KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search

• URL: http://arxiv.org/abs/2406.17630v3
• Date: Wed, 11 Dec 2024 22:52:39 GMT
• Title: KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search
• Authors: Akash Kundu, Aritra Sarkar, Abhishek Sadhu,
• Abstract summary: We use the Kolmogorov-Arnold Network (KAN) in the Quantum Search (QAS) algorithm, analyzing their efficiency in the task of quantum state preparation and quantum chemistry.<n>In quantum state preparation, our results show that in a noiseless scenario, the probability of success is 2 to 5 times higher than robustnesss.<n>In tackling quantum chemistry problems, we enhance the recently proposed QAS algorithm by integrating curriculum reinforcement learning with a KAN structure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum architecture Search (QAS) is a promising direction for optimization and automated design of quantum circuits towards quantum advantage. Recent techniques in QAS emphasize Multi-Layer Perceptron (MLP)-based deep Q-networks. However, their interpretability remains challenging due to the large number of learnable parameters and the complexities involved in selecting appropriate activation functions. In this work, to overcome these challenges, we utilize the Kolmogorov-Arnold Network (KAN) in the QAS algorithm, analyzing their efficiency in the task of quantum state preparation and quantum chemistry. In quantum state preparation, our results show that in a noiseless scenario, the probability of success is 2 to 5 times higher than MLPs. In noisy environments, KAN outperforms MLPs in fidelity when approximating these states, showcasing its robustness against noise. In tackling quantum chemistry problems, we enhance the recently proposed QAS algorithm by integrating curriculum reinforcement learning with a KAN structure. This facilitates a more efficient design of parameterized quantum circuits by reducing the number of required 2-qubit gates and circuit depth. Further investigation reveals that KAN requires a significantly smaller number of learnable parameters compared to MLPs; however, the average time of executing each episode for KAN is higher.

Related papers

• Q-Fusion: Diffusing Quantum Circuits [2.348041867134616]
  We propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits. Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.
  arXiv  Detail & Related papers  (2025-04-29T14:10:10Z)
• Topology-Driven Quantum Architecture Search Framework [2.9862856321580895]
  We propose a Topology-Driven Quantum Architecture Search (TD-QAS) framework to identify high-performance quantum circuits. By decoupling the extensive search space into topology and gate-type components, TD-QAS avoids exploring gate configurations within low-performance topologies.
  arXiv  Detail & Related papers  (2025-02-20T05:05:53Z)
• Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits [10.073911279652918]
  We study the relationship between the quantum noise and the diffusion model. We propose a novel diffusion-inspired learning approach to mitigate the quantum noise in the PQCs.
  arXiv  Detail & Related papers  (2024-06-02T19:35:38Z)
• 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)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• 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)
• Quantum Architecture Search with Unsupervised Representation Learning [24.698519892763283]
  Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS) QAS is designed to optimize quantum circuits for Variational Quantum Algorithms (VQAs)
  arXiv  Detail & Related papers  (2024-01-21T19:53:17Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Scaling Limits of Quantum Repeater Networks [62.75241407271626]
  Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing. Due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability. In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed.
  arXiv  Detail & Related papers  (2023-05-15T14:57:01Z)
• 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)
• Iterative Qubits Management for Quantum Index Searching in a Hybrid System [56.39703478198019]
  IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
  arXiv  Detail & Related papers  (2022-09-22T21:54:28Z)
• DeepQMLP: A Scalable Quantum-Classical Hybrid DeepNeural Network Architecture for Classification [6.891238879512672]
  Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks. We present a scalable quantum-classical hybrid deep neural network (DeepQMLP) architecture inspired by classical deep neural network architectures. DeepQMLP provides up to 25.3% lower loss and 7.92% higher accuracy during inference under noise than QMLP.
  arXiv  Detail & Related papers  (2022-02-02T15:29:46Z)
• Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning [0.0]
  We introduce a training method for parametrized quantum circuits (PQCs) that can be used to solve RL tasks for discrete and continuous state spaces. We investigate which architectural choices for quantum Q-learning agents are most important for successfully solving certain types of environments.
  arXiv  Detail & Related papers  (2021-03-28T08:57:22Z)
• 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)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

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.