Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Nature Photonics

Beyond quantum linear optics with adaptive boson sampling

Giovanni Rodari, Tommaso Francalanci, Eugenio Caruccio, Francesco Hoch, Giorgio Milani, Gonzalo Carvacho, Taira Giordani, Nicolò Spagnolo, Riccardo Albiero, Niki Di Giano, Giacomo Corrielli, Andrea Crespi, Francesco Ceccarelli, Mattia Bossi, Abhiram Rajan, Roberto Osellame, Ulysse Chabaud, Fabio Sciarrino

Published Jul 20, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.1038/s41566-026-01959-3

Related papers

Quantum machine learning with Adaptive Boson Sampling via post-selection

Francesco Hoch, Eugenio Caruccio, Giovanni Rodari +18 more

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. Extending quantum photonics’ capabilities from simple linear-optics-based schemes to universal quantum computing presents several challenges, but intermediate regimes with some degree of adaptivity might already bring practical advantages. Here, the authors experimentally emulate an adaptive Boson Sampling scheme using post-selection, and apply it to a data classification task.

arXiv:2502.20305•Nature Communications•Jan 21, 2025•34 citations

Extensible universal photonic quantum computing with nonlinearity

Shang Yu, Jinzhao Sun, Kuan-Cheng Chen +17 more

Universal quantum computing requires an architecture that supports both linear circuits and, crucially, strong nonlinear resources. For quantum photonic systems, integrating such nonlinearities with scalable linear circuitry has been a major bottleneck, leaving most optical experiments without nonlinear operations and, consequently, incapable of achieving universality. Here, we report an extensible photonic computer that supports a universal gate set by seamlessly combining fully programmable, scalable linear optical networks with integrated nonlinear modules. This platform enables a broad range of quantum computing and simulation tasks. We demonstrate the quasi-deterministic generation of optical Gottesman-Kitaev-Preskill states, which are essential resources for bosonic error correction, yet had previously been realized only probabilistically. Furthermore, we simulate complex many-body quantum dynamics, exemplified by the Bose-Hubbard model. Such quantum simulation tasks have long been considered beyond the reach of photonic hardware limited to linear operations. These capabilities, enabled by our extensible architecture, establish a viable route towards photonic quantum simulation and fault-tolerant quantum computing.

quant-pharXiv:2602.06544•Nature Photonics•Feb 9, 2026•0 citations

Generalized photon-bunching bounds in linear and non-linear quantum optical dynamics

Tommaso Francalanci, Giovanni Rodari, Eugenio Caruccio +11 more

To assess the capabilities of photonic quantum systems, it is essential to develop analytical and experimental methods for probing the underlying optical dynamics. Here, we investigate fundamental constraints arising when multi-photon states evolve through a quantum linear optical network, showing how such limitations can be overcome when introducing measurement-induced nonlinearities. Specifically, we analyze bounds related to photon-bunching behavior at the output of a linear interferometer and test them via a hybrid photonic platform realizing experiments with up to four photons. When the analysis is extended to a nonlinear regime, such bounds can be violated. We experimentally validate such behavior by employing an optical architecture featuring measurement-induced nonlinearity, thus introducing dynamics that go beyond standard linear optics. Our results highlight how the constraints implied by the linear optical formalism can be used as tools to test and characterize photonic models going beyond quantum linear optical dynamics.

Optica•Apr 15, 2026•0 citations

Photonic quantum convolutional neural networks with adaptive state injection

Léo Monbroussou, Beatrice Polacchi, Verena Yacoub +14 more

Recent photonic quantum machine learning proposals combined linear optics with adaptivity to enhance expressivity and improve algorithm performance and scalability. The particle-number-preserving property of linear optical platforms was recently employed to design a quantum convolutional neural network architecture with advantages in terms of resource complexity and the number of parameters needed. Here, we design and experimentally implement a photonic quantum convolutional neural network (PQCNN) based on linear optics equipped with adaptive state injection, a tool that increases the linear optical circuits controllability. We 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. To investigate the scalability of the PQCNN design, we performed numerical simulations on datasets of different sizes. These findings demonstrate potential utilities of a simple adaptive technique for a nonlinear boson sampling task, compatible with near-term quantum devices.

arXiv:2504.20989•Advanced Photonics•Nov 18, 2025•5 citations

Observation of Lie algebraic invariants in quantum linear optics

Giovanni Rodari, Tommaso Francalanci, Eugenio Caruccio +12 more

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.

arXiv:2505.03001•Physical Review Research•Dec 2, 2025•2 citations

Quantum Interference Dynamics in Chip-Scale Photonic Boson Sampling Circuit

Gaurav Kumar Yadav, S K Satish Kumar, Chandan Pathak +1 more

Boson sampling is a leading platform for exploring many-body quantum interference in linear optical systems. However, identifying circuit-level operating regimes where quantum signatures remain robust under realistic conditions remains challenging. In this work, multimode photonic boson sampling is investigated using a programmable six mode integrated photonic circuit composed of cascaded beam splitters and tunable phase shifters. Each beam splitter is realized through directional couplers combined with local phase control, enabling arbitrary unitary transformations within the linear optical circuit. The circuit is modeled using a tight-binding Hamiltonian allowing controlled manipulation of optical phases and inter-mode coupling that directly governs multiphoton interference pathways. This set up is utilised to do the analysis of Heavy Output Generation (HOG), Total Variation Distance (TVD) and Linear Cross-Entropy Benchmarking (XEB), together with configuration-resolved probability differences, for up to five photons. Distinct dynamic regimes associated with coherent evolution, optimal interference and phase dispersion induced mixing is identified. The results further reveal a photon number dependent complexity–fragility trade-off and demonstrate the robustness of TVD to symmetric photon loss under post-selection. Overall, this work establishes a circuit-relevant validation framework linking programmable interferometer parameters, phase control and quantum interference metrics, providing guidance for scalable photonic boson-sampling implementations in the NISQ regime.

IEEE Journal of Quantum Electronics•May 20, 2026•0 citations
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.
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. Extending quantum photonics’ capabilities from simple linear-optics-based schemes to universal quantum computing presents several challenges, but intermediate regimes with some degree of adaptivity might already bring practical advantages. Here, the authors experimentally emulate an adaptive Boson Sampling scheme using post-selection, and apply it to a data classification task.
Universal quantum computing requires an architecture that supports both linear circuits and, crucially, strong nonlinear resources. For quantum photonic systems, integrating such nonlinearities with scalable linear circuitry has been a major bottleneck, leaving most optical experiments without nonlinear operations and, consequently, incapable of achieving universality. Here, we report an extensible photonic computer that supports a universal gate set by seamlessly combining fully programmable, scalable linear optical networks with integrated nonlinear modules. This platform enables a broad range of quantum computing and simulation tasks. We demonstrate the quasi-deterministic generation of optical Gottesman-Kitaev-Preskill states, which are essential resources for bosonic error correction, yet had previously been realized only probabilistically. Furthermore, we simulate complex many-body quantum dynamics, exemplified by the Bose-Hubbard model. Such quantum simulation tasks have long been considered beyond the reach of photonic hardware limited to linear operations. These capabilities, enabled by our extensible architecture, establish a viable route towards photonic quantum simulation and fault-tolerant quantum computing.
To assess the capabilities of photonic quantum systems, it is essential to develop analytical and experimental methods for probing the underlying optical dynamics. Here, we investigate fundamental constraints arising when multi-photon states evolve through a quantum linear optical network, showing how such limitations can be overcome when introducing measurement-induced nonlinearities. Specifically, we analyze bounds related to photon-bunching behavior at the output of a linear interferometer and test them via a hybrid photonic platform realizing experiments with up to four photons. When the analysis is extended to a nonlinear regime, such bounds can be violated. We experimentally validate such behavior by employing an optical architecture featuring measurement-induced nonlinearity, thus introducing dynamics that go beyond standard linear optics. Our results highlight how the constraints implied by the linear optical formalism can be used as tools to test and characterize photonic models going beyond quantum linear optical dynamics.
Recent photonic quantum machine learning proposals combined linear optics with adaptivity to enhance expressivity and improve algorithm performance and scalability. The particle-number-preserving property of linear optical platforms was recently employed to design a quantum convolutional neural network architecture with advantages in terms of resource complexity and the number of parameters needed. Here, we design and experimentally implement a photonic quantum convolutional neural network (PQCNN) based on linear optics equipped with adaptive state injection, a tool that increases the linear optical circuits controllability. We 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. To investigate the scalability of the PQCNN design, we performed numerical simulations on datasets of different sizes. These findings demonstrate potential utilities of a simple adaptive technique for a nonlinear boson sampling task, compatible with near-term quantum devices.
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.
Boson sampling is a leading platform for exploring many-body quantum interference in linear optical systems. However, identifying circuit-level operating regimes where quantum signatures remain robust under realistic conditions remains challenging. In this work, multimode photonic boson sampling is investigated using a programmable six mode integrated photonic circuit composed of cascaded beam splitters and tunable phase shifters. Each beam splitter is realized through directional couplers combined with local phase control, enabling arbitrary unitary transformations within the linear optical circuit. The circuit is modeled using a tight-binding Hamiltonian allowing controlled manipulation of optical phases and inter-mode coupling that directly governs multiphoton interference pathways. This set up is utilised to do the analysis of Heavy Output Generation (HOG), Total Variation Distance (TVD) and Linear Cross-Entropy Benchmarking (XEB), together with configuration-resolved probability differences, for up to five photons. Distinct dynamic regimes associated with coherent evolution, optimal interference and phase dispersion induced mixing is identified. The results further reveal a photon number dependent complexity–fragility trade-off and demonstrate the robustness of TVD to symmetric photon loss under post-selection. Overall, this work establishes a circuit-relevant validation framework linking programmable interferometer parameters, phase control and quantum interference metrics, providing guidance for scalable photonic boson-sampling implementations in the NISQ regime.