in computer science, where non-classical technologies have promised to outperform their classical counterpart. Such an advantage was only demonstrated for tasks without practical applications, still out of reach for the state-of-art quantum technologies. In this context, a promising strategy to find practical use of quantum computers is to exploit hybrid quantum-classical models, where a quantum device estimates a hard-to-compute quantity, while a classical optimizer trains the parameters of the model. In this work, we demonstrate that single photons and linear optical networks are sufficient for implementing Variational Quantum Algorithms, when the problem specification, or ansatz, is tailored to this specific platform. We show this by a proof-of-principle demonstration of a variational approach to tackle an instance of a factorization task, whose solution is encoded in the ground state of a suitable Hamiltonian. This work which combines Variational Quantum Algorithms with hardware efficient ansatzes for linear-optics networks showcases a promising pathway towards practical applications for photonic quantum platforms.

quant-ph↗

arXiv2024

Experimental quantum-enhanced kernels on a photonic processor

Recently, machine learning had a remarkable impact, from scientific to everyday-life applications. However, complex tasks often imply unfeasible energy and computational power consumption. Quantum computation might lower such requirements, although it is unclear whether enhancements are reachable by current technologies. Here, we demonstrate a kernel method on a photonic integrated processor to perform a binary classification. We show that our protocol outperforms state-of-the-art kernel methods including gaussian and neural tangent kernels, exploiting quantum interference, and brings a smaller improvement also by single photon coherence. Our scheme does not require entangling gates and can modify the system dimension through additional modes and injected photons. This result opens to more efficient algorithms and to formulating tasks where quantum effects improve standard methods.

quant-ph↗

arXiv2024

Variational quantum cloning machine on an integrated photonic interferometer

A seminal task in quantum information theory is to realize a device able to produce copies of a generic input state with the highest possible output fidelity, thus realizing an \textit{optimal} quantum cloning machine. Recently, the concept of variational quantum cloning was introduced: a quantum machine learning algorithm through which, by exploiting a classical feedback loop informed by the output of a quantum processing unit, the system can self-learn the programming required for an optimal quantum cloning strategy. In this work, we experimentally implement a $1 \rightarrow 2$ variational cloning machine of dual-rail encoded photonic qubits, both for phase-covariant and state-dependent cloning. We exploit a fully programmable 6-mode universal integrated device and classical feedback to reach near-optimal cloning performances. Our results demonstrate the potential of programmable integrated photonic platforms for variational self-learning of quantum algorithms.

quant-ph↗

arXiv2023

Experimental certification of contextuality, coherence and dimension in a programmable universal photonic processor

Quantum superposition of high-dimensional states enables both computational speed-up and security in cryptographic protocols. However, the exponential complexity of tomographic processes makes certification of these properties a challenging task. In this work, we experimentally certify coherence witnesses tailored for quantum systems of increasing dimension, using pairwise overlap measurements enabled by a six-mode universal photonic processor fabricated with a femtosecond laser writing technology. In particular, we show the effectiveness of the proposed coherence and dimension witnesses for qudits of dimensions up to 5. We also demonstrate advantage in a quantum interrogation task, and show it is fueled by quantum contextuality. Our experimental results testify to the efficiency of this novel approach for the certification of quantum properties in programmable integrated photonic platforms

quant-ph↗

arXiv2023

High-fidelity and polarization insensitive universal photonic processors fabricated by femtosecond laser writing

Universal photonic processors (UPPs) are fully programmable photonic integrated circuits that are key components in quantum photonics. With this work, we present a novel platform for the realization of low-loss, low-power and high-fidelity UPPs based on femtosecond laser writing (FLW) and compatible with a large wavelength spectrum. In fact, we demonstrate different UPPs, tailored for operation at 785 nm and 1550 nm, providing similar high-level performances. Moreover, we show that standard calibration techniques applied to FLW-UPPs result in Haar random polarization independent photonic transformations implemented with average amplitude fidelity as high as 0.9979 at 785 nm (0.9970 at 1550 nm), with the possibility of increasing the fidelity over 0.9990 thanks to novel optimization algorithms. Besides being the first demonstrations of polarization-transparent UPPs, these devices show the highest level of control and reconfigurability ever reported for a FLW circuit. These qualities will be greatly beneficial to applications in quantum information processing.

physics.optics↗

arXiv2023

Variational quantum algorithm for experimental photonic multiparameter estimation

Variational quantum metrology represents a powerful tool for optimizing generic estimation strategies, combining the principles of variational optimization with the techniques of quantum metrology. Such optimization procedures result particularly effective for multiparameter estimation problems, where traditional approaches, requiring prior knowledge of the system behavior, often suffer from limitations due to the curse of dimensionality and computational complexity. To overcome these challenges, we develop a variational approach able to efficiently optimize a multiparameter quantum phase sensor operating in a noisy environment. By exploiting the high reconfigurability of an integrated photonic device, we implement a hybrid quantum-classical feedback loop able to enhance the estimation performances, combining classical optimization techniques with quantum circuit evaluations. The latter allows us to compute the system partial derivatives with respect to the variational parameters by applying the parameter-shift rule, and thus reconstruct experimentally the Fisher information matrix. This in turn is adopted as the cost function of a derivative-free classical learning algorithm run to optimize the measurement settings. Our experimental results reveal significant improvements in terms of estimation accuracy and noise robustness, highlighting the potential of the implementation of variational techniques for practical applications in quantum sensing and more generally for quantum information processing with photonic circuits.

quant-ph↗

arXiv2023

Six-telescope integrated optics beam combiner fabricated using ultrafast laser inscription for J- and H-band astronomy

We have built and characterized the first six-telescope discrete beam combiner (DBC) for stellar interferometry in the astronomical J-band. It is the DBC with the largest number of beam combinations and was manufactured using ultrafast laser inscription (ULI) in borosilicate glass, with a throughput of approximately 56%. For calibration of the visibility-to-pixel matrix (V2PM), we use a two-input Michelson interferometer and extract the complex visibility. A visibility amplitude of 1.05 and relative precision of 2.9% and 3.8% are extracted for 1328 nm and 1380 nm, respectively. Broadband (< 40 nm) characterization is affected by dispersion, but shows similar performance.

astro-ph.IM↗

arXiv2023

Programmable multi-photon quantum interference in a single spatial mode

The interference of non-classical states of light enables quantum-enhanced applications reaching from metrology to computation. Most commonly, the polarisation or spatial location of single photons are used as addressable degrees-of-freedom for turning these applications into praxis. However, the scale-up for the processing of a large number of photons of such architectures is very resource demanding due to the rapidily increasing number of components, such as optical elements, photon sources and detectors. Here we demonstrate a resource-efficient architecture for multi-photon processing based on time-bin encoding in a single spatial mode. We employ an efficient quantum dot single-photon source, and a fast programmable time-bin interferometer, to observe the interference of up to 8 photons in 16 modes, all recorded only with one detector--thus considerably reducing the physical overhead previously needed for achieving equivalent tasks. Our results can form the basis for a future universal photonics quantum processor operating in a single spatial mode.

quant-ph↗

arXiv2022

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 the 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 that often results computationally and experimentally demanding. Here, 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 on 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 feedbacks 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. This work represents an important step towards fully artificial intelligence-based quantum metrology.

quant-ph↗

arXiv2022

Experimental multiparameter quantum metrology in adaptive regime

Relevant metrological scenarios involve the simultaneous estimation of multiple parameters. The fundamental ingredient to achieve quantum-enhanced performances is based on the use of appropriately tailored quantum probes. However, reaching the ultimate resolution allowed by physical laws requires non trivial estimation strategies both from a theoretical and a practical point of view. A crucial tool for this purpose is the application of adaptive learning techniques. Indeed, adaptive strategies provide a flexible approach to obtain optimal parameter-independent performances, and optimize convergence to the fundamental bounds with limited amount of resources. Here, we combine on the same platform quantum-enhanced multiparameter estimation attaining the corresponding quantum limit and adaptive techniques. We demonstrate the simultaneous estimation of three optical phases in a programmable integrated photonic circuit, in the limited resource regime. The obtained results show the possibility of successfully combining different fundamental methodologies towards transition to quantum sensors applications.

quant-ph↗

arXiv2021

Reconfigurable continuously-coupled 3D photonic circuit for Boson Sampling experiments

Boson Sampling is a computational paradigm representing one of the most viable and pursued approaches to demonstrate the regime of quantum advantage. Recent results have demonstrated significant technological leaps in single-photon generation and detection, leading to progressively larger experimental instances of Boson Sampling experiments in different photonic systems. However, a crucial requirement for a fully-fledged platform solving this problem is the capability of implementing large-scale interferometers, that must simultaneously exhibit low losses, high degree of reconfigurability and the realization of arbitrary transformations. In this work, we move a step forward in this direction by demonstrating the adoption of a compact and reconfigurable 3D-integrated platform for photonic Boson Sampling. We perform 3- and 4-photon experiments by using such platform, showing the possibility of programming the circuit to implement a large number of unitary transformations. These results show that such compact and highly-reconfigurable layout can be scaled up to experiments with larger number of photons and modes, and can provide a viable direction for hybrid computing with photonic processors.

quant-ph↗

arXiv2021

Experimental quantum memristor

Quantum computer technology harnesses the features of quantum physics for revolutionizing information processing and computing. As such, quantum computers use physical quantum gates that process information unitarily, even though the final computing steps might be measurement-based or non-unitary. The applications of quantum computers cover diverse areas, reaching from well-known quantum algorithms to quantum machine learning and quantum neural networks. The last of these is of particular interest by belonging to the promising field of artificial intelligence. However, quantum neural networks are technologically challenging as the underlying computation requires non-unitary operations for mimicking the behavior of neurons. A landmark development for classical neural networks was the realization of memory-resistors, or "memristors". These are passive circuit elements that keep a memory of their past states in the form of a resistive hysteresis and thus provide access to nonlinear gate operations. The quest for realising a quantum memristor led to a few proposals, all of which face limited technological practicality. Here we introduce and experimentally demonstrate a novel quantum-optical memristor that is based on integrated photonics and acts on single photons. We characterize its memristive behavior and underline the practical potential of our device by numerically simulating instances of quantum reservoir computing, where we predict an advantage in the use of our quantum memristor over classical architectures. Given recent progress in the realization of photonic circuits for neural networks applications, our device could become a building block of immediate and near-term quantum neuromorphic architectures.

quant-ph↗

arXiv2020

Space qualification of ultrafast laser written integrated waveguide optics

Satellite-based quantum technologies represent a possible route for extending the achievable range of quantum communication, allowing the construction of worldwide quantum networks without quantum repeaters. In space missions, however, the volume available for the instrumentation is limited, and footprint is a crucial specification of the devices that can be employed. Integrated optics could be highly beneficial in this sense, as it allows for the miniaturization of different functionalities in small and monolithic photonic circuits. In this work, we report on the qualification of waveguides fabricated in glass by femtosecond laser micromachining for their use in a low Earth orbit space environment. In particular, we exposed different laser written integrated devices, such as straight waveguides, directional couplers, and Mach-Zehnder interferometers, to suitable proton and $\gamma$-ray irradiation. Our experiments show that no significant changes have been induced to their characteristics and performances by the radiation exposure. Our results, combined with the high compatibility of laser-written optical circuits to quantum communication applications, pave the way for the use of laser-written integrated photonic components in future satellite missions.

physics.optics↗