Hybrid-Quantum Neural Architecture Search for The Proximal Policy Optimization Algorithm

• URL: http://arxiv.org/abs/2501.10673v1
• Date: Sat, 18 Jan 2025 06:39:05 GMT
• Title: Hybrid-Quantum Neural Architecture Search for The Proximal Policy Optimization Algorithm
• Authors: Moustafa Zada,
• Abstract summary: This research attempts to tackle that gap in the literature by using the Regularized Evolution algorithm to search for the optimal hybrid classical-quantum architecture.<n>We also try to explain the factors that contributed to such results in hope to grasp a better intuition about what we should consider good practices for designing an efficient hybrid architecture.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies in quantum machine learning advocated the use of hybrid models to assist with the limitations of the currently existing Noisy Intermediate Scale Quantum (NISQ) devices, but what was missing from most of them was the explanations and interpretations of the choices that were made to pick those exact architectures and the differentiation between good and bad hybrid architectures, this research attempts to tackle that gap in the literature by using the Regularized Evolution algorithm to search for the optimal hybrid classical-quantum architecture for the Proximal Policy Optimization (PPO) algorithm, a well-known reinforcement learning algorithm, ultimately the classical models dominated the leaderboard with the best hybrid model coming in eleventh place among all unique models, while we also try to explain the factors that contributed to such results,and for some models to behave better than others in hope to grasp a better intuition about what we should consider good practices for designing an efficient hybrid architecture.

Related papers

• Inference of maximum parsimony phylogenetic trees with model-based classical and quantum methods [10.13082983732946]
  We design three optimization models compatible with both classical and quantum solvers.<n>Our method directly searches the complete solution space of all possible tree topologies and ancestral states.<n>Our quantum simulations successfully find the exact optimal solutions for small-scale instances.
  arXiv  Detail & Related papers  (2025-08-01T09:43:12Z)
• TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learning [0.0]
  We develop TunnElQNN, a non-sequential architecture composed of alternating classical and quantum layers.<n>We evaluate the performance of this hybrid model on a synthetic dataset of interleaving half-circle for multi-class classification tasks.<n>Our results show that the TunnElQNN model consistently outperforms the ReLUQNN counterpart.
  arXiv  Detail & Related papers  (2025-05-02T00:30:50Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  MoE models offer enhanced model capacity and computational efficiency through conditional computation.<n>Deployment and inference of MoE models present substantial challenges in terms of computational resources, latency, and energy efficiency.<n>This survey systematically analyzes the current landscape of inference optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Integrating Optimization Theory with Deep Learning for Wireless Network Design [38.257335693563554]
  Traditional wireless network design relies on optimization algorithms derived from domain-specific mathematical models.<n>Deep learning has emerged as a promising alternative to overcome complexity and adaptability concerns.<n>This paper introduces a novel approach that integrates optimization theory with deep learning methodologies to address these issues.
  arXiv  Detail & Related papers  (2024-12-11T20:27:48Z)
• STAR: Synthesis of Tailored Architectures [61.080157488857516]
  We propose a new approach for the synthesis of tailored architectures (STAR)<n>Our approach combines a novel search space based on the theory of linear input-varying systems, supporting a hierarchical numerical encoding into architecture genomes. STAR genomes are automatically refined and recombined with gradient-free, evolutionary algorithms to optimize for multiple model quality and efficiency metrics.<n>Using STAR, we optimize large populations of new architectures, leveraging diverse computational units and interconnection patterns, improving over highly-optimized Transformers and striped hybrid models on the frontier of quality, parameter size, and inference cache for autoregressive language modeling.
  arXiv  Detail & Related papers  (2024-11-26T18:42:42Z)
• Hybrid Tree Tensor Networks for quantum simulation [0.0]
  We introduce a novel algorithm to perform ground state optimizations with hybrid Tree Networks (hTTNs) We successfully demonstrate that hTTNs can improve upon classical equivalents with equal bond dimension in the classical part.
  arXiv  Detail & Related papers  (2024-04-08T18:00:00Z)
• Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
  We identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis. We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
  arXiv  Detail & Related papers  (2024-03-26T16:33:12Z)
• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)
• Quality Diversity Evolutionary Learning of Decision Trees [4.447467536572625]
  We show that MAP-Elites can diversify hybrid models over a feature space that captures both the model complexity and its behavioral variability. We apply our method on two well-known control problems from the OpenAI Gym library, on which we discuss the "illumination" patterns projected by MAP-Elites.
  arXiv  Detail & Related papers  (2022-08-17T13:57:32Z)
• A Copositive Framework for Analysis of Hybrid Ising-Classical Algorithms [18.075115172621096]
  We present a formal analysis of hybrid algorithms in the context of solving mixed-binary quadratic programs via Ising solvers. We propose to solve this reformulation with a hybrid quantum-classical cutting-plane algorithm.
  arXiv  Detail & Related papers  (2022-07-27T16:47:32Z)
• Neural Improvement Heuristics for Graph Combinatorial Optimization Problems [49.85111302670361]
  We introduce a novel Neural Improvement (NI) model capable of handling graph-based problems where information is encoded in the nodes, edges, or both. The presented model serves as a fundamental component for hill-climbing-based algorithms that guide the selection of neighborhood operations for each.
  arXiv  Detail & Related papers  (2022-06-01T10:35:29Z)
• Neural Combinatorial Optimization: a New Player in the Field [69.23334811890919]
  This paper presents a critical analysis on the incorporation of algorithms based on neural networks into the classical optimization framework. A comprehensive study is carried out to analyse the fundamental aspects of such algorithms, including performance, transferability, computational cost and to larger-sized instances.
  arXiv  Detail & Related papers  (2022-05-03T07:54:56Z)
• Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components [71.03032589756434]
  We investigate the effect of different variation operators in a complex domain, that of multi-network heterogeneous neural models. We characterize both the variation operators, according to their effect on the complexity and performance of the model; and the models, relying on diverse metrics which estimate the quality of the different parts composing it.
  arXiv  Detail & Related papers  (2021-06-16T17:12:26Z)

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.