Feedback-based quantum algorithms for ground state preparation
- URL: http://arxiv.org/abs/2303.02917v2
- Date: Tue, 26 Sep 2023 20:16:30 GMT
- Title: Feedback-based quantum algorithms for ground state preparation
- Authors: James B. Larsen, Matthew D. Grace, Andrew D. Baczewski, Alicia B.
Magann
- Abstract summary: Ground state properties of quantum many-body systems are a subject of interest across chemistry, materials science, and physics.
Variational quantum algorithms are one class of ground state algorithms that has received significant attention in recent years.
We develop formulations of feedback-based quantum algorithms for ground state preparation.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The ground state properties of quantum many-body systems are a subject of
interest across chemistry, materials science, and physics. Thus, algorithms for
finding ground states can have broad impacts. Variational quantum algorithms
are one class of ground state algorithms that has received significant
attention in recent years. These algorithms utilize a hybrid quantum-classical
computing framework to prepare ground states on quantum computers. However,
this requires solving a classical optimization problem that can become
prohibitively expensive in high dimensions. Here, we develop formulations of
feedback-based quantum algorithms for ground state preparation that can be used
to address this challenge for two broad classes of Hamiltonians: Fermi-Hubbard
Hamiltonians, and molecular Hamiltonians represented in second quantization.
Feedback-based quantum algorithms are optimization-free; in place of classical
optimization, quantum circuit parameters are set according to a deterministic
feedback law derived from quantum Lyapunov control principles. This feedback
law guarantees a monotonic improvement in solution quality with respect to the
depth of the quantum circuit. A variety of numerical illustrations are provided
that analyze the convergence and robustness of feedback-based quantum
algorithms for these problem classes.
Related papers
- Scalable Quantum Algorithms for Noisy Quantum Computers [0.0]
This thesis develops two main techniques to reduce the quantum computational resource requirements.
The aim is to scale up application sizes on current quantum processors.
While the main focus of application for our algorithms is the simulation of quantum systems, the developed subroutines can further be utilized in the fields of optimization or machine learning.
arXiv Detail & Related papers (2024-03-01T19:36:35Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - State-Averaged Orbital-Optimized VQE: A quantum algorithm for the
democratic description of ground and excited electronic states [0.0]
The SA-OO-VQE package aims to answer both problems with its hybrid quantum-classical conception based on a typical Variational Quantum Eigensolver approach.
The SA-OO-VQE has the ability to treat degenerate (or quasi-degenerate) states on the same footing, thus avoiding known numerical optimization problems around avoided crossings or conical intersections.
arXiv Detail & Related papers (2024-01-22T12:16:37Z) - Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
The anticipated applications of quantum computers span across science and industry.
We present a survey of several potential application areas of quantum algorithms.
We outline the challenges and opportunities in each area in an "end-to-end" fashion.
arXiv Detail & Related papers (2023-10-04T17:53:55Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Fighting noise with noise: a stochastic projective quantum eigensolver [0.0]
We present a novel approach to estimating physical observables which leads to a two order of magnitude reduction in the required sampling of the quantum state.
The method can be applied to excited-state calculations and simulation for general chemistry on quantum devices.
arXiv Detail & Related papers (2023-06-26T09:22:06Z) - Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem.
The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
arXiv Detail & Related papers (2023-04-18T11:57:15Z) - A full circuit-based quantum algorithm for excited-states in quantum
chemistry [6.973166066636441]
We propose a non-variational full circuit-based quantum algorithm for obtaining the excited-state spectrum of a quantum chemistry Hamiltonian.
Compared with previous classical-quantum hybrid variational algorithms, our method eliminates the classical optimization process.
The algorithm can be widely applied to various Hamiltonian spectrum determination problems on the fault-tolerant quantum computers.
arXiv Detail & Related papers (2021-12-28T15:48:09Z) - Parametrized Complexity of Quantum Inspired Algorithms [0.0]
Two promising areas of quantum algorithms are quantum machine learning and quantum optimization.
Motivated by recent progress in quantum technologies and in particular quantum software, research and industrial communities have been trying to discover new applications of quantum algorithms.
arXiv Detail & Related papers (2021-12-22T06:19:36Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z)
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.