Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Quantum Architecture Search: A Survey

Darya Martyniuk, Johannes Jung, Adrian Paschke

TL;DR

QAS seeks to automatically design a parametrized quantum circuit (PQC) with learnable parameters $\vec{\theta}$ that optimizes a cost function $L$ under hardware constraints. Using a taxonomy inspired by neural architecture search, the paper surveys reinforcement learning, evolutionary algorithms, differentiable QAS, adaptive methods, Monte Carlo tree search, Bayesian optimization, and generative models for PQC design. It highlights efficiency boosters such as one-shot weight sharing, performance predictors, meta-learning, and search-space reduction, while emphasizing hardware-awareness and noise resilience in NISQ devices. It identifies key gaps, including benchmarks and end-to-end tooling, and outlines future directions for standardized evaluation, encoding schemes, and scalable, hardware-aware QAS methods.

Abstract

Quantum computing has made significant progress in recent years, attracting immense interest not only in research laboratories but also in various industries. However, the application of quantum computing to solve real-world problems is still hampered by a number of challenges, including hardware limitations and a relatively under-explored landscape of quantum algorithms, especially when compared to the extensive development of classical computing. The design of quantum circuits, in particular parameterized quantum circuits (PQCs), which contain learnable parameters optimized by classical methods, is a non-trivial and time-consuming task requiring expert knowledge. As a result, research on the automated generation of PQCs, known as quantum architecture search (QAS), has gained considerable interest. QAS focuses on the use of machine learning and optimization-driven techniques to generate PQCs tailored to specific problems and characteristics of quantum hardware. In this paper, we provide an overview of QAS methods by examining relevant research studies in the field. We discuss main challenges in designing and performing an automated search for an optimal PQC, and survey ways to address them to ease future research.

Quantum Architecture Search: A Survey

TL;DR

QAS seeks to automatically design a parametrized quantum circuit (PQC) with learnable parameters that optimizes a cost function under hardware constraints. Using a taxonomy inspired by neural architecture search, the paper surveys reinforcement learning, evolutionary algorithms, differentiable QAS, adaptive methods, Monte Carlo tree search, Bayesian optimization, and generative models for PQC design. It highlights efficiency boosters such as one-shot weight sharing, performance predictors, meta-learning, and search-space reduction, while emphasizing hardware-awareness and noise resilience in NISQ devices. It identifies key gaps, including benchmarks and end-to-end tooling, and outlines future directions for standardized evaluation, encoding schemes, and scalable, hardware-aware QAS methods.

Abstract

Quantum computing has made significant progress in recent years, attracting immense interest not only in research laboratories but also in various industries. However, the application of quantum computing to solve real-world problems is still hampered by a number of challenges, including hardware limitations and a relatively under-explored landscape of quantum algorithms, especially when compared to the extensive development of classical computing. The design of quantum circuits, in particular parameterized quantum circuits (PQCs), which contain learnable parameters optimized by classical methods, is a non-trivial and time-consuming task requiring expert knowledge. As a result, research on the automated generation of PQCs, known as quantum architecture search (QAS), has gained considerable interest. QAS focuses on the use of machine learning and optimization-driven techniques to generate PQCs tailored to specific problems and characteristics of quantum hardware. In this paper, we provide an overview of QAS methods by examining relevant research studies in the field. We discuss main challenges in designing and performing an automated search for an optimal PQC, and survey ways to address them to ease future research.
Paper Structure (23 sections, 3 figures, 1 table)

This paper contains 23 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. QAS Research and Relations to Other Fields
  3. Background
  4. Search Strategies
  5. Reinforcement Learning
  6. Evolutionary Algorithms
  7. Differentiable QAS
  8. Adaptive Algorithms
  9. Monte Carlo Tree Search
  10. Bayesian Optimization
  11. Generative Models
  12. Efficiency Improvement
  13. One-shot Techniques
  14. Performance predictors
  15. Meta-Learning
  16. ...and 8 more sections

Figures (3)

  • Figure 1: General overview of QAS: Given a search space (which can be constrained by hardware characteristics), performance criteria, and, in some applications, a dataset, QAS aims to automatically find an optimal PQC with parameters $\vec{\theta^*}$ that maximizes the performance. Quantum and classical systems can be accessed to perform the evaluation of candidate circuits.
  • Figure 2: Dimensions of QAS methods. Inspired by elsken2019neural.
  • Figure 3: Examples of quantum circuit representations.