Boosting Gaussian Boson Sampling using Optical Parametric Amplification Networks

• URL: http://arxiv.org/abs/2512.00753v1
• Date: Sun, 30 Nov 2025 06:13:58 GMT
• Title: Boosting Gaussian Boson Sampling using Optical Parametric Amplification Networks
• Authors: Yukuan Zhao, Xiao-Ye Xu, Chuan-Feng Li, Guang-Can Guo,
• Abstract summary: We propose a nonlinear photonic architecture based on optical parametric amplifiers arranged in an interferometer network.<n>We numerically show that entanglement scales linearly with both the OPA gain and network depth in the lossless limit.<n>Our results demonstrate that OPA-boosted GBS preserves computational hardness in noisy environments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gaussian Boson Sampling (GBS) provides a route toward demonstrating quantum computational advantage. However, optical loss, which reduces the entanglement in the system, can render GBS results classically simulable. We propose a nonlinear photonic architecture based on optical parametric amplifiers (OPAs) arranged in an interferometer network. This active configuration amplifies quantum correlations within the circuit while preserving the #P-hard Hafnian structure of the output probabilities. Using logarithmic negativity, we numerically show that entanglement scales linearly with both the OPA gain and network depth in the lossless limit, and maintains linear scaling with the number of modes under realistic loss rate. These scaling behaviors suggest that classical simulation in lossy scenarios remains computationally intractable. Our results demonstrate that OPA-boosted GBS preserves computational hardness in noisy environments, offering a more effective implementations of near-term photonic quantum computers.

Related papers

• Exponentially enhanced sensing through nonreciprocal light propagation [42.02177719060884]
  Non-reciprocal light propagation allows detecting small perturbations with a signal-to-noise ratio that scales exponentially with system size.<n>Our results open a new paradigm in non-Hermitian sensing, with potential applications in remote sensing including the optical readout of superconducting circuits.
  arXiv  Detail & Related papers  (2025-11-21T02:17:33Z)
• Hybrid quantum-classical algorithm for near-optimal planning in POMDPs [39.682133213072554]
  Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments.<n>Recent advances demonstrate that inference on sparse Bayesian networks can be accelerated using quantum rejection sampling combined with amplitude amplification.<n>We introduce Quantum Bayesian Reinforcement Learning (QBRL), a hybrid quantum-classical look-ahead algorithm for model-based RL in partially observable environments.
  arXiv  Detail & Related papers  (2025-07-24T17:42:30Z)
• Boundaries for quantum advantage with single photons and loop-based time-bin interferometers [37.28808413070634]
  Loop-based boson samplers interfere photons in the time degree of freedom using a sequence of delay lines.<n>We propose a method to exploit this loop-based structure to more efficiently classically sample from such systems.
  arXiv  Detail & Related papers  (2024-11-25T19:13:20Z)
• Photonic Quantum Receiver Attaining the Helstrom Bound [0.9674145073701151]
  We propose an efficient decomposition scheme for a quantum receiver that attains the Helstrom bound in the low-photon regime for discriminating binary coherent states. We account for realistic conditions by examining the impact of photon loss and imperfect photon detection, including the presence of dark counts. Our scheme motivates testing quantum advantages with cubic-phase gates and designing photonic quantum computers to optimize symbol-by-symbol measurements in optical communication.
  arXiv  Detail & Related papers  (2024-10-29T07:08:39Z)
• Classical simulability of constant-depth linear-optical circuits with noise [0.0]
  Noise is one of the main obstacles to realizing quantum devices that achieve a quantum computational advantage.<n>In this work, we investigate the complexity of shallow-depth linear-optical circuits under the effects of photon loss and partial distinguishability.
  arXiv  Detail & Related papers  (2024-06-12T11:08:57Z)
• Improving Continuous-variable Quantum Channels with Unitary Averaging [37.69303106863453]
  We present a scheme of passive linear optical unitary averaging for protecting unknown Gaussian states transmitted through an optical channel. The scheme reduces the effect of phase noise on purity, squeezing and entanglement, thereby enhancing the channel via a probabilistic error correcting protocol.
  arXiv  Detail & Related papers  (2023-11-17T10:10:19Z)
• Classical simulations of noisy variational quantum circuits [0.0]
  Noisely affects quantum computations so that they not only become less accurate but also easier to simulate classically as systems scale up. We construct a classical simulation algorithm, LOWESA, for estimating expectation values of noisy parameterised quantum circuits.
  arXiv  Detail & Related papers  (2023-06-08T17:52:30Z)
• Simulating lossy Gaussian boson sampling with matrix product operators [7.33258560389563]
  We show that efficient tensor network simulations are likely possible under the $N_textoutproptosqrtN$ scaling of the number of surviving photons. We overcome previous challenges due to the large local space dimensions in Gaussian boson sampling with hardware acceleration.
  arXiv  Detail & Related papers  (2023-01-30T12:10:39Z)
• Protecting the quantum interference of cat states by phase-space compression [45.82374977939355]
  Cat states with their unique phase-space interference properties are ideal candidates for understanding quantum mechanics. They are highly susceptible to photon loss, which inevitably diminishes their quantum non-Gaussian features. Here, we protect these non-Gaussian features by compressing the phase-space distribution of a cat state.
  arXiv  Detail & Related papers  (2022-12-02T16:06:40Z)
• Optical Neural Ordinary Differential Equations [44.97261923694945]
  We propose the optical neural ordinary differential equations (ON-ODE) architecture that parameterizes the continuous dynamics of hidden layers with optical ODE solvers. The ON-ODE comprises the PNNs followed by the photonic integrator and optical feedback loop, which can be configured to represent residual neural networks (ResNet) and recurrent neural networks with effectively reduced chip area occupancy.
  arXiv  Detail & Related papers  (2022-09-26T04:04:02Z)
• Simulating the Mott transition on a noisy digital quantum computer via Cartan-based fast-forwarding circuits [62.73367618671969]
  Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model. Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models. This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
  arXiv  Detail & Related papers  (2021-12-10T17:32:15Z)
• Fault-tolerant quantum computation with static linear optics [0.0]
  In this work we propose a topologically error-corrected architecture that does away with these elements at no cost. Our computer consists of three modules: a 2D array of probabilistic sources of GKP states; a depth-four circuit of static beamsplitters, phase shifters, and single-time-step delay lines. The symmetry of our proposed circuit allows us to combine the effects of finite squeezing and uniform photon loss within the noise model, resulting in more comprehensive threshold estimates.
  arXiv  Detail & Related papers  (2021-04-07T16:43:34Z)
• Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
  Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry. New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications. We present a novel technique to reconstruct the transfer matrix of linear optical networks.
  arXiv  Detail & Related papers  (2020-10-01T16:04:22Z)
• Efficient sampling from shallow Gaussian quantum-optical circuits with local interactions [0.9786690381850354]
  We prove that a classical computer can efficiently sample from the photon-number probability distribution of a Gaussian state prepared by using an optical circuit that is shallow and local. Since sampling from deep optical circuits with exponential-scaling photon loss is classically simulable, our results pose a challenge to the feasibility of demonstrating quantum supremacy on photonic platforms with local interactions.
  arXiv  Detail & Related papers  (2020-09-24T17:10:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.