Variational optimization of the quantum annealing schedule for the Lechner-Hauke-Zoller scheme

• URL: http://arxiv.org/abs/2012.01694v2
• Date: Mon, 1 Mar 2021 01:41:01 GMT
• Title: Variational optimization of the quantum annealing schedule for the Lechner-Hauke-Zoller scheme
• Authors: Yuki Susa, Hidetoshi Nishimori
• Abstract summary: We show that nonmonotonic annealing schedules optimize the performance measured by the residual energy and the final ground-state fidelity. This improvement does not accompany a notable increase in the energy gap.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The annealing schedule is optimized for a parameter in the Lechner-Hauke-Zoller (LHZ) scheme for quantum annealing designed for the all-to-all-interacting Ising model representing generic combinatorial optimization problems. We adapt the variational approach proposed by Matsuura et al. (arXiv:2003.09913) to the annealing schedule of a term representing a constraint for variables intrinsic to the LHZ scheme with the annealing schedule of other terms kept intact. Numerical results for a simple ferromagnetic model and the spin-glass problem show that nonmonotonic annealing schedules optimize the performance measured by the residual energy and the final ground-state fidelity. This improvement does not accompany a notable increase in the instantaneous energy gap, which suggests the importance of a dynamical viewpoint in addition to static analyses in the study of practically relevant diabatic processes in quantum annealing.

Related papers

• Asymptotically Optimal Change Detection for Unnormalized Pre- and Post-Change Distributions [65.38208224389027]
  This paper addresses the problem of detecting changes when only unnormalized pre- and post-change distributions are accessible. Our approach is based on the estimation of the Cumulative Sum statistics, which is known to produce optimal performance.
  arXiv  Detail & Related papers  (2024-10-18T17:13:29Z)
• Quantum Annealing with chaotic driver Hamiltonians [0.0]
  We investigate driver Hamiltonians based on a bosonic spin version of the Sachdev-Ye-Kitaev (SYK) model. We find that a considerable proportion of SYK model instances demonstrate significant speedups.
  arXiv  Detail & Related papers  (2024-09-30T17:39:54Z)
• Non-Adiabatic Quantum Optimization for Crossing Quantum Phase Transitions [0.0]
  We consider the optimal driving of the ground state of a quantum system across a quantum phase transition in finite time. We introduce a novel framework, Non-Adiabatic Quantum Optimization (NAQO), that outperforms schedules obtained via both local adiabaticity and state-of-the-art numerical optimization.
  arXiv  Detail & Related papers  (2024-07-12T18:00:01Z)
• An Optimization-based Deep Equilibrium Model for Hyperspectral Image Deconvolution with Convergence Guarantees [71.57324258813675]
  We propose a novel methodology for addressing the hyperspectral image deconvolution problem. A new optimization problem is formulated, leveraging a learnable regularizer in the form of a neural network. The derived iterative solver is then expressed as a fixed-point calculation problem within the Deep Equilibrium framework.
  arXiv  Detail & Related papers  (2023-06-10T08:25:16Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Evaluating the Convergence of Tabu Enhanced Hybrid Quantum Optimization [58.720142291102135]
  We introduce the Tabu Enhanced Hybrid Quantum Optimization metaheuristic approach useful for optimization problem solving on a quantum hardware. We address the theoretical convergence of the proposed scheme from the viewpoint of the collisions in the object which stores the tabu states, based on the Ising model.
  arXiv  Detail & Related papers  (2022-09-05T07:23:03Z)
• Quantum and classical annealing in a continuous space with multiple local minima [0.0]
  We show that quantum annealing yields a power law convergence, thus an exponential improvement over simulated annealing. We also reveal how diabatic quantum dynamics, quantum tunneling in particular, steers the systems toward the global minimum.
  arXiv  Detail & Related papers  (2022-03-22T02:02:23Z)
• Greedy parameter optimization for diabatic quantum annealing [0.0]
  A shorter processing time is desirable for quantum computation to minimize the effects of noise. We propose a simple procedure to variationally determine a set of parameters in the transverse-field Ising model. We test the idea in the ferromagnetic system with all-to-all couplings and spin-glass problems.
  arXiv  Detail & Related papers  (2021-11-26T01:31:01Z)
• Genetic optimization of quantum annealing [0.0]
  We present a numerical approach based on genetic algorithms to improve the performance of quantum annealing. We also explore shortcuts to abaticity by computing a practically feasible $k$-local optimal driving operator.
  arXiv  Detail & Related papers  (2021-08-06T15:59:23Z)
• 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)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)

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.