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

EPJ Quantum Technology

Traversing Gaussian and non-Gaussian regimes in photonic quantum simulators: a review

Dennis Delali Kwesi Wayo, Rodrigo Alves Dias, Masoud Darvish Ganji, Camila Martins Saporetti, Leonardo Goliatt

Published Aug 14, 2026• 0 citations
Publisher

Abstract

Abstract Quantum photonic simulators are pivotal for designing and analyzing quantum photonic circuits that utilize both continuous-variable (CV) and discrete-variable (DV) encodings. This review critically examines the transition from Gaussian to non-Gaussian photonic models and the computational overhead that emerges from simulating systems with more than 20 modes or photon numbers exceeding 10 per mode. Gaussian states; such as coherent and squeezed states, are modeled efficiently using first-moment vectors and covariance matrices of dimension $2n \times 2n$ 2 n × 2 n , where n is the number of modes. Non-Gaussian elements like the cubic phase gate $U = \exp (i \gamma \hat{x}^{3})$ U = exp ( i γ x ˆ 3 ) and photon-subtracted states demand full Fock space representations and scale exponentially with photon number $d^{2}$ d 2 , where d is the Hilbert space dimension. We analyze simulators including , , and , benchmarking their support for tensor networks (bond dimension D >100), GPU acceleration, thus NVIDIA A100 with 1555 GB/s bandwidth, and SoC implementations such as Edge TPU with 8 TFLOPS. Special focus is given to noise models such as photon loss with transmissivity η , thermal noise modeled with $\bar{n} = 1.5$ n ¯ = 1.5 , and dark count Poisson distributions ( $\lambda = 0.1$ λ = 0.1 ). The review underscores scalable techniques and hybrid quantum-classical workflows vital for near-term photonic quantum computing.

DOI
10.1140/epjqt/s40507-026-00496-w

Related papers

Gaussian Models to Non-Gaussian Realms of Quantum Photonic Simulators

Dennis Delali Kwesi Wayo, Rodrigo Alves Dias, Masoud Darvish Ganji +2 more

Quantum photonic simulators have emerged as indispensable tools for modeling and optimizing quantum photonic circuits, bridging the gap between theoretical models and experimental implementations. This review explores the landscape of photonic quantum simulation, focusing on the transition from Gaussian to non-Gaussian models and the computational challenges associated with simulating large-scale photonic systems. Gaussian states and operations, which enable efficient simulations through covariance matrices and phase-space representations, serve as the foundation for photonic quantum computing. However, non-Gaussian states crucial for universal quantum computation introduce significant computational complexity, requiring advanced numerical techniques such as tensor networks and high-performance GPU acceleration. We evaluate the leading photonic quantum simulators, including Strawberry Fields, Piquasso, QuTiP SimulaQron, Perceval, and QuantumOPtics.jl analyzing their capabilities in handling continuous-variable (CV) and discrete-variable (DV) quantum systems. Special attention is given to hardware-accelerated methods, including GPU-based tensor network approaches, machine learning integration, and hybrid quantum-classical workflows. Furthermore, we investigate noise modeling techniques, such as photon loss and dark counts, and their impact on simulation accuracy. As photonic quantum computing moves toward practical implementations, advancements in high-performance computing (HPC) architectures, such as tensor processing units (TPUs) and system-on-a-chip (SoC) solutions, are accelerating the field. This review highlights emerging trends, challenges, and future directions for developing scalable and efficient photonic quantum simulators.

arXiv:2502.05245•Feb 7, 2025•0 citations

Mode-Selective Photon Subtraction via Time-to-Frequency Mapping for Multimode Entangled States

Bastien Oriot, Peter Namdar, David Fainsin +3 more

Photonics quantum networks are pivotal for quantum technologies, facilitating efficient information distribution and manipulation through quantum states of light. Continuous-variable (CV) cluster states, essential resources for Measurement-Based Quantum Computing, can be deterministically generated using squeezed states of light and mode-basis changes [1]. However, Gaussian states like squeezed states or cluster states, can be efficiently simulated with a classical computer [2]. Thus, to achieve quantum advantage, non-Gaussianity should be introduced, which can be accomplished by subtracting a photon from a squeezed state [3].

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

Efficient Simulation of Hybrid Continuous- and Discrete-Variable Quantum Circuits via Gaussian Decompositions

Kazufumi Tanji, Dot Belin Pio, Shohei Kiryu +2 more

Hybrid quantum systems combining discrete-variable (DV) and continuous-variable (CV) subsystems arise across a broad range of physical platforms and enable quantum information processing beyond purely qubit-based designs. Their classical simulation, however, is challenging since computational costs of both CV and DV systems exponentially increase. In this paper, we introduce a simulation framework for CV--DV hybrid systems based on linear combination of Gaussian (LCoG) decomposition. We decompose the hybrid density matrix into blocks in a DV basis and represent each block as an LCoG expansion. Within the representation, Gaussian operations on the bosonic subsystem independently act on each Gaussian function. We derive analytic formulas describing how complex Gaussian functions transform under controlled Gaussian operations. We further extend the framework to controlled Gaussian channels with state-dependent Gaussian noise. The method avoids a Fock-space truncation for the CV subsystem, and the cost of updating each Gaussian term scales polynomially with the number of modes. The framework provides a general computational tool for analyzing hybrid quantum algorithms, quantum simulation protocols, and non-Gaussian state generation schemes. As an example, we simulate Gottesman--Kitaev--Preskill (GKP) state generation based on a cavity-QED system.

quant-pharXiv:2609.34793•Sep 29, 2026•0 citations

Hardware-inspired Continuous Variables Quantum Optical Neural Networks

Todor Krasimirov-Ivanov, Alba Cervera-Lierta, Paolo Stornati +1 more

Continuous-variables (CV) quantum optics is a natural formalism for neural networks (NNs) due to its ability to reproduce the information processing of such trainable interconnected systems. In quantum optics, Gaussian operators induce affine mappings on the quadratures of optical modes while non-Gaussian resources (the challenging piece for physical implementation) originate the nonlinear effects, unlocking quantum analogs of an artificial neuron. This work presents a novel experimentally-feasible framework for continuous-variable quantum optical neural networks (QONNs) developed with available photonic components: coherent states as input encoding, a general Gaussian transformation followed by multi-mode photon subtractions as the processing layer, and homodyne detection as outputs readout. The closed-form expressions of such architecture are derived demonstrating the family of adaptive activations and the quantum-optical neurons that emerge from the amount of photon-subtracted modes, proving that the proposed design satisfies the Universal Approximation Theorem within a single layer. To classically simulate the QONN training, the high-performance QuaNNTO library has been developed based on Wick-Isserlis expansion and Bogoliubov transformations, allowing multi-layer exact expectation values of non-Gaussian states without truncating the infinite-dimensional Hilbert space. Experiments on supervised learning and state-preparation tasks show balanced-resource efficiency with strong expressivity and generalization capabilities, illustrating the potential of the architecture for scalable photonic quantum machine learning and for quantum applications such as complex non-Gaussian gate synthesis.

quant-pharXiv:2512.05204•Dec 8, 2025•0 citations

Simulating lossy and partially distinguishable quantum optical circuits: theory, algorithms, and applications to experiment validation and state preparation

Jacob F. F. Bulmer, Javier Martínez-Cifuentes, Bryn A. Bell +1 more

To understand quantum optics experiments, we must perform calculations that consider the principal sources of noise, such as losses, spectral impurity, and partial distinguishability. In both discrete and continuous variable systems, these can be modeled as mixed Gaussian states over multiple modes. The modes are not all resolved by photon-number measurements and so require calculations on coarse-grained photon-number distributions. Existing methods can lead to a combinatorial explosion in the time complexity, making this task unfeasible for even moderately sized experiments of interest. We prove that the computation of this type of distribution can be done in exponential time, providing a combinatorial speed-up. We develop numerical techniques that allow us to determine coarse-grained photon number distributions of Gaussian states, as well as density matrix elements of heralded non-Gaussian states prepared in the presence of spectral impurity and partial distinguishability. These results offer significant speed-up and accuracy improvements to validation tests of both Fock and Gaussian boson samplers that rely on binned probability distributions. Moreover, they pave the way to a more efficient simulation of realistic photonic circuits, unlocking the ability to perform exact calculations at scales that were previously out of reach. In addition, our results, including loop Hafnian master theorems, may be of interest to the fields of combinatorics and graph theory.

arXiv:2412.17742•Advanced Photonics•Feb 18, 2026•1 citation

Efficient Qubit Simulation of Hybrid Oscillator-Qubit Quantum Computation

Xi Lu, Bojko N. Bakalov, Yuan Liu

We introduce a framework for simulating hybrid oscillator-qubit quantum processors on qubit-only systems through position encoding. By encoding continuous-variable position and momentum wave functions into qubit amplitudes, our method efficiently simulates all Gaussian and conditional Gaussian operations -- encompassing the phase-space instruction set (beam splitter, single-qubit rotation, conditional displacement) and extending to squeezing, conditional squeezing, conditional rotation, and conditional beam splitter -- using $O\!\left(\log^2\!\left(Γ+ \log(1/ε)\right)\right)$ qubit gates per hybrid gate, where $Γ$ is the Fock-level bound and $ε$ is the target precision. This polylogarithmic per-gate complexity represents an exponential improvement over Fock basis encoding approaches, which require exponential quantum or classical resources in the number of qubits per mode. We provide rigorous numerical characterization of quantum Fourier transform errors for Fock-bounded states, enabling precise resource estimation for practical implementations. This work establishes that hybrid oscillator-qubit algorithms can be implemented on qubit processors with polynomial overhead, providing new insights into the computational power trade-offs between discrete-variable and hybrid continuous-discrete-variable quantum computing.

quant-pharXiv:2603.09233•Mar 11, 2026•0 citations
Abstract Quantum photonic simulators are pivotal for designing and analyzing quantum photonic circuits that utilize both continuous-variable (CV) and discrete-variable (DV) encodings. This review critically examines the transition from Gaussian to non-Gaussian photonic models and the computational overhead that emerges from simulating systems with more than 20 modes or photon numbers exceeding 10 per mode. Gaussian states; such as coherent and squeezed states, are modeled efficiently using first-moment vectors and covariance matrices of dimension 2n×2n2n \times 2n2n×2n 2 n × 2 n , where n is the number of modes. Non-Gaussian elements like the cubic phase gate U=exp⁡(iγx^3)U = \exp (i \gamma \hat{x}^{3})U=exp(iγx^3) U = exp ( i γ x ˆ 3 ) and photon-subtracted states demand full Fock space representations and scale exponentially with photon number d2d^{2}d2 d 2 , where d is the Hilbert space dimension. We analyze simulators including , , and , benchmarking their support for tensor networks (bond dimension D >100), GPU acceleration, thus NVIDIA A100 with 1555 GB/s bandwidth, and SoC implementations such as Edge TPU with 8 TFLOPS. Special focus is given to noise models such as photon loss with transmissivity η , thermal noise modeled with nˉ=1.5\bar{n} = 1.5nˉ=1.5 n ¯ = 1.5 , and dark count Poisson distributions ( λ=0.1\lambda = 0.1λ=0.1 λ = 0.1 ). The review underscores scalable techniques and hybrid quantum-classical workflows vital for near-term photonic quantum computing.
Quantum photonic simulators have emerged as indispensable tools for modeling and optimizing quantum photonic circuits, bridging the gap between theoretical models and experimental implementations. This review explores the landscape of photonic quantum simulation, focusing on the transition from Gaussian to non-Gaussian models and the computational challenges associated with simulating large-scale photonic systems. Gaussian states and operations, which enable efficient simulations through covariance matrices and phase-space representations, serve as the foundation for photonic quantum computing. However, non-Gaussian states crucial for universal quantum computation introduce significant computational complexity, requiring advanced numerical techniques such as tensor networks and high-performance GPU acceleration. We evaluate the leading photonic quantum simulators, including Strawberry Fields, Piquasso, QuTiP SimulaQron, Perceval, and QuantumOPtics.jl analyzing their capabilities in handling continuous-variable (CV) and discrete-variable (DV) quantum systems. Special attention is given to hardware-accelerated methods, including GPU-based tensor network approaches, machine learning integration, and hybrid quantum-classical workflows. Furthermore, we investigate noise modeling techniques, such as photon loss and dark counts, and their impact on simulation accuracy. As photonic quantum computing moves toward practical implementations, advancements in high-performance computing (HPC) architectures, such as tensor processing units (TPUs) and system-on-a-chip (SoC) solutions, are accelerating the field. This review highlights emerging trends, challenges, and future directions for developing scalable and efficient photonic quantum simulators.
Photonics quantum networks are pivotal for quantum technologies, facilitating efficient information distribution and manipulation through quantum states of light. Continuous-variable (CV) cluster states, essential resources for Measurement-Based Quantum Computing, can be deterministically generated using squeezed states of light and mode-basis changes [1]. However, Gaussian states like squeezed states or cluster states, can be efficiently simulated with a classical computer [2]. Thus, to achieve quantum advantage, non-Gaussianity should be introduced, which can be accomplished by subtracting a photon from a squeezed state [3].
Hybrid quantum systems combining discrete-variable (DV) and continuous-variable (CV) subsystems arise across a broad range of physical platforms and enable quantum information processing beyond purely qubit-based designs. Their classical simulation, however, is challenging since computational costs of both CV and DV systems exponentially increase. In this paper, we introduce a simulation framework for CV--DV hybrid systems based on linear combination of Gaussian (LCoG) decomposition. We decompose the hybrid density matrix into blocks in a DV basis and represent each block as an LCoG expansion. Within the representation, Gaussian operations on the bosonic subsystem independently act on each Gaussian function. We derive analytic formulas describing how complex Gaussian functions transform under controlled Gaussian operations. We further extend the framework to controlled Gaussian channels with state-dependent Gaussian noise. The method avoids a Fock-space truncation for the CV subsystem, and the cost of updating each Gaussian term scales polynomially with the number of modes. The framework provides a general computational tool for analyzing hybrid quantum algorithms, quantum simulation protocols, and non-Gaussian state generation schemes. As an example, we simulate Gottesman--Kitaev--Preskill (GKP) state generation based on a cavity-QED system.
Continuous-variables (CV) quantum optics is a natural formalism for neural networks (NNs) due to its ability to reproduce the information processing of such trainable interconnected systems. In quantum optics, Gaussian operators induce affine mappings on the quadratures of optical modes while non-Gaussian resources (the challenging piece for physical implementation) originate the nonlinear effects, unlocking quantum analogs of an artificial neuron. This work presents a novel experimentally-feasible framework for continuous-variable quantum optical neural networks (QONNs) developed with available photonic components: coherent states as input encoding, a general Gaussian transformation followed by multi-mode photon subtractions as the processing layer, and homodyne detection as outputs readout. The closed-form expressions of such architecture are derived demonstrating the family of adaptive activations and the quantum-optical neurons that emerge from the amount of photon-subtracted modes, proving that the proposed design satisfies the Universal Approximation Theorem within a single layer. To classically simulate the QONN training, the high-performance QuaNNTO library has been developed based on Wick-Isserlis expansion and Bogoliubov transformations, allowing multi-layer exact expectation values of non-Gaussian states without truncating the infinite-dimensional Hilbert space. Experiments on supervised learning and state-preparation tasks show balanced-resource efficiency with strong expressivity and generalization capabilities, illustrating the potential of the architecture for scalable photonic quantum machine learning and for quantum applications such as complex non-Gaussian gate synthesis.
To understand quantum optics experiments, we must perform calculations that consider the principal sources of noise, such as losses, spectral impurity, and partial distinguishability. In both discrete and continuous variable systems, these can be modeled as mixed Gaussian states over multiple modes. The modes are not all resolved by photon-number measurements and so require calculations on coarse-grained photon-number distributions. Existing methods can lead to a combinatorial explosion in the time complexity, making this task unfeasible for even moderately sized experiments of interest. We prove that the computation of this type of distribution can be done in exponential time, providing a combinatorial speed-up. We develop numerical techniques that allow us to determine coarse-grained photon number distributions of Gaussian states, as well as density matrix elements of heralded non-Gaussian states prepared in the presence of spectral impurity and partial distinguishability. These results offer significant speed-up and accuracy improvements to validation tests of both Fock and Gaussian boson samplers that rely on binned probability distributions. Moreover, they pave the way to a more efficient simulation of realistic photonic circuits, unlocking the ability to perform exact calculations at scales that were previously out of reach. In addition, our results, including loop Hafnian master theorems, may be of interest to the fields of combinatorics and graph theory.
We introduce a framework for simulating hybrid oscillator-qubit quantum processors on qubit-only systems through position encoding. By encoding continuous-variable position and momentum wave functions into qubit amplitudes, our method efficiently simulates all Gaussian and conditional Gaussian operations -- encompassing the phase-space instruction set (beam splitter, single-qubit rotation, conditional displacement) and extending to squeezing, conditional squeezing, conditional rotation, and conditional beam splitter -- using O ⁣(log⁡2 ⁣(Γ+log⁡(1/ε)))O\!\left(\log^2\!\left(Γ+ \log(1/ε)\right)\right)O(log2(Γ+log(1/ε))) qubit gates per hybrid gate, where ΓΓΓ is the Fock-level bound and εεε is the target precision. This polylogarithmic per-gate complexity represents an exponential improvement over Fock basis encoding approaches, which require exponential quantum or classical resources in the number of qubits per mode. We provide rigorous numerical characterization of quantum Fourier transform errors for Fock-bounded states, enabling precise resource estimation for practical implementations. This work establishes that hybrid oscillator-qubit algorithms can be implemented on qubit processors with polynomial overhead, providing new insights into the computational power trade-offs between discrete-variable and hybrid continuous-discrete-variable quantum computing.