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

arXiv:2604.06679

Environment-Assisted Decoherence Suppression of Optical Non-Gaussian States

Akihiro Machinaga, Naoki Aritomi, Ryoga Sakurada, Daichi Okuno, Keitaro Anai, Takahiro Kashiwazaki, Takeshi Umeki, Shigehito Miki, Masahiro Yabuno, Hirotaka Terai, Petr Marek, Radim Filip, Shuntaro Takeda

quant-phSubmitted Apr 8, 2026• Announced Apr 9, 2026• 0 citations
arXivHTMLPDF

Abstract

Optical loss is a common bottleneck in photonic quantum information processing, undermining the quantum advantage over classical approaches. Although several countermeasures, such as quantum distillation and error correction, have been proposed, they typically require experimentally demanding non-Gaussian operations. Here, we demonstrate a Gaussian-only scheme that suppresses loss-induced decoherence for general, unknown optical quantum states. By injecting a squeezed vacuum state into an environment of the loss channel and performing feedforward based on environmental monitoring, the scheme effectively suppresses loss-induced noise. Our programmable loop-based optical circuit allows us to implement the scheme for several types of loss-sensitive non-Gaussian states under various loss conditions for up to five steps, and directly compare the results with the unsuppressed case. Our results show that the scheme consistently mitigates state degradation, preserving higher fidelity and Wigner negativity than without suppression. This approach can be applied to mitigating a broad class of errors in optical systems and extending quantum memory lifetimes. Moreover, it is compatible with other loss-suppression techniques and extendable to physical platforms beyond optics, offering a promising route toward reducing the overhead required for fault-tolerant quantum information processing.

Comments
16 pages, 9 figures

Related papers

Decoherence Suppression of Optical Non-Gaussian States via Environment-Assisted Error Correction

Akihiro Machinaga, N. Aritomi, Ryoga Sakurada +9 more

We experimentally demonstrate multi-step decoherence suppression for various optical non-Gaussian quantum states using an environment-assisted error correction scheme. This scheme is applicable to general unknown states, leading to longer-lifetime quantum memories and larger-scale quantum computing.

•Jan 1, 2026•0 citations

All-optical Implementation of Generalized Quantum Teleportation

Takaya Hoshi, Akito Kawasaki, Xiruo Yan +7 more

Measurement-based continuous-variable optical quantum computing inherently offers high-speed, large-scale operations, yet its practical performance remains constrained by the processing latencies and throughput bottlenecks imposed by classical electronic feedforward circuits. To overcome these limitations, we propose a loss-tolerant, all-optical feedforward (AOFF) architecture for generalized quantum teleportation capable of executing arbitrary linear operations. Quantitative noise analysis under realistic device parameters demonstrates that the architecture successfully suppresses hardware-induced noise floor, confirming its compatibility with fault-tolerant quantum computing requirements. By eliminating optoelectronic conversions, this scheme enables continuous high-throughput operations that drastically reduce circuit runtime. Ultimately, this approach delivers a noise-resilient platform that reconciles operational versatility with the intrinsic speed and bandwidth of optical quantum information processing.

quant-pharXiv:2606.22736•Jun 23, 2026•0 citations

Quantum error cancellation in photonic systems: Undoing photon losses

Adam Taylor, Gabriele Bressanini, Hyukjoon Kwon +1 more

Real photonic devices are subject to photon losses that can decohere quantum information encoded in the system. In the absence of full fault tolerance, quantum error mitigation techniques have been introduced to help manage errors in noisy quantum devices. In this paper, we introduce an error mitigation protocol inspired by probabilistic error cancellation (a popular error mitigation technique in discrete variable systems) for continuous variable systems. We show that our quantum error cancellation protocol can undo photon losses in expectation value estimation tasks. To do this, we analytically derive the (nonphysical) inverse photon loss channel and decompose it into a sum over physically realizable channels with potentially negative coefficients. The bias of our ideal expectation value estimator can be made arbitrarily small at the cost of increasing the sampling overhead. The protocol requires a noiseless amplification followed by a series of photon subtractions. While these operations can be implemented probabilistically, for certain classes of initial state one can avoid the burden of carrying out the amplification and photon subtractions by leveraging Monte Carlo methods to give an unbiased estimate of the ideal expectation value. We validate our proposed mitigation protocol by simulating the scheme on squeezed vacuum states, cat states, and entangled coherent states. Published by the American Physical Society 2024

arXiv:2403.05252•Physical Review A•Aug 27, 2024•8 citations

Extrapolative Quantum Error Mitigation in Continuous-Variable Systems beyond the Training Horizon

Jingpeng Zhang, Shengyong Li, Jie Han +3 more

Continuous-variable (CV) quantum systems provide a versatile platform for quantum information processing, in which quantum states can be represented in the quadrature phase space. In realistic implementations, environmental noise, primarily photon loss and dephasing, progressively degrades these states. Machine-learning-based quantum error mitigation (QEM) has recently emerged as a promising approach to suppress such noise; however, existing methods are typically limited to the training horizon and require training data that cover the entire evolution, which is experimentally demanding. Here we introduce a framework for extrapolative quantum error mitigation based on a time-conditioned Swin Transformer. By explicitly embedding the evolution time via adaptive layer normalization, the model learns a correction map that accounts for the continuous accumulation of noise while capturing nonlocal phase-space correlations. Numerical simulations under both Markovian and non-Markovian noise demonstrate accurate state recovery in the long-time regime, where existing approaches deteriorate. Our results establish extrapolative QEM as a practical route to mitigating noise in CV quantum systems without exhaustive training data.

quant-pharXiv:2603.08548•Mar 10, 2026•0 citations

DifGa: Differentiable Error Mitigation for Multi-Mode Gaussian and Non-Gaussian Noise in Quantum Photonic Circuits

Dennis Delali Kwesi Wayo, Rodrigo Alves Dias, Leonardo Goliatt +1 more

We introduce DifGa, a fully differentiable error-mitigation framework for continuous-variable (CV) quantum photonic circuits operating under Gaussian loss and weak non-Gaussian noise. The approach is demonstrated using analytic simulations with the default.gaussian backend of PennyLane, where quantum states are represented by first and second moments and optimized end-to-end via automatic differentiation. Gaussian loss is modeled as a beam splitter interaction with an environmental vacuum mode of transmissivity $η\in [0.3,0.95]$, while non-Gaussian phase noise is incorporated through a differentiable Monte-Carlo mixture of random phase rotations with jitter amplitudes $δ\in [0,0.7]$. The core architecture employs a multi-mode Gaussian circuit consisting of a signal, ancilla, and environment mode. Input states are prepared using squeezing and displacement operations with parameters $(r_s,\varphi_s,α)=(0.60,0.30,0.80)$ and $(r_a,\varphi_a)=(0.40,0.10)$, followed by an entangling beam splitter with angles $(θ,φ)=(0.70,0.20)$. Error mitigation is achieved by appending a six-parameter trainable Gaussian recovery layer comprising local phase rotations and displacements, optimized by minimizing a quadratic loss on the signal-mode quadratures $\langle \hat{x}_0\rangle$ and $\langle \hat{p}_0\rangle$ using gradient descent with fixed learning rate $0.06$ and identical initialization across experiments. Under pure Gaussian loss, the optimized recovery suppresses reconstruction error to near machine precision ($<10^{-30}$) for moderate loss ($η\ge 0.5$). When non-Gaussian phase noise is present, noise-aware training using Monte Carlo averaging yields robust generalization, reducing error by more than an order of magnitude compared to Gaussian-trained recovery at large phase jitter. Runtime benchmarks confirm linear scaling with the number of Monte Carlo samples.

quant-pharXiv:2512.23776•Journal of Physics Photonics•Dec 30, 2025•0 citations

Systematic construction of digital autonomous quantum error correction for state preparation and error suppression via conditional Gaussian operations

Keitaro Anai, Suguru Endo, Shuntaro Takeda +1 more

In continuous-variable quantum computing, autonomous quantum error correction (QEC) can dissipatively steer a noisy quantum state into a target state or manifold, enabling robust quantum information processing without explicit syndrome measurements and feedback. Here, we propose a nullifier-based digital autonomous QEC enabled by conditional Gaussian operations. By designing jump operators for target nullifiers and compiling the resulting Lindbladian into a Trotterized sequence of elementary conditional Gaussian operations, we demonstrate two use cases: (i) deterministic preparation of non-Gaussian resource states for universal computation, including finitely squeezed cubic phase states and approximate trisqueezed states, and (ii) autonomous suppression of dephasing error for cat and squeezed cat states. We provide explicit gate decompositions for the required conditional Gaussian operations and numerically evaluate the performance under realistic imperfections, including photon loss in the bosonic mode and ancillary-qubit decoherence. Our results clarify the resource requirements and trade-offs, such as circuit depth, time-step choices, and the required set of conditional Gaussian operations, for scalable, gate-level implementations of autonomous state preparation and error suppression.

quant-pharXiv:2603.21598•Mar 24, 2026•0 citations
Optical loss is a common bottleneck in photonic quantum information processing, undermining the quantum advantage over classical approaches. Although several countermeasures, such as quantum distillation and error correction, have been proposed, they typically require experimentally demanding non-Gaussian operations. Here, we demonstrate a Gaussian-only scheme that suppresses loss-induced decoherence for general, unknown optical quantum states. By injecting a squeezed vacuum state into an environment of the loss channel and performing feedforward based on environmental monitoring, the scheme effectively suppresses loss-induced noise. Our programmable loop-based optical circuit allows us to implement the scheme for several types of loss-sensitive non-Gaussian states under various loss conditions for up to five steps, and directly compare the results with the unsuppressed case. Our results show that the scheme consistently mitigates state degradation, preserving higher fidelity and Wigner negativity than without suppression. This approach can be applied to mitigating a broad class of errors in optical systems and extending quantum memory lifetimes. Moreover, it is compatible with other loss-suppression techniques and extendable to physical platforms beyond optics, offering a promising route toward reducing the overhead required for fault-tolerant quantum information processing.
Measurement-based continuous-variable optical quantum computing inherently offers high-speed, large-scale operations, yet its practical performance remains constrained by the processing latencies and throughput bottlenecks imposed by classical electronic feedforward circuits. To overcome these limitations, we propose a loss-tolerant, all-optical feedforward (AOFF) architecture for generalized quantum teleportation capable of executing arbitrary linear operations. Quantitative noise analysis under realistic device parameters demonstrates that the architecture successfully suppresses hardware-induced noise floor, confirming its compatibility with fault-tolerant quantum computing requirements. By eliminating optoelectronic conversions, this scheme enables continuous high-throughput operations that drastically reduce circuit runtime. Ultimately, this approach delivers a noise-resilient platform that reconciles operational versatility with the intrinsic speed and bandwidth of optical quantum information processing.
Real photonic devices are subject to photon losses that can decohere quantum information encoded in the system. In the absence of full fault tolerance, quantum error mitigation techniques have been introduced to help manage errors in noisy quantum devices. In this paper, we introduce an error mitigation protocol inspired by probabilistic error cancellation (a popular error mitigation technique in discrete variable systems) for continuous variable systems. We show that our quantum error cancellation protocol can undo photon losses in expectation value estimation tasks. To do this, we analytically derive the (nonphysical) inverse photon loss channel and decompose it into a sum over physically realizable channels with potentially negative coefficients. The bias of our ideal expectation value estimator can be made arbitrarily small at the cost of increasing the sampling overhead. The protocol requires a noiseless amplification followed by a series of photon subtractions. While these operations can be implemented probabilistically, for certain classes of initial state one can avoid the burden of carrying out the amplification and photon subtractions by leveraging Monte Carlo methods to give an unbiased estimate of the ideal expectation value. We validate our proposed mitigation protocol by simulating the scheme on squeezed vacuum states, cat states, and entangled coherent states. Published by the American Physical Society 2024
Continuous-variable (CV) quantum systems provide a versatile platform for quantum information processing, in which quantum states can be represented in the quadrature phase space. In realistic implementations, environmental noise, primarily photon loss and dephasing, progressively degrades these states. Machine-learning-based quantum error mitigation (QEM) has recently emerged as a promising approach to suppress such noise; however, existing methods are typically limited to the training horizon and require training data that cover the entire evolution, which is experimentally demanding. Here we introduce a framework for extrapolative quantum error mitigation based on a time-conditioned Swin Transformer. By explicitly embedding the evolution time via adaptive layer normalization, the model learns a correction map that accounts for the continuous accumulation of noise while capturing nonlocal phase-space correlations. Numerical simulations under both Markovian and non-Markovian noise demonstrate accurate state recovery in the long-time regime, where existing approaches deteriorate. Our results establish extrapolative QEM as a practical route to mitigating noise in CV quantum systems without exhaustive training data.
We introduce DifGa, a fully differentiable error-mitigation framework for continuous-variable (CV) quantum photonic circuits operating under Gaussian loss and weak non-Gaussian noise. The approach is demonstrated using analytic simulations with the default.gaussian backend of PennyLane, where quantum states are represented by first and second moments and optimized end-to-end via automatic differentiation. Gaussian loss is modeled as a beam splitter interaction with an environmental vacuum mode of transmissivity η∈[0.3,0.95]η\in [0.3,0.95]η∈[0.3,0.95], while non-Gaussian phase noise is incorporated through a differentiable Monte-Carlo mixture of random phase rotations with jitter amplitudes δ∈[0,0.7]δ\in [0,0.7]δ∈[0,0.7]. The core architecture employs a multi-mode Gaussian circuit consisting of a signal, ancilla, and environment mode. Input states are prepared using squeezing and displacement operations with parameters (rs,φs,α)=(0.60,0.30,0.80)(r_s,\varphi_s,α)=(0.60,0.30,0.80)(rs​,φs​,α)=(0.60,0.30,0.80) and (ra,φa)=(0.40,0.10)(r_a,\varphi_a)=(0.40,0.10)(ra​,φa​)=(0.40,0.10), followed by an entangling beam splitter with angles (θ,φ)=(0.70,0.20)(θ,φ)=(0.70,0.20)(θ,φ)=(0.70,0.20). Error mitigation is achieved by appending a six-parameter trainable Gaussian recovery layer comprising local phase rotations and displacements, optimized by minimizing a quadratic loss on the signal-mode quadratures ⟨x^0⟩\langle \hat{x}_0\rangle⟨x^0​⟩ and ⟨p^0⟩\langle \hat{p}_0\rangle⟨p^​0​⟩ using gradient descent with fixed learning rate 0.060.060.06 and identical initialization across experiments. Under pure Gaussian loss, the optimized recovery suppresses reconstruction error to near machine precision (<10−30<10^{-30}<10−30) for moderate loss (η≥0.5η\ge 0.5η≥0.5). When non-Gaussian phase noise is present, noise-aware training using Monte Carlo averaging yields robust generalization, reducing error by more than an order of magnitude compared to Gaussian-trained recovery at large phase jitter. Runtime benchmarks confirm linear scaling with the number of Monte Carlo samples.
In continuous-variable quantum computing, autonomous quantum error correction (QEC) can dissipatively steer a noisy quantum state into a target state or manifold, enabling robust quantum information processing without explicit syndrome measurements and feedback. Here, we propose a nullifier-based digital autonomous QEC enabled by conditional Gaussian operations. By designing jump operators for target nullifiers and compiling the resulting Lindbladian into a Trotterized sequence of elementary conditional Gaussian operations, we demonstrate two use cases: (i) deterministic preparation of non-Gaussian resource states for universal computation, including finitely squeezed cubic phase states and approximate trisqueezed states, and (ii) autonomous suppression of dephasing error for cat and squeezed cat states. We provide explicit gate decompositions for the required conditional Gaussian operations and numerically evaluate the performance under realistic imperfections, including photon loss in the bosonic mode and ancillary-qubit decoherence. Our results clarify the resource requirements and trade-offs, such as circuit depth, time-step choices, and the required set of conditional Gaussian operations, for scalable, gate-level implementations of autonomous state preparation and error suppression.