Hot-Start Optimization for Variational Quantum Eigensolver

• URL: http://arxiv.org/abs/2104.15001v1
• Date: Fri, 30 Apr 2021 13:47:11 GMT
• Title: Hot-Start Optimization for Variational Quantum Eigensolver
• Authors: Belozerova Polina, Shangareev Arthur, Zotov Yuriy, Yung Manhong, lv Dingshun
• Abstract summary: Variational Quantum Eigensolver (VQE) is one the most perspective algorithms for simulation of quantum many body physics. In our work we present the optimization approach allowing shallow circuit solution.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Variational Quantum Eigensolver (VQE) is one the most perspective algorithms for simulation of quantum many body physics that have recently attached a lot of attention and believed would be practical for implementation on the near term quantum devices. However, its feasibility and accuracy critically depend on the ansatz structure, which can be defined in different ways and appropriate choosing the structure presents a bottleneck of the protocol. In our work we present the optimization approach allowing shallow circuit solution. The major achievement of our Hot-Start method is the requiring gates number restriction by several times. In result it increases the fidelity of the final results what is important for the current noise quantum devices implementation. We suggest Hot-Start optimization to be a good new methods beginning for the modern quantum computational devices functionality development.

Related papers

• Optimization via Quantum Preconditioning [0.0]
  We introduce a quantum preconditioning approach based on the quantum approximate optimization. It transforms the input problem into a more suitable form for a solver with the level of preconditioning determined by the depth of the quantum circuit. We demonstrate that best-in-class classical algorithms can converge more rapidly when given quantum preconditioned input for various problems.
  arXiv  Detail & Related papers  (2025-02-25T19:00:41Z)
• Eigenstate Preparation on Quantum Computers [0.0]
  This thesis investigates algorithms for eigenstate preparation using near-term quantum computing devices. We establish three methods in detail: quantum adiabatic evolution with optimal control, the Rodeo Algorithm, and the Variational Rodeo Algorithm. We show results suggesting that this method can be effective in preparing eigenstates, but its practicality is predicated on the preparation of an initial state that has significant overlap with the desired eigenstate.
  arXiv  Detail & Related papers  (2024-12-19T17:28:21Z)
• Bayesian Optimization Priors for Efficient Variational Quantum Algorithms [0.0]
  Quantum computers currently rely on a quantum-classical approach known as Variational Quantum Algorithms (VQAs) to solve problems. We propose a hybrid framework for basic computational optimization that reduces the number of shots per time is charged. Using both proposed features, we show that using both proposed features statistically outperforms an implementation within VQAs for simulations.
  arXiv  Detail & Related papers  (2024-06-20T18:00:12Z)
• Performant near-term quantum combinatorial optimization [1.1999555634662633]
  We present a variational quantum algorithm for solving optimization problems with linear-depth circuits. Our algorithm uses an ansatz composed of Hamiltonian generators designed to control each term in the target quantum function. We conclude our performant and resource-minimal approach is a promising candidate for potential quantum computational advantages.
  arXiv  Detail & Related papers  (2024-04-24T18:49:07Z)
• Surrogate optimization of variational quantum circuits [1.0546736060336612]
  Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE.
  arXiv  Detail & Related papers  (2024-04-03T18:00:00Z)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
  Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2022-02-17T18:59:20Z)
• 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)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• VQE Method: A Short Survey and Recent Developments [5.9640499950316945]
  The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues and eigenvalues of a Hamiltonian. VQE has been successfully applied to solve the electronic Schr"odinger equation for a variety of small molecules. Modern quantum computers are not capable of executing deep quantum circuits produced by using currently available ansatze.
  arXiv  Detail & Related papers  (2021-03-15T16:25:36Z)
• 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)

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.