Adiabatic training for Variational Quantum Algorithms

• URL: http://arxiv.org/abs/2410.18618v1
• Date: Thu, 24 Oct 2024 10:17:48 GMT
• Title: Adiabatic training for Variational Quantum Algorithms
• Authors: Ernesto Acosta, Carlos Cano Gutierrez, Guillermo Botella, Roberto Campos,
• Abstract summary: This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements. Gate-based Quantum Computer running the Variational Quantum Algorithm (VQA) representing the Quantum Neural Network (QNN) An adiabatic Quantum Computer where the optimization function is executed to find the best parameters for the VQA.
• Score: 0.4374837991804085
• License:
• Abstract: This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements: a classical computer in charge of the data preparation and interpretation; a Gate-based Quantum Computer running the Variational Quantum Algorithm (VQA) representing the Quantum Neural Network (QNN); and an adiabatic Quantum Computer where the optimization function is executed to find the best parameters for the VQA. As of the moment of this writing, the majority of QNNs are being trained using gradient-based classical optimizers having to deal with the barren-plateau effect. Some gradient-free classical approaches such as Evolutionary Algorithms have also been proposed to overcome this effect. To the knowledge of the authors, adiabatic quantum models have not been used to train VQAs. The paper compares the results of gradient-based classical algorithms against adiabatic optimizers showing the feasibility of integration for gate-based and adiabatic quantum computing models, opening the door to modern hybrid QML approaches for High Performance Computing.

Related papers

• Tensor-Based Binary Graph Encoding for Variational Quantum Classifiers [3.5051814539447474]
  We propose a novel quantum encoding framework for graph classification using Variational Quantums (VQCs) By constructing slightly more complex circuits tailored for graph encoding, we demonstrate that VQCs can effectively classify graphs within the constraints of current quantum hardware.
  arXiv  Detail & Related papers  (2025-01-24T02:26:21Z)
• 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 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)
• 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)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• Hybrid Quantum Classical Simulations [0.0]
  We report on two major hybrid applications of quantum computing, namely, the quantum approximate optimisation algorithm (QAOA) and the variational quantum eigensolver (VQE) Both are hybrid quantum classical algorithms as they require incremental communication between a classical central processing unit and a quantum processing unit to solve a problem.
  arXiv  Detail & Related papers  (2022-10-06T10:49:15Z)
• Alternating Layered Variational Quantum Circuits Can Be Classically Optimized Efficiently Using Classical Shadows [4.680722019621822]
  Variational quantum algorithms (VQAs) are the quantum analog of classical neural networks (NNs) We introduce a training algorithm with an exponential reduction in training cost of such VQAs.
  arXiv  Detail & Related papers  (2022-08-24T15:47:44Z)
• Identification of topological phases using classically-optimized variational quantum eigensolver [0.6181093777643575]
  Variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid quantum-classical algorithm for quantum computers. We propose classically-optimized VQE (co-VQE), where the whole process of the optimization is efficiently conducted on a classical computer. In co-VQE, we only use quantum computers to measure nonlocal quantities after the parameters are optimized.
  arXiv  Detail & Related papers  (2022-02-07T02:26:58Z)
• 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)
• Improved accuracy on noisy devices by non-unitary Variational Quantum Eigensolver for chemistry applications [0.0]
  We propose a modification of the Variational Quantum Eigensolver algorithm for electronic structure optimization using quantum computers. A non-unitary operator is combined with the original system Hamiltonian leading to a new variational problem with a simplified wavefunction Ansatz.
  arXiv  Detail & Related papers  (2021-01-22T20:17:37Z)
• 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)

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.