Measurement-Efficient Variational Quantum Linear Solver for Carleman-Linearized Nonlinear Dynamics
- URL: http://arxiv.org/abs/2605.15366v1
- Date: Thu, 14 May 2026 19:45:27 GMT
- Title: Measurement-Efficient Variational Quantum Linear Solver for Carleman-Linearized Nonlinear Dynamics
- Abstract summary: We show that Carleman linearization accurately approximates the weakly nonlinear Duffing equation.<n>We deploy VQLS with symmetry-grouped Hadamard Test evaluations under both global and local cost formulations.<n>Across block-banded test cases, each method achieves near-unity fidelity and vanishing relative residuals.
- Score: 1.4180331276028664
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present hybrid quantum-classical pipelines for solving the Duffing equation that leverage Carleman linearization and the Variational Quantum Linear Solver (VQLS). First, we demonstrate that Carleman linearization accurately approximates the weakly nonlinear Duffing equation, with errors diminishing as the truncation order increases. Next, across IBM and Xanadu platforms, we deploy VQLS with symmetry-grouped Hadamard Test evaluations under both global and local cost formulations, compare distinct Hermitianization within a common cost framework, and benchmark hardware-efficient ansatz architectures under a fixed Hermitianization. Across block-banded test cases, each method achieves near-unity fidelity and vanishing relative residuals. These results show that topology-agnostic ansatz, optimized Hermitianization, and efficient cost formulation enable VQLS to recover quantum states proportional to classical solutions for Carleman-structured systems, providing a portable recipe for quantum-in-the-loop simulation of nonlinear dynamics.
Related papers
- Scalable Lindblad Noise Learning via Stochastic Tensor-Network Simulation [48.887627688666974]
Learning dissipation rates in large-scale open quantum systems is a major obstacle for near-term quantum technologies.<n>Here, we propose a scalable noise-learning framework for Lindblad dissipation rates that combines a simulation method, the Jump Method (TJM)<n>We demonstrate the approach on two noise models in the Ising model, in which independent dissipation rates are learned for each site up to $N_mathrmsite=16$.<n>This combination of scalable numerics and rigorous theoretical guarantees positions TJM-based noise learning as a practical foundation for characterizing dissipation in large quantum devices.
arXiv Detail & Related papers (2026-08-25T15:07:11Z) - QUBO Modeling of Module Learning With Errors: Stability and Scaling in Post-Quantum Cryptography [0.0]
Post-quantum cryptography relies on the hardness of the Learning With Errors (LWE) and Module Learning With Errors (MLWE) problems.<n>This work introduces a constructive framework for encoding small MLWE instances as Quadratic Unconstrained Binary Optimization (QUBO) models.
arXiv Detail & Related papers (2026-07-07T08:05:45Z) - Approximate Quantum Linear Solvers for Hybrid CFD: End-to-End Analysis with a Chebyshev-LCU Approach [0.0]
We analyze how an approximate quantum linear solver affects the convergence of the overall CFD iteration.<n>We develop an approximate qubitization-based solver (Cheb-LCU) that can reduce quantum resource requirements.
arXiv Detail & Related papers (2026-05-31T07:20:42Z) - Quantum Algorithms for Nonlinear Differential Equations via Pivot-Shifted Carleman Linearization [3.4002853946164726]
We develop a pivot-shifted framework for quantum algorithms solving quadratic nonlinear ordinary differential equations.<n>By shifting the dynamics by a pivot state prior to Carleman lifting, we enlarge the class of nonlinear systems that can be efficiently simulated on quantum computers.
arXiv Detail & Related papers (2026-05-19T16:26:02Z) - Efficient Adjoint Matching for Fine-tuning Diffusion Models [47.05085426361986]
We propose EAM, which substantially improves training efficiency by reformulating the SOC problem with a textit base drift and a textitterminal cost.<n>EAM converges up to 4x faster than AM and matches or surpasses it across various metrics including PickScore, ImageReward, HPSv2.1, CLIPScore and Aesthetics.
arXiv Detail & Related papers (2026-05-12T03:55:12Z) - Adaptive-Growth Randomized Neural Networks for Level-Set Computation of Multivalued Nonlinear First-Order PDEs with Hyperbolic Characteristics [38.23142730599331]
This paper proposes an Adaptive-Growth Randomized Neural Network (AG-RaNN) method for computing multivalued solutions of nonlinear first-order PDEs with hyperbolic characteristics.<n>Such solutions arise in geometric optics, seismic waves, semiclassical limit of quantum dynamics and high frequency limit of linear waves.
arXiv Detail & Related papers (2026-03-01T13:16:25Z) - Quantum algorithms for viscosity solutions to nonlinear Hamilton-Jacobi equations based on an entropy penalisation method [40.18480271945435]
We present a framework for efficient extraction of the viscosity solutions of nonlinear Hamilton-Jacobi equations with convex Hamiltonians.<n>These viscosity solutions play a central role in areas such as front propagation, mean-field games, optimal control, machine learning, and a direct application to the forced Burgers' equation.<n>We provide quantum algorithms, both analog and digital, for extracting pointwise values, gradients, minima, and function evaluations at the minimiser of the viscosity solution.
arXiv Detail & Related papers (2025-12-08T14:19:32Z) - A Quantum Linear Systems Pathway for Solving Differential Equations [0.0]
We present a systematic pathway for solving differential equations within the quantum linear systems framework.<n>The approach is demonstrated on a complex tridiagonal linear system and extended to problems in computational fluid dynamics.<n>This pathway lays a foundation for advancing quantum linear system methods toward large-scale applications.
arXiv Detail & Related papers (2025-10-08T10:01:38Z) - Solving wave equation problems on D-Wave quantum annealers [44.99833362998488]
We solve the one-dimensional Helmholtz equation in several scenarios using the quantum annealer provided by the D-Wave systems within a pseudospectral scheme.<n>We assess the performance of different strategies of encoding based on algebraic arguments and the adiabatic condition.
arXiv Detail & Related papers (2025-07-18T08:06:43Z) - i-QLS: Quantum-supported Algorithm for Least Squares Optimization in Non-Linear Regression [4.737806718785056]
We propose an iterative quantum-assisted least squares (i-QLS) optimization method.<n>We overcome the scalability and precision limitations of prior quantum least squares approaches.<n> Experiments confirm that i-QLS enables near-term quantum hardware to perform regression tasks with improved precision and scalability.
arXiv Detail & Related papers (2025-05-05T17:02:35Z) - Practical Application of the Quantum Carleman Lattice Boltzmann Method in Industrial CFD Simulations [44.99833362998488]
This work presents a practical numerical assessment of a hybrid quantum-classical approach to CFD based on the Lattice Boltzmann Method (LBM)<n>We evaluate this method on three benchmark cases featuring different boundary conditions, periodic, bounceback, and moving wall.<n>Our results confirm the validity of the approach, achieving median error fidelities on the order of $10-3$ and success probabilities sufficient for practical quantum state sampling.
arXiv Detail & Related papers (2025-04-17T15:41:48Z) - Quantum algorithms for the variational optimization of correlated electronic states with stochastic reconfiguration and the linear method [0.0]
We present quantum algorithms for the variational optimization of wavefunctions correlated by products of unitary operators.
While an implementation on classical computing hardware would require exponentially growing compute cost, the cost (number of circuits and shots) of our quantum algorithms is in system size.
arXiv Detail & Related papers (2024-08-03T17:53:35Z) - Quantum Algorithms for Nonlinear Dynamics: Revisiting Carleman Linearization with No Dissipative Conditions [0.7373617024876725]
We explore the embedding of nonlinear dynamical systems into linear ordinary differential equations (ODEs) via the Carleman linearization method.<n>Our analysis extends these findings by exploring error bounds beyond the traditional dissipative condition.<n>We prove how this resonance condition leads to a linear convergence with respect to the truncation level $N$ in Carleman linearization.
arXiv Detail & Related papers (2024-05-21T12:09:34Z) - Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary
Optimization [44.96576908957141]
We present a hybrid classical-quantum framework based on the Frank-Wolfe algorithm, Q-FW, for solving quadratic, linear iterations problems on quantum computers.
arXiv Detail & Related papers (2022-03-23T18:00:03Z) - Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves.
We find both methods provide good estimates of the energy and ground state.
gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
arXiv Detail & Related papers (2020-11-02T19:52:04Z)
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.