Fault-tolerant logical state construction based on cavity-QED network

• URL: http://arxiv.org/abs/2503.11500v2
• Date: Mon, 28 Apr 2025 10:12:41 GMT
• Title: Fault-tolerant logical state construction based on cavity-QED network
• Authors: Rui Asaoka, Yasunari Suzuki, Yuuki Tokunaga,
• Abstract summary: We propose and evaluate a scalable and practical architecture with a cavity-quantum-electrodynamics (CQED) network.<n>Our architecture takes advantage of the stability of neutral atoms and the flexibility of a CQED network.
• Score: 0.196629787330046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exploring an efficient and scalable architecture of fault-tolerant quantum computing (FTQC) is vital for demonstrating useful quantum computing. Here, we propose and evaluate a scalable and practical architecture with a cavity-quantum-electrodynamics (CQED) network. Our architecture takes advantage of the stability of neutral atoms and the flexibility of a CQED network. We show a concrete framework for implementing surface codes and numerically analyze the logical error rate and threshold values beyond the simplified circuit-level noise model on several network structures. Although the requirement of CQED parameters is demanding given the current performance of experimental systems, we show that an error-decoding algorithm tailored to our proposed architecture, where the loss information of ancillary photons is utilized, greatly improves the error threshold. For example, the internal cooperativity, a good figure of merit of the cavity performance for quantum computing, required for FTQC is relaxed to 1/5 compared to the normal error-decoding for the surface code. Since our proposal and results can be extended to other LDPC codes straightforwardly, our approach will lead to achieve more reliable FTQC using CQED.

Related papers

• Q3DE: A fault-tolerant quantum computer architecture for multi-bit burst errors by cosmic rays [2.5387423427791047]
  We propose an FTQC architecture that enhances the tolerance to multi-bit burst errors (MBBEs) by cosmic rays with moderate changes and overhead.<n>We show that Q3DE effectively reduces the period of MBBEs by 1000 times and halves the size of their region.
  arXiv  Detail & Related papers  (2024-12-31T08:04:58Z)
• High-Performance and Scalable Fault-Tolerant Quantum Computation with Lattice Surgery on a 2.5D Architecture [0.5779598097190628]
  We propose a high-performance and low-overhead FTQC architecture based on lattice surgery (LS) using surface code (SC) The proposed Bypass architecture is a 2.5-dimensional architecture consisting of dense and sparse qubit layers. The results show that the Bypass architecture improves the fidelity of FTQC and both a 1.73x speedup and a 17% reduction in classical/quantum hardware resources.
  arXiv  Detail & Related papers  (2024-11-26T15:27:59Z)
• A Quantum Approximate Optimization Algorithm-based Decoder Architecture for NextG Wireless Channel Codes [6.52154420965995]
  Forward Error Correction (FEC) provides reliable data flow in wireless networks despite the presence of noise and interference. FEC processing demands significant fraction of a wireless network's resources, due to its computationally-expensive decoding process. We present FDeQ, a QAOA-based FEC Decoder design targeting the popular NextG wireless Low Density Parity Check (LDPC) and Polar codes. FDeQ achieves successful decoding with error performance at par with state-of-the-art classical decoders at low FEC code block lengths.
  arXiv  Detail & Related papers  (2024-08-21T15:53:09Z)
• Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension [2.9396076967931526]
  Quantum convolutional neural networks (QCNNs) represent a promising approach in quantum machine learning. We propose a QCNN architecture capable of handling arbitrary input data dimensions while optimizing the allocation of quantum resources.
  arXiv  Detail & Related papers  (2024-03-28T02:25:12Z)
• Reconfigurable Intelligent Surface (RIS)-Assisted Entanglement Distribution in FSO Quantum Networks [62.87033427172205]
  Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments where establishing an optical fiber infrastructure is challenging and costly. A reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual line-of-sight between users for entanglement distribution.
  arXiv  Detail & Related papers  (2024-01-19T17:16:40Z)
• Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits [70.97518416003358]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• 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)
• Deep Quantum Error Correction [73.54643419792453]
  Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. In this work, we efficiently train novel emphend-to-end deep quantum error decoders. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-01-27T08:16:26Z)
• Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
  arXiv  Detail & Related papers  (2022-04-18T21:45:13Z)
• 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 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.