arXiv subjects

Roberto Osellame

Publications and source records attributed to Roberto Osellame.

At least 37 records · Page 2Linked to original sources

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

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↗

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

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

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↗

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

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 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

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

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

Integrated thermo-optic phase shifters for laser-written photonic circuits operating at cryogenic temperatures

Integrated photonics offers compact and stable manipulation of optical signals in miniaturized chips, with the possibility of changing dynamically their functionality by means of integrated phase shifters. Cryogenic operation of these devices is becoming essential for advancing photonic quantum technologies, accommodating components like quantum light sources, single photon detectors and quantum memories operating at liquid helium temperatures. In this work, we report on a programmable glass photonic integrated circuit (PIC) fabricated through femtosecond laser waveguide writing (FLW) and controlled by thermo-optic phase shifters both in a room-temperature and in a cryogenic setting. By taking advantage of a femtosecond laser microstructuring process, we achieved reliable PIC operation with minimal power consumption and confined temperature gradients in both conditions. This advancement marks the first cryogenically-compatible programmable FLW PIC, paving the way for fully integrated quantum architectures realized on a laser-written photonic chip.

physics.optics↗

arXiv2024

A micro-opto-mechanical glass interferometer for megahertz modulation of optical signals

Waveguide-based interferometric circuits are widely employed in optical communications, sensing and computing applications. In particular, glass-based devices are appealing due to the transparency and bio-compatibility of this substrate, or where low-loss interfacing with fiber networks is required. However, fast electro-optic phase modulation is hard to achieve in glass materials. Here, we demonstrate an optical phase and intensity modulator in glass, working in the megahertz range. This modulator exploits the elasto-optic effect inside a mechanical microstructure, brought to oscillation at resonance, and is entirely realized by femtosecond laser micromachining. In detail, we demonstrate 23-dB optical intensity modulation at 1.17 MHz, with an internal optical loss of the phase-modulator component as low as 0.04 dB.

physics.optics↗

arXiv2024

Semi-device independent characterization of multiphoton indistinguishability

Multiphoton indistinguishability is a central resource for quantum enhancement in sensing and computation. Developing and certifying large scale photonic devices requires reliable and accurate characterization of this resource, preferably using methods that are robust against experimental errors. Here, we propose a set of methods for the characterization of multiphoton indistinguishability, based on measurements of bunching and photon number variance. Our methods are robust in a semi-device independent way, in the sense of being effective even when the interferometers are incorrectly dialled. We demonstrate the effectiveness of this approach using an advanced photonic platform comprising a quantum-dot single-photon source and a universal fully-programmable integrated photonic processor. Our results show the practical usefulness of our methods, providing robust certification tools that can be scaled up to larger systems.

quant-ph↗

arXiv2024

Laser-written micro-channel atomic magnetometer

We demonstrate a sensitive optically-pumped magnetometer using rubidium vapor and 0.75 amg of nitrogen buffer gas in a sub-mm-width sensing channel excavated by femtosecond laser writing followed by chemical etching. The channel is buried less than 1 mm below the surface of its fused silica host material, which also includes reservoir chambers and micro-strainer connections, to preserve a clean optical environment. Using a zero-field-resonance magnetometry strategy and a sensing volume of 2.25 mm$^3$, we demonstrate a sensitivity of $\approx$ 1 $\mathrm{pT}/\sqrt{\mathrm{Hz}}$ at $10$ Hz. The device can be integrated with photonic structures and microfluidic channels with 3D versatility. Its sensitivity, bandwidth and stand-off distance will enable detection of localized fields from magnetic nanoparticles and \mul NMR samples.

quant-ph↗

arXiv2024

Synthesizing extreme-ultraviolet vector beams in a chip

Structured light has gained significant attention in recent years, especially in the generation and application of vector beams. These beams, characterized by a spatially varying polarization state, are a powerful tool to enhance our capacity to control light-matter interactions. In this study, we demonstrate the synthesis of extreme-ultraviolet (EUV) vector beams in a chip through high-order harmonic generation (HHG). Our findings showcase the chip's ability to transfer the laser polarization state into the EUV beam despite the extended interaction length. This approach not only outperforms conventional free-space methods but also paves the way for a multitude of on-chip investigations in the realms of EUV and soft-X-ray spectroscopy.

physics.optics↗