Gradient-free quantum optimization on NISQ devices
- URL: http://arxiv.org/abs/2012.13453v2
- Date: Wed, 3 Mar 2021 13:58:13 GMT
- Title: Gradient-free quantum optimization on NISQ devices
- Authors: L. Franken, B. Georgiev, S. Muecke, M. Wolter, N. Piatkowski and C.
Bauckhage
- Abstract summary: We consider recent advances in weight-agnostic learning and propose a strategy that addresses the trade-off between finding appropriate circuit architectures and parameter tuning.
We investigate the use of NEAT-inspired algorithms which evaluate circuits via genetic competition and thus circumvent issues due to exceeding numbers of parameters.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Variational Quantum Eigensolvers (VQEs) have recently attracted considerable
attention. Yet, in practice, they still suffer from the efforts for estimating
cost function gradients for large parameter sets or resource-demanding
reinforcement strategies. Here, we therefore consider recent advances in
weight-agnostic learning and propose a strategy that addresses the trade-off
between finding appropriate circuit architectures and parameter tuning. We
investigate the use of NEAT-inspired algorithms which evaluate circuits via
genetic competition and thus circumvent issues due to exceeding numbers of
parameters. Our methods are tested both via simulation and on real quantum
hardware and are used to solve the transverse Ising Hamiltonian and the
Sherrington-Kirkpatrick spin model.
Related papers
- Adaptive variational quantum dynamics simulations with compressed circuits and fewer measurements [4.2643127089535104]
We show an improved version of the adaptive variational quantum dynamics simulation (AVQDS) method, which we call AVQDS(T)
The algorithm adaptively adds layers of disjoint unitary gates to the ansatz circuit so as to keep the McLachlan distance, a measure of the accuracy of the variational dynamics, below a fixed threshold.
We also show a method based on eigenvalue truncation to solve the linear equations of motion for the variational parameters with enhanced noise resilience.
arXiv Detail & Related papers (2024-08-13T02:56:43Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Reinforcement learning-assisted quantum architecture search for variational quantum algorithms [0.0]
This thesis focuses on identifying functional quantum circuits in noisy quantum hardware.
We introduce a tensor-based quantum circuit encoding, restrictions on environment dynamics to explore the search space of possible circuits efficiently.
In dealing with various VQAs, our RL-based QAS outperforms existing QAS.
arXiv Detail & Related papers (2024-02-21T12:30:39Z) - 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) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Gradient Ascent Pulse Engineering with Feedback [0.0]
We introduce feedback-GRAPE, which borrows some concepts from model-free reinforcement learning to incorporate the response to strong measurements.
Our method yields interpretable feedback strategies for state preparation and stabilization in the presence of noise.
arXiv Detail & Related papers (2022-03-08T18:46:09Z) - 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) - FLIP: A flexible initializer for arbitrarily-sized parametrized quantum
circuits [105.54048699217668]
We propose a FLexible Initializer for arbitrarily-sized Parametrized quantum circuits.
FLIP can be applied to any family of PQCs, and instead of relying on a generic set of initial parameters, it is tailored to learn the structure of successful parameters.
We illustrate the advantage of using FLIP in three scenarios: a family of problems with proven barren plateaus, PQC training to solve max-cut problem instances, and PQC training for finding the ground state energies of 1D Fermi-Hubbard models.
arXiv Detail & Related papers (2021-03-15T17:38:33Z) - 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) - Classical Optimizers for Noisy Intermediate-Scale Quantum Devices [1.43494686131174]
We present a collection of tunings tuned for usage on Noisy Intermediate-Scale Quantum (NISQ) devices.
We analyze the efficiency and effectiveness of different minimizes in a VQE case study.
While most results to date concentrated on tuning the quantum VQE circuit, we show that, in the presence of quantum noise, the classical minimizer step needs to be carefully chosen to obtain correct results.
arXiv Detail & Related papers (2020-04-06T21:31:22Z)
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.