Task-Oriented Gaussian Optimization for Non-Gaussian Resources in Continuous-Variable Quantum Computation

• URL: http://arxiv.org/abs/2509.15747v1
• Date: Fri, 19 Sep 2025 08:22:35 GMT
• Title: Task-Oriented Gaussian Optimization for Non-Gaussian Resources in Continuous-Variable Quantum Computation
• Authors: Boxuan Jing, Feng-Xiao Sun, Qiongyi He,
• Abstract summary: In continuous-variable systems, non-Gaussian resources are essential for achieving universal quantum computation.<n>We present a Gaussian optimization protocol that systematically refines the non-Gaussian resources.<n>Our protocol offers an experimentally feasible approach to enhance gate fidelity in magic-state-based quantum computation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In continuous-variable systems, non-Gaussian resources are essential for achieving universal quantum computation that lies beyond classical simulation. Among the candidate states, the cubic phase state stands out as the simplest form of single-mode non-Gaussian resource, yet its experimental preparation still remains a great challenge. Although a variety of approximate schemes have been proposed to simulate the cubic phase state, they often fall short when deployed in concrete quantum tasks. In this work, we present a Gaussian optimization protocol that systematically refines the non-Gaussian resources, which significantly improves the performance of both magic-state-based and measurement-based quantum computation. Leveraging task-specific Gaussian operations on approximate cubic phase states, our protocol offers an experimentally feasible approach to enhance gate fidelity in magic-state-based quantum computation and reduce the variance of nonlinear quadrature measurement in measurement-based quantum computation. Building on this framework, we further propose a task-oriented non-Gaussian state preparation scheme based on superpositions in the Fock basis followed by squeezing and displacement. This strategy enables direct tailoring of resource states to specific task goals. Owing to its flexibility and generality, our framework provides a powerful and broadly applicable tool for enhancing performance across a wide range of continuous-variable quantum information protocols.

Related papers

• Distributed Quantum Gaussian Processes for Multi-Agent Systems [2.526124003343442]
  Quantum computing offers the potential to overcome limitations by embedding data into exponentially large Hilbert spaces.<n>We propose a Distributed Quantum Gaussian Process (DQGP) method in a multiagent setting to enhance modeling capabilities and scalability.<n>We use real-world, non-stationary elevation datasets of NASA's Shuttle Radar Topography Mission and synthetic datasets generated by Quantum Gaussian Processes.
  arXiv  Detail & Related papers  (2026-02-16T18:46:23Z)
• Quantum Phase Estimation Beyond the Gaussian Limit [0.3495246564946556]
  A critical milestone beyond the standard quantum limit is surpassing the Gaussian bound.<n>Certain non-Gaussian states can outperform this Gaussian bound within an intermediate energy range.<n>This work sheds light on the fundamental impact of non-Gaussianity and asymmetry on metrological tasks.
  arXiv  Detail & Related papers  (2025-08-18T16:03:28Z)
• Efficient Gaussian State Preparation in Quantum Circuits [4.930778301847907]
  We propose and analyze a circuit-based approach that starts with single-qubit rotations to form an exponential amplitude profile.<n>We demonstrate that this procedure achieves high fidelity with the target Gaussian state.<n>We conclude that the proposed technique is a promising route to make Gaussian states accessible on noisy quantum hardware.
  arXiv  Detail & Related papers  (2025-07-27T15:15:20Z)
• Calibration of Quantum Devices via Robust Statistical Methods [45.464983015777314]
  We numerically analyze advanced statistical methods for Bayesian inference against the state-of-the-art in quantum parameter learning.<n>We show advantages of these approaches over existing ones, namely under multi-modality and high dimensionality.<n>Our findings have applications in challenging quantumcharacterization tasks namely learning the dynamics of open quantum systems.
  arXiv  Detail & Related papers  (2025-07-09T15:22:17Z)
• Adaptive Non-Gaussian Quantum State Engineering [0.0]
  Non-Gaussian quantum states of bosons are a key resource in quantum information science.<n>In this work, we extend on existing passive architectures and explore a broad set of adaptive schemes.
  arXiv  Detail & Related papers  (2025-02-20T19:04:55Z)
• Application of Langevin Dynamics to Advance the Quantum Natural Gradient Optimization Algorithm [47.47843839099175]
  A Quantum Natural Gradient (QNG) algorithm for optimization of variational quantum circuits has been proposed recently.<n>Momentum-QNG is more effective to escape local minima and plateaus in the variational parameter space.
  arXiv  Detail & Related papers  (2024-09-03T15:21:16Z)
• Sufficient condition for universal quantum computation using bosonic circuits [44.99833362998488]
  We focus on promoting circuits that are otherwise simulatable to computational universality. We first introduce a general framework for mapping a continuous-variable state into a qubit state. We then cast existing maps into this framework, including the modular and stabilizer subsystem decompositions.
  arXiv  Detail & Related papers  (2023-09-14T16:15:14Z)
• Resources for bosonic quantum computational advantage [0.0]
  We show that every bosonic quantum computation can be recast into a continuous-variable sampling computation. We derive a general classical algorithm for the strong simulation of bosonic computations.
  arXiv  Detail & Related papers  (2022-07-24T17:50:20Z)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)
• Gaussian conversion protocols for cubic phase state generation [104.23865519192793]
  Universal quantum computing with continuous variables requires non-Gaussian resources. The cubic phase state is a non-Gaussian state whose experimental implementation has so far remained elusive. We introduce two protocols that allow for the conversion of a non-Gaussian state to a cubic phase state.
  arXiv  Detail & Related papers  (2020-07-07T09:19:49Z)

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.