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Adiabatic training for Variational Quantum Algorithms

Ernesto Acosta, Carlos Cano Gutierrez, Guillermo Botella, Roberto Campos

TL;DR

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.

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.

Adiabatic training for Variational Quantum Algorithms

TL;DR

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.

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.

Paper Structure

This paper contains 28 sections, 7 equations, 6 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Quantum RNN
  3. Quantum Recurrent Block (QRB)
  4. Input data encoding.
  5. Ansatz subcircuit.
  6. Partial Measurement.
  7. Recurrent Blocks Arrangement
  8. Adiabatic Training for QRNNs
  9. Quantum Annealing.
  10. QUBO formulation.
  11. Proposed QRNN optimization model.
  12. Implementation
  13. Problem and data description
  14. Architecture of the proposed solution
  15. Classical Training
  16. ...and 13 more sections

Figures (6)

  • Figure 1: Quantum Recurrent Block (QRB). Source: ref_article_li2023quantum
  • Figure 2: Ansatz subcircuit design. Source: ref_article_li2023quantum
  • Figure 3: pQRNN architecture. Source: ref_article_li2023quantum
  • Figure 4: Execution Flow.
  • Figure 5: Quantum Recurrent Neural Network containing three QRB, with identical ansatz, processing three historical values, each value duplicated in $q_2$ and $q_3$.
  • ...and 1 more figures