arXiv subjects

Francesco Ceccarelli

Publications and source records attributed to Francesco Ceccarelli.

At least 19 recordsLinked to original sources

arXiv2026

Observation of associative-memory retrieval and spin-glass phases on a photonic quantum simulator

Models of interacting complex systems provide the fundamental statistical physics reference frame for the study and the understanding of associative memories, machine learning, and the dynamics of neural networks. On the other hand, simulating complex multi-synaptic interactions on a classical hardware is computationally demanding due to the super-linear scaling of the system complexity. Photonic quantum technologies provide a promising solution to these limitations by leveraging on their inherent speed and parallel processing ability in order to simulate complex networks. Recently, a connection between multiphoton processes and generalized $p$-body Hopfield models has been theoretically established. Here, we design and demonstrate an experimental platform that exploits single photons distributed across a set of optical modes, in which controlled arrays of binary phase shifters act as Ising-like neurons. We focus specifically on a fully connected Hopfield Hamiltonian with four-body local interaction terms, realized via two-photon processes. Through quantum simulations on programmable photonic processors, the study identifies three distinct regimes: a memory retrieval phase, a spin-glass memory "black-out" phase, and a paramagnetic phase. Experimental results confirm successful memory retrieval at low storage capacities and temperatures, where the system consistently relaxes to fixed points with high memory overlap, effectively reconstructing the stored patterns. Future research will extend the platform design to investigate networks with local or dilute interactions, while advances in the realization of scalable photonic circuits will enable architectures that encompass very large numbers of interacting spins.

quant-ph↗

arXiv2026

Boson Sampling with a reconfigurable 128 modes 3D integrated photonic circuit

Integrated quantum photonics has emerged as one of the leading platforms for scaling quantum information processing, offering compact, stable, and low-loss hardware with precise phase and mode control. Advances in integrated photonics architectures and active programmability now enable complex, high-dimensional transformations essential for quantum advantage tasks. We introduce an integrated, reconfigurable 3D photonic device with 128 modes for manipulation of single-photon quantum states (Qolossus 3D). Leveraging a continuously coupled architecture and thermo-optic programmability, the platform implements reconfigurable unitary transformations at unprecedented scale for integrated quantum optics. Exploiting indistinguishable single photons demultiplexed from a quantum dot source, we perform Boson Sampling across the large-dimensional chip and analyse the resulting output distributions for up to 4 photons. We then exploit it to demonstrate randomness generation via Boson Sampling. Agreement with theoretical predictions validates both the device's reconfigurable operation and the generation of random numbers. Our results highlight the scalability, stability, and precise control of integrated photonics for quantum information processing.

quant-ph↗

arXiv2025

Laser-written reconfigurable photonic integrated circuit directly coupled to a single-photon avalanche diode array

To date, most integrated quantum photonics experiments rely on single-photon detectors operating at cryogenic temperatures coupled to photonic integrated circuits (PICs) through single-mode optical fibers. This approach presents significant challenges due to the detection complexity, as cryogenic conditions hinder the development of scalable systems. In addition, going towards fully-integrated devices or, at least, removing the optical fibers would be also advantageous to develop compact and cost-efficient solutions featuring a high number of optical modes. This work reports on the direct coupling of a PIC, fabricated by femtosecond laser writing (FLW), and a silicon single-photon avalanche diode (SPAD) array, fabricated in a custom planar technology and compatible with the operation at room temperature. The effectiveness of this solution is shown by achieving perfect coupling and a system detection efficiency as high as 41.0% at a wavelength of 561 nm, which is the highest value reported to date among both heterogeneous/hybrid integrated and directly coupled systems. We also show the robustness of the coupling to misalignments, demonstrating that costly alignment procedures are not needed. Finally, we exploit the SPAD array to characterize a reconfigurable Mach-Zehnder interferometer, i.e., the basic building block of multimode reconfigurable PICs. This solution provides a new avenue to the design and implementation of quantum photonics experiments, especially effective when compact and cost-efficient systems are needed.

physics.optics↗

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

MoRE-GNN: Multi-omics Data Integration with a Heterogeneous Graph Autoencoder

The integration of multi-omics single-cell data remains challenging due to high-dimensionality and complex inter-modality relationships. To address this, we introduce MoRE-GNN (Multi-omics Relational Edge Graph Neural Network), a heterogeneous graph autoencoder that combines graph convolution and attention mechanisms to dynamically construct relational graphs directly from data. Evaluations on six publicly available datasets demonstrate that MoRE-GNN captures biologically meaningful relationships and outperforms existing methods, particularly in settings with strong inter-modality correlations. Furthermore, the learned representations allow for accurate downstream cross-modal predictions. While performance may vary with dataset complexity, MoRE-GNN offers an adaptive, scalable and interpretable framework for advancing multi-omics integration.

cs.LG↗

arXiv2025

ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data

Inferring trajectories from longitudinal spatially-resolved omics data is fundamental to understanding the dynamics of structural and functional tissue changes in development, regeneration and repair, disease progression, and response to treatment. We propose ContextFlow, a novel context-aware flow matching framework that incorporates prior knowledge to guide the inference of structural tissue dynamics from spatially resolved omics data. Specifically, ContextFlow integrates local tissue organization and ligand-receptor communication patterns into a transition plausibility matrix that regularizes the optimal transport objective. By embedding these contextual constraints, ContextFlow generates trajectories that are not only statistically consistent but also biologically meaningful, making it a generalizable framework for modeling spatiotemporal dynamics from longitudinal, spatially resolved omics data. Evaluated on three datasets, ContextFlow consistently outperforms state-of-the-art flow matching methods across multiple quantitative and qualitative metrics of inference accuracy and biological coherence. Our code is available at: \href{https://github.com/santanurathod/ContextFlow}{ContextFlow}

cs.LG↗

arXiv2025

Optimal distillation of photonic indistinguishability

Imperfect photons' indistinguishability limits the performance of photonic quantum communication and computation . Distillation protocols, inspired by entanglement purification, enhance photons' indistinguishability by leveraging quantum interference in linear optical circuits. In this work, we present a three-photon distillation protocol optimized to achieve the maximum visibility gain, which requires consideration of multi-photon effects such as collective photonic phases. We employ interferometers with the minimum number of modes, optimizing also over the protocol's success probability. The developed protocol is experimentally validated with a platform featuring a demultiplexed quantum dot source interfaced with a programmable eight-mode laser-written integrated photonic processor. We achieve indistinguishability distillation with limited photonic resources and for several multi-photon distinguishability scenarios. This work helps to strengthen the role of distillation as a practical tool for photon-based quantum technologies.

quant-ph↗

arXiv2025

Experimental data re-uploading with provable enhanced learning capabilities

The last decades have seen the development of quantum machine learning, stemming from the intersection of quantum computing and machine learning. This field is particularly promising for the design of alternative quantum (or quantum inspired) computation paradigms that could require fewer resources with respect to standard ones, e.g. in terms of energy consumption. In this context, we present the implementation of a data re-uploading scheme on a photonic integrated processor, achieving high accuracies in several image classification tasks. We thoroughly investigate the capabilities of this apparently simple model, which relies on the evolution of one-qubit states, by providing an analytical proof that our implementation is a universal classifier and an effective learner, capable of generalizing to new, unknown data. Hence, our results not only demonstrate data re-uploading in a potentially resource-efficient optical implementation but also provide new theoretical insight into this algorithm, its trainability, and generalizability properties. This lays the groundwork for developing more resource-efficient machine learning algorithms, leveraging our scheme as a subroutine.

quant-ph↗

arXiv2025

Observation of Lie algebraic invariants in Quantum Linear Optics

Over the past few years, various methods have been developed to engineeer and to exploit the dynamics of photonic quantum states as they evolve through linear optical networks. Recent theoretical works have shown that the underlying Lie algebraic structure plays a crucial role in the description of linear optical Hamiltonians, as such formalism identifies intrinsic symmetries within photonic systems subject to linear optical dynamics. Here, we experimentally investigate the role of Lie algebra applied to the context of Boson sampling, a pivotal model to the current understanding of computational complexity regimes in photonic quantum information. Performing experiments of increasing complexity, realized within a fully-reconfigurable photonic circuit, we show that sampling experiments do indeed fulfill the constraints implied by a Lie algebraic structure. In addition, we provide a comprehensive picture about how the concept of Lie algebraic invariant can be interpreted from the point of view of n-th order correlation functions in quantum optics. Our work shows how Lie algebraic invariants can be used as a benchmark tool for the correctness of an underlying linear optical dynamics and to verify the reliability of Boson Sampling experiments. This opens new avenues for the use of algebraic-inspired methods as verification tools for photon-based quantum computing protocols.

quant-ph↗

arXiv2025

A low-loss, 24-mode laser-written universal photonic processor in a glass-based platform

We report the fabrication of the first 24-mode universal photonic processor (UPP) realized through femtosecond laser writing (FLW), marking the most complex UPP demonstrated to date. Optimized for quantum dot emission at 925 nm, the device exhibits total insertion losses averaging only 4.35 dB, enabling its direct application in advanced multi-photon quantum experiments. Leveraging the versatility of FLW, we introduce suspended waveguides and precisely engineered 2D and 3D microstructures, significantly enhancing thermal isolation and minimizing power dissipation. As a result, our processor operates efficiently at less than 10 W, requiring only a simple thermo-electric cooler for stable thermal management. The device exhibits exceptional performance after calibration, implementing Haar-random unitary transformations with an amplitude fidelity of 99.7 %. This work establishes FLW-based integrated photonics as a scalable and robust platform for advancing quantum computing, communication, and sensing technologies.

quant-ph↗

arXiv2025

Photonic Quantum Convolutional Neural Networks with Adaptive State Injection

Linear optical architectures have been extensively investigated for quantum computing and quantum machine learning applications. Recently, proposals for photonic quantum machine learning have combined linear optics with resource adaptivity, such as adaptive circuit reconfiguration, which promises to enhance expressivity and improve algorithm performances and scalability. Moreover, linear optical platforms preserve some subspaces due to the fixed number of particles during the computation, a property recently exploited to design a novel quantum convolutional neural networks. This last architecture has shown an advantage in terms of running time complexity and of the number of parameters needed with respect to other quantum neural network proposals. In this work, we design and experimentally implement the first photonic quantum convolutional neural network (PQCNN) architecture based on particle-number preserving circuits equipped with state injection, an approach recently proposed to increase the controllability of linear optical circuits. Subsequently, we experimentally validate the PQCNN for a binary image classification on a photonic platform utilizing a semiconductor quantum dot-based single-photon source and programmable integrated photonic interferometers comprising 8 and 12 modes. In order to investigate the scalability of the PQCNN design, we have performed numerical simulations on datasets of different sizes. We highlight the potential utility of a simple adaptive technique for a nonlinear Boson Sampling task, compatible with near-term quantum devices.

quant-ph↗

arXiv2025

Experimental neuromorphic computing based on quantum memristor

Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such an approach retains the universality of standard methods, while attempting to circumvent their excessive requirements, which hinder their scalability. In this landscape, quantum (or quantum inspired) algorithms may bring enhancement. However, high-performing neural networks invariably display nonlinear behaviours, which poses a challenge to quantum platforms, given the intrinsically linear evolution of closed systems. We propose a strategy to enhance the nonlinearity achievable in this context, without resorting to entangling gates and report the first neuromorphic architecture based on a photonic quantum memristor. In detail, we show how the memristive feedback loop enhances the nonlinearity and hence the performance of the tested algorithms. We benchmark our model on four tasks, a nonlinear function and three time series prediction. In these cases, we highlight the essential role of the quantum memristive element and demonstrate the possibility of using it as a building block in more sophisticated networks.

quant-ph↗

arXiv2025

Experimental verification of Threshold Quantum State Tomography on a fully-reconfigurable photonic integrated circuit

Reconstructing the state of a complex quantum system represents a pivotal task for all quantum information applications, both for characterization purposes and for verification of quantum protocols. Recent technological developments have shown the capability of building quantum systems with progressively larger number of qubits in different platforms. The standard approach based on quantum state tomography, while providing a method to completely characterize an unknown quantum state, requires a number of measurements that scales exponentially with the number of qubits. Other methods have been subsequently proposed and tested to reduce the number of measurements, or to focus on specific properties of the output state rather than on its complete reconstruction. Here, we show experimentally the application of an approach, called threshold quantum state tomography, in an advanced hybrid photonic platform with states up to n=4 qubits. This method does not require a priori knowledge on the state, and selects only the informative projectors starting from the measurement of the density matrix diagonal. We show the effectiveness of this approach in a photonic platform, showing that a consistent reduction in the number of measurement is obtained while reconstructing relevant states for quantum protocols, with only very limited loss of information. The advantage of this protocol opens perspective of its application in larger, more complex, systems.

quant-ph↗

arXiv2025

Quantum machine learning with Adaptive Boson Sampling via post-selection

The implementation of large-scale universal quantum computation represents a challenging and ambitious task on the road to quantum processing of information. In recent years, an intermediate approach has been pursued to demonstrate quantum computational advantage via non-universal computational models. A relevant example for photonic platforms has been provided by the Boson Sampling paradigm and its variants, which are known to be computationally hard while requiring at the same time only the manipulation of the generated photonic resources via linear optics and detection. Beside quantum computational advantage demonstrations, a promising direction towards possibly useful applications can be found in the field of quantum machine learning, considering the currently almost unexplored intermediate scenario between non-adaptive linear optics and universal photonic quantum computation. Here, we report the experimental implementation of quantum machine learning protocols by adding adaptivity via post-selection to a Boson Sampling platform based on universal programmable photonic circuits fabricated via femtosecond laser writing. Our experimental results demonstrate that Adaptive Boson Sampling is a viable route towards dimension-enhanced quantum machine learning with linear optical devices.

quant-ph↗

arXiv2025

Quantum Analog-to-Digital Converter for Phase Estimation

Traditional quantum metrology assesses precision using the figures of merit of continuous-valued parameter estimation. Recently, quantum digital estimation was introduced: it evaluates the performance information-theoretically by quantifying the number of significant bits of the parameter, redefining key benchmarks like the Heisenberg bound. Here, we report the first experimental realization of a Quantum Analog-to-Digital Converter for quantum metrology, that takes an input continuous parameter and outputs a bit string, using an advanced photonic platform, comprising a fully reconfigurable integrated circuit and a quantum dot source of highly indistinguishable photons. We implement a protocol for digital phase estimation that is capable of surpassing the standard quantum limit, through the simultaneous use of different entangled state resources. We tackle experimental imperfections by employing machine learning techniques for noise deconvolution and estimation process refinement. Our protocol is experimentally benchmarked against classical strategies via the number of recoverable bits of the unknown parameter. Our results open new perspectives for future implementation of quantum digital estimation strategies.

quant-ph↗

arXiv2024

Experimental observation of counter-intuitive features of photonic bunching

Bosonic bunching is a term used to describe the well-known tendency of bosons to bunch together, and which differentiates their behaviour from that of fermions or classical particles. However, in some situations perfectly indistinguishable bosons may counter-intuitively bunch less than classical, distinguishable particles. Here we report two such counter-intuitive multiphoton bunching effects observed with three photons in a three-mode balanced photonic Fourier interferometer. In this setting, we show indistinguishable photons actually minimize the probability of bunching. We also show that any non-trivial value of the three-photon collective photonic phase leads to a decreased probability of all photons ending up in the same mode, even as we increase pairwise indistinguishability. Our experiments feature engineering of partial indistinguishability scenarios using both the time and the polarization photonic degrees of freedom, and a polarization-transparent 8-mode tunable interferometer with a quantum-dot source of single photons. Besides the foundational understanding, the observation of these counter-intuitive phenomena open news perspective in devising more efficient ways of routing photons for advantage in metrology and quantum computation.

quant-ph↗

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↗