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

Variational optical processors

Charles Roques‐Carmes, Aviv Karnieli, David A. B. Miller, Shanhui Fan

Published Jan 1, 2025• 0 citations
Publisher

Abstract

We introduce “variational optical processors,” self-configuring photonic networks that learn modal representations of partially coherent or quantum optical fields through optimization, applicable to diverse classical and quantum optical processing tasks.
DOI
10.1364/cleo_fs.2025.ff105_7

Related papers

Variational Optical Processors

Charles Roques‐Carmes, Aviv Karnieli, David A. B. Miller +1 more

We introduce a new class of self-configuring photonic architectures called variational optical processors (VOPs). These devices tackle the problem of decomposing and measuring multimode light fields, both partially coherent and quantum, without requiring prior knowledge of the modes involved. Classical strategies for modal decomposition—such as Karhunen-Loève expansions [1] for partially coherent beams or Schmidt decompositions [2] for bipartite quantum states—often rely on comprehensive tomography and complex data processing. By contrast, VOPs discover the relevant modes in situ through a simple optimization of detection signals at their outputs (e.g., measured optical power or coincidence counts), eliminating the overhead usually associated with scanning and reconstruction. Prior demonstrations of self-configuring photonic networks have proven the feasibility of such approaches for analyzing and generating multimode coherent optical fields [3].

Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)•Jun 23, 2025•0 citations

Variational Processing of Multimode Squeezed Light

Aviv Karnieli, Paul-Alexis Mor, Charles Roques‐Carmes +5 more

Integrated multimode quantum optics is a promising platform for scalable continuous-variable quantum technologies leveraging multimode squeezing in both the spatial and spectral domains. However, on-chip measurement, routing and processing the relevant “supermodes” over which the squeezing resource is distributed still scales quadratically with the number of modes N , causing a rapid increase in photonic circuit size and number of required measurements. Here, we introduce a variational scheme, relying on self-configuring photonic networks (SCNs) that learn and extract the most-squeezed supermodes sequentially, reducing both the circuit size and the experimental overhead. Using homodyne measurement as a cost function, a sparse SCN discovers the l ≪ N most significant supermodes using O ( l N ) physical elements and optimization steps. We analyze and numerically simulate these architectures for both real-space and frequency-domain implementations, showing a fidelity close to unity between the learned circuit and the supermode decomposition, even in the presence of optical losses and detection noise. In the frequency domain, we show that circuit size can be further reduced by using inverse-designed surrogate networks, which emulate the layers learned thus far. Using two different frequency encoding schemes—uniformly and nonuniformly spaced frequency bins—we reduce an entire network (learning all N supermodes) to O ( N ) and even O ( 1 ) modulated cavities. Our results point toward chip-scale, resource-efficient quantum processing units and demultiplexers for continuous variable processing in multimode quantum optics, with applications ranging from quantum communication, metrology, and computation.

arXiv:2509.16753•PRX Quantum•Mar 3, 2026•1 citation

Quantum Photonic Neural Networks

Gregory R. Steinbrecher, Jonathan P. Olson, Dirk Englund +1 more

We propose an architecture for next generation quantum photonic processors, which maps the features of neural networks into the quantum optical domain. Through numerical simulation we demonstrate a range of new quantum information processing tasks.

Conference on Lasers and Electro-Optics•Jan 1, 2019•0 citations

Diagonalizing an optical coherence matrix via on-chip Stokes tomography

Amin Hashemi, Abbas Shiri, Bahaa E. A. Saleh +2 more

Structured coherence -- partially coherent light spanned by a finite number of modes -- is emerging as a powerful tool in optical communications, computation, cryptography, and spectroscopy. Key to these prospects is the recent development of on-chip processing of structured coherence, in which large meshes of interferometers implement unitary and non-unitary transformations on the Hermitian coherence matrix representing multimode partially coherent light. Two related critical tasks for the applications of structured coherence are the reconstruction of an unknown coherence matrix and its diagonalization. Stokes tomography has been utilized in reconstructing the coherence matrix, whereas variational processing has been employed in its diagonalization. We show here that Stokes tomography can also be exploited in the on-chip diagonalization of an unknown coherence matrix, which we verify for two-mode and four-mode structured coherence in an integrated hexagonal mesh of Mach-Zehnder interferometers. This photonic circuit implements a predetermined sequence of configurations to estimate the generalized Stokes parameters, which -- in a final step -- inform a reconfiguration of the photonic circuit that diagonalizes the coherence matrix. The field is thus left in a coherent-mode representation comprising uncorrelated, orthogonal modes whose weights correspond to the eigenvalues of the original coherence matrix. Moreover, the integrated photonic circuit can be configured to provide the original field alongside its diagonalized counterpart at the circuit output. We verify the diagonalization procedure for coherence matrices of different coherence rank, entropy, and structure. Finally, we dispel the common notion that O(N^2) steps are required for reconstructing an N x N coherence matrix and show that only O(N) steps are needed.

arXiv:2608.22372•Aug 23, 2026•0 citations

Variational quantum unsampling on a quantum photonic processor

Jacques Carolan, Masoud Mohseni, Jonathan P. Olson +9 more

arXiv:1904.10463•Nature Physics•Jan 13, 2020•90 citations

Automated Modal Analysis of Entanglement with Bipartite Self-Configuring Optics

Charles Roques‐Carmes, Aviv Karnieli, David A. B. Miller +1 more

Entanglement is a unique feature of quantum mechanics. In coupled systems of light and matter, entanglement manifests itself in the linear superposition of multipartite quantum states (e.g., parametrized by multiple spatial, spectral, or temporal degrees of freedom of a light field). In bipartite systems, the Schmidt decomposition provides a modal decomposition of the entanglement structure over independent, separable states. Although ubiquitous as a mathematical tool to describe and measure entanglement, there exists no general efficient experimental method to decompose a bipartite quantum state onto its Schmidt modes. Here, we propose a method that relies on bipartite self-configuring optics that automatically “learns” the Schmidt decomposition of an arbitrary pure quantum state. Our method is designed for entanglement distributed over spatial or spectral degrees of freedom of a light field and can reconstruct the Schmidt modes and values by variational optimization of the network’s output powers or coincidences. We illustrate our method with numerical examples of spectral entanglement analysis for biphotons generated via spontaneous parametric down conversion and provide experimental guidelines for its realization and generalization to other degrees of freedom of light, including the influence of losses and impurities. Our method provides a versatile and scalable way of analyzing entanglement in bipartite integrated quantum photonic systems.

ACS Photonics•May 28, 2025•5 citations
We introduce a new class of self-configuring photonic architectures called variational optical processors (VOPs). These devices tackle the problem of decomposing and measuring multimode light fields, both partially coherent and quantum, without requiring prior knowledge of the modes involved. Classical strategies for modal decomposition—such as Karhunen-Loève expansions [1] for partially coherent beams or Schmidt decompositions [2] for bipartite quantum states—often rely on comprehensive tomography and complex data processing. By contrast, VOPs discover the relevant modes in situ through a simple optimization of detection signals at their outputs (e.g., measured optical power or coincidence counts), eliminating the overhead usually associated with scanning and reconstruction. Prior demonstrations of self-configuring photonic networks have proven the feasibility of such approaches for analyzing and generating multimode coherent optical fields [3].
Integrated multimode quantum optics is a promising platform for scalable continuous-variable quantum technologies leveraging multimode squeezing in both the spatial and spectral domains. However, on-chip measurement, routing and processing the relevant “supermodes” over which the squeezing resource is distributed still scales quadratically with the number of modes N , causing a rapid increase in photonic circuit size and number of required measurements. Here, we introduce a variational scheme, relying on self-configuring photonic networks (SCNs) that learn and extract the most-squeezed supermodes sequentially, reducing both the circuit size and the experimental overhead. Using homodyne measurement as a cost function, a sparse SCN discovers the l ≪ N most significant supermodes using O ( l N ) physical elements and optimization steps. We analyze and numerically simulate these architectures for both real-space and frequency-domain implementations, showing a fidelity close to unity between the learned circuit and the supermode decomposition, even in the presence of optical losses and detection noise. In the frequency domain, we show that circuit size can be further reduced by using inverse-designed surrogate networks, which emulate the layers learned thus far. Using two different frequency encoding schemes—uniformly and nonuniformly spaced frequency bins—we reduce an entire network (learning all N supermodes) to O ( N ) and even O ( 1 ) modulated cavities. Our results point toward chip-scale, resource-efficient quantum processing units and demultiplexers for continuous variable processing in multimode quantum optics, with applications ranging from quantum communication, metrology, and computation.
Structured coherence -- partially coherent light spanned by a finite number of modes -- is emerging as a powerful tool in optical communications, computation, cryptography, and spectroscopy. Key to these prospects is the recent development of on-chip processing of structured coherence, in which large meshes of interferometers implement unitary and non-unitary transformations on the Hermitian coherence matrix representing multimode partially coherent light. Two related critical tasks for the applications of structured coherence are the reconstruction of an unknown coherence matrix and its diagonalization. Stokes tomography has been utilized in reconstructing the coherence matrix, whereas variational processing has been employed in its diagonalization. We show here that Stokes tomography can also be exploited in the on-chip diagonalization of an unknown coherence matrix, which we verify for two-mode and four-mode structured coherence in an integrated hexagonal mesh of Mach-Zehnder interferometers. This photonic circuit implements a predetermined sequence of configurations to estimate the generalized Stokes parameters, which -- in a final step -- inform a reconfiguration of the photonic circuit that diagonalizes the coherence matrix. The field is thus left in a coherent-mode representation comprising uncorrelated, orthogonal modes whose weights correspond to the eigenvalues of the original coherence matrix. Moreover, the integrated photonic circuit can be configured to provide the original field alongside its diagonalized counterpart at the circuit output. We verify the diagonalization procedure for coherence matrices of different coherence rank, entropy, and structure. Finally, we dispel the common notion that O(N^2) steps are required for reconstructing an N x N coherence matrix and show that only O(N) steps are needed.
Entanglement is a unique feature of quantum mechanics. In coupled systems of light and matter, entanglement manifests itself in the linear superposition of multipartite quantum states (e.g., parametrized by multiple spatial, spectral, or temporal degrees of freedom of a light field). In bipartite systems, the Schmidt decomposition provides a modal decomposition of the entanglement structure over independent, separable states. Although ubiquitous as a mathematical tool to describe and measure entanglement, there exists no general efficient experimental method to decompose a bipartite quantum state onto its Schmidt modes. Here, we propose a method that relies on bipartite self-configuring optics that automatically “learns” the Schmidt decomposition of an arbitrary pure quantum state. Our method is designed for entanglement distributed over spatial or spectral degrees of freedom of a light field and can reconstruct the Schmidt modes and values by variational optimization of the network’s output powers or coincidences. We illustrate our method with numerical examples of spectral entanglement analysis for biphotons generated via spontaneous parametric down conversion and provide experimental guidelines for its realization and generalization to other degrees of freedom of light, including the influence of losses and impurities. Our method provides a versatile and scalable way of analyzing entanglement in bipartite integrated quantum photonic systems.