Improving variational counterdiabatic driving with weighted actions and computer algebra
- URL: http://arxiv.org/abs/2505.18367v1
- Date: Fri, 23 May 2025 20:51:51 GMT
- Title: Improving variational counterdiabatic driving with weighted actions and computer algebra
- Authors: Naruo Ohga, Takuya Hatomura,
- Abstract summary: Variational counterdiabatic (CD) driving is a disciplined and widely used method to robustly control quantum many-body systems.<n>Here, we reveal that introducing a new degree of freedom into the theory of the AGP can significantly improve variational CD driving.<n>We develop an efficient numerical algorithm to compute the refined driving protocol using computer algebra.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Variational counterdiabatic (CD) driving is a disciplined and widely used method to robustly control quantum many-body systems by mimicking adiabatic processes with high fidelity and reduced duration. Central to this technique is a universal structure of the adiabatic gauge potential (AGP) over a parameterized Hamiltonian. Here, we reveal that introducing a new degree of freedom into the theory of the AGP can significantly improve variational CD driving. Specifically, we find that the algebraic characterization of the AGP is not unique, and we exploit this non-uniqueness to develop the weighted variational method for deriving a refined driving protocol. This approach extends the conventional method in two aspects: it effectively incorporates nonlocal information, and it assigns customized weights to matrix elements relevant to specific problems. We also develop an efficient numerical algorithm to compute the refined driving protocol using computer algebra. Our framework is broadly applicable, as it can replace almost all previous uses of variational CD driving. We demonstrate its practicality by applying it to adiabatic evolution along the ground state of a parameterized Hamiltonian. This proposal outperforms the conventional method in terms of fidelity, as confirmed by extensive numerical simulations on quantum Ising models.
Related papers
- Adiabatic modulation of driving protocols in periodically driven quantum systems [14.37149160708975]
We consider a periodically driven system where the high-frequency driving protocol consists of a sequence of potentials switched on and off.<n>We examine how this influences the long-term dynamics of periodically driven quantum systems.
arXiv Detail & Related papers (2024-04-09T22:59:44Z) - A numerical approach for calculating exact non-adiabatic terms in
quantum dynamics [0.0]
We present a novel approach to computing the Adiabatic Gauge Potential (AGP), which gives information on the non-adiabatic terms that arise from time dependence in the Hamiltonian.
We use this approach to study the AGP obtained for the transverse field Ising model on a variety of graphs, showing how the different underlying graph structures can give rise to very different scaling for the number of terms required in the AGP.
arXiv Detail & Related papers (2024-01-19T19:00:25Z) - Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states.
This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO)
We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
arXiv Detail & Related papers (2023-12-10T15:22:30Z) - Manifold Gaussian Variational Bayes on the Precision Matrix [70.44024861252554]
We propose an optimization algorithm for Variational Inference (VI) in complex models.
We develop an efficient algorithm for Gaussian Variational Inference whose updates satisfy the positive definite constraint on the variational covariance matrix.
Due to its black-box nature, MGVBP stands as a ready-to-use solution for VI in complex models.
arXiv Detail & Related papers (2022-10-26T10:12:31Z) - Variational counterdiabatic driving of the Hubbard model for
ground-state preparation [0.7734726150561086]
Counterdiabatic protocols enable fast driving of quantum states by invoking an auxiliary adiabatic gauge potential (AGP)
We show that the optimal variational parameters in the approximated AGP satisfy a set of linear equations whose coefficients are given by the squared Frobenius norms of these commutators.
We then examine the CD driving of the one-dimensional Hubbard model up to $L = 14 sites with driving order $l leqslant 3$.
arXiv Detail & Related papers (2022-06-15T15:24:40Z) - Variational Adiabatic Gauge Transformation on real quantum hardware for
effective low-energy Hamiltonians and accurate diagonalization [68.8204255655161]
We introduce the Variational Adiabatic Gauge Transformation (VAGT)
VAGT is a non-perturbative hybrid quantum algorithm that can use nowadays quantum computers to learn the variational parameters of the unitary circuit.
The accuracy of VAGT is tested trough numerical simulations, as well as simulations on Rigetti and IonQ quantum computers.
arXiv Detail & Related papers (2021-11-16T20:50:08Z) - Scalable Variational Gaussian Processes via Harmonic Kernel
Decomposition [54.07797071198249]
We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability.
We demonstrate that, on a range of regression and classification problems, our approach can exploit input space symmetries such as translations and reflections.
Notably, our approach achieves state-of-the-art results on CIFAR-10 among pure GP models.
arXiv Detail & Related papers (2021-06-10T18:17:57Z) - A Discrete Variational Derivation of Accelerated Methods in Optimization [68.8204255655161]
We introduce variational which allow us to derive different methods for optimization.
We derive two families of optimization methods in one-to-one correspondence.
The preservation of symplecticity of autonomous systems occurs here solely on the fibers.
arXiv Detail & Related papers (2021-06-04T20:21:53Z) - Fast Gravitational Approach for Rigid Point Set Registration with
Ordinary Differential Equations [79.71184760864507]
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA)
In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field.
We show that the new method class has characteristics not found in previous alignment methods.
arXiv Detail & Related papers (2020-09-28T15:05:39Z) - Lie transformation on shortcut to adiabaticity in parametric driving
quantum system [4.303312411299436]
Shortcut to adiabaticity (STA) is a speed way to produce the same final state that would result in an adiabatic, infinitely slow process.
Two typical techniques to engineer STA are developed by either introducing auxiliary counterdiabatic fields or finding new Hamiltonians that own dynamical invariants to constraint the system into the adiabatic paths.
arXiv Detail & Related papers (2020-09-26T08:34:40Z) - Quantum-optimal-control-inspired ansatz for variational quantum
algorithms [105.54048699217668]
A central component of variational quantum algorithms (VQA) is the state-preparation circuit, also known as ansatz or variational form.
Here, we show that this approach is not always advantageous by introducing ans"atze that incorporate symmetry-breaking unitaries.
This work constitutes a first step towards the development of a more general class of symmetry-breaking ans"atze with applications to physics and chemistry problems.
arXiv Detail & Related papers (2020-08-03T18:00:05Z) - Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized
Gaussian Process Approach [1.6242924916178285]
This study presents a microscopic traffic model that can capture randomness and measure errors in the real world.
Since one unique feature of the proposed framework is the capability of capturing both car-following and lane-changing behaviors with one single model, numerical tests are carried out with two separated datasets.
arXiv Detail & Related papers (2020-07-17T06:03:32Z)
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.