Riccardo Albiero

Universal photon-based quantum computing requires optical nonlinearities, which can be induced by intermediate measurements and by adaptivity, to be supplied to linear-optical elements. In a near-term perspective, it is essential to probe whether dynamics going beyond linear optics can be accessed with a limited amount of resources. Although recent results show how linear-optical dynamics implies bounds on the set of photonic states that can be generated, quantitative methods for studying the emergence of nonlinear dynamics are largely missing. Here we analyse a regime in which such bounds can be surpassed. We do this by leveraging an adaptive boson sampling architecture in which the optical evolution implemented in a photonic device is progressively adapted via measurement-based feedback. We introduce practical methods to quantify the emergence of a gap with respect to linear optics and derive nonlinearity witnesses from the properties of linear-optical evolution. Then, we validate the toolbox developed within adaptive boson sampling architectures of increasing complexity implemented on a state-of-the-art photonic platform, both by realizing real-time adaptivity and by emulating adaptive protocols via post-selection for more complex configurations. In particular, we probe experimentally a regime in which nonlinear dynamics, unobtainable within a linear-optical paradigm, can arise, thus showing how optical architectures with limited adaptivity are a powerful testbed for exploring new regimes.

Bosonic bunching is a term used to describe the well-known tendency of bosons to bunch together, and which differentiates their behavior 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 that 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.

The past 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 reuploading 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 reuploading in a potentially resource-efficient optical implementation but also provide 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.

Over the past few years, various methods have been developed to engineer 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 algebras 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 within typical interferometric architectures for photonic quantum information. This opens avenues for the use of algebraic-inspired methods as analysis tools for photon-based quantum computing protocols.

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 dialed. 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. Published by the American Physical Society 2025

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.