Error Analysis of the Variational Quantum Eigensolver Algorithm
- URL: http://arxiv.org/abs/2301.07263v1
- Date: Wed, 18 Jan 2023 02:02:30 GMT
- Title: Error Analysis of the Variational Quantum Eigensolver Algorithm
- Authors: Sebastian Brandhofer, Simon Devitt, Ilia Polian
- Abstract summary: We study variational quantum eigensolver (VQE) and its individual quantum subroutines.
We show through explicit simulation that the VQE algorithm effectively collapses already when single errors occur during a quantum processing call.
- Score: 0.18188255328029254
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Variational quantum algorithms have been one of the most intensively studied
applications for near-term quantum computing applications. The noisy
intermediate-scale quantum (NISQ) regime, where small enough algorithms can be
run successfully on noisy quantum computers expected during the next 5 years,
is driving both a large amount of research work and a significant amount of
private sector funding. Therefore, it is important to understand whether
variational algorithms are effective at successfully converging to the correct
answer in presence of noise. We perform a comprehensive study of the
variational quantum eigensolver (VQE) and its individual quantum subroutines.
Building on asymptotic bounds, we show through explicit simulation that the VQE
algorithm effectively collapses already when single errors occur during a
quantum processing call. We discuss the significant implications of this result
in the context of being able to run any variational type algorithm without
resource expensive error correction protocols.
Related papers
- Dissipative variational quantum algorithms for Gibbs state preparation [0.0]
We introduce dissipative variational quantum algorithms (D-VQAs) by incorporating dissipative operations, such as qubit RESET and gates, as an intrinsic part of a variational quantum circuit.
We demonstrate how such algorithms can prepare Gibbs states over a wide range of quantum many-body Hamiltonians and temperatures, while significantly reducing errors due to both coherent and non-coherent noise.
arXiv Detail & Related papers (2024-07-12T18:48:46Z) - Noise-induced transition in optimal solutions of variational quantum
algorithms [0.0]
Variational quantum algorithms are promising candidates for delivering practical quantum advantage on noisy quantum hardware.
We study the effect of noise on optimization by studying a variational quantum eigensolver (VQE) algorithm calculating the ground state of a spin chain model.
arXiv Detail & Related papers (2024-03-05T08:31:49Z) - 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) - 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) - Reducing the cost of energy estimation in the variational quantum
eigensolver algorithm with robust amplitude estimation [50.591267188664666]
Quantum chemistry and materials is one of the most promising applications of quantum computing.
Much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them.
arXiv Detail & Related papers (2022-03-14T16:51:36Z) - The Variational Quantum Eigensolver: a review of methods and best
practices [3.628860803653535]
The variational quantum eigensolver (or VQE) uses the variational principle to compute the ground state energy of a Hamiltonian.
This review aims to provide an overview of the progress that has been made on the different parts of the algorithm.
arXiv Detail & Related papers (2021-11-09T14:40:18Z) - 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) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - 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) - Policy Gradient based Quantum Approximate Optimization Algorithm [2.5614220901453333]
We show that policy-gradient-based reinforcement learning algorithms are well suited for optimizing the variational parameters of QAOA in a noise-robust fashion.
We analyze the performance of the algorithm for quantum state transfer problems in single- and multi-qubit systems.
arXiv Detail & Related papers (2020-02-04T00:46:51Z)
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.