Benchmarking Emerging Cavity-Mediated Quantum Interconnect Technologies for Modular Quantum Computers

• URL: http://arxiv.org/abs/2407.15651v1
• Date: Mon, 22 Jul 2024 14:11:20 GMT
• Title: Benchmarking Emerging Cavity-Mediated Quantum Interconnect Technologies for Modular Quantum Computers
• Authors: Sahar Ben Rached, Sergio Navarro Reyes, Junaid Khan, Carmen G. Almudever, Eduard Alarcon, Sergi Abadal,
• Abstract summary: This work presents a comparative analysis of the cavity-mediated interconnect technologies according to a defined figure of merit. We identify the configurations related to the cavity and atomic decay rates as well as the qubit-cavity coupling strength that meet the efficiency thresholds.
• Score: 1.0653685964361501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modularity is a promising approach for scaling up quantum computers and therefore integrating higher qubit counts. The essence of such architectures lies in their reliance on high-fidelity and fast quantum state transfers enabled by generating entanglement between chips. In addressing the challenge of implementing quantum coherent communication channels to interconnect quantum processors, various techniques have been proposed to account for qubit technology specifications and the implemented communication protocol. By employing Design Space Exploration (DSE) methodologies, this work presents a comparative analysis of the cavity-mediated interconnect technologies according to a defined figure of merit, and we identify the configurations related to the cavity and atomic decay rates as well as the qubit-cavity coupling strength that meet the efficiency thresholds. We therefore contribute to benchmarking contemporary cavity-mediated quantum interconnects and guide the development of reliable and scalable chip-to-chip links for modular quantum computers.

Related papers

• Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures [1.8781124875646162]
  This research contributes to the advancement of scalable quantum computing systems by introducing a novel learning-based approach for efficient quantum circuit compilation and mapping. In this work, we propose a novel approach employing Deep Reinforcement Learning (DRL) methods to learn theses for a specific multi-core architecture.
  arXiv  Detail & Related papers  (2024-06-17T12:09:11Z)
• 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)
• Peptide Binding Classification on Quantum Computers [3.9540968630765643]
  We conduct an extensive study on using near-term quantum computers for a task in the domain of computational biology. We perform sequence classification on a task relevant to the design of therapeutic proteins, and find competitive performance with classical baselines of similar scale. This work constitutes the first proof-of-concept application of near-term quantum computing to a task critical to the design of therapeutic proteins.
  arXiv  Detail & Related papers  (2023-11-27T10:32:31Z)
• Towards Quantum-Native Communication Systems: New Developments, Trends, and Challenges [63.67245855948243]
  The survey examines technologies such as quantum-domain (QD) multi-input multi-output (MIMO), QD non-orthogonal multiple access (NOMA), quantum secure direct communication (QSDC) The current status of quantum sensing, quantum radar, and quantum timing is briefly reviewed in support of future applications.
  arXiv  Detail & Related papers  (2023-11-09T09:45:52Z)
• Characterizing the Inter-Core Qubit Traffic in Large-Scale Quantum Modular Architectures [2.465579331213113]
  We present a pioneering characterization of the era of monolithic-temporal inter-core qubit traffic in large-scale circuits. The programs are executed on an all-to-all connected-core architecture that supports up to around 1000 qubits. Based on the showcased results, we provide a set of guidelines to improve mapping quantum circuits to multi-core processors, and lay the foundations of benchmarking large-scale multi-core architectures.
  arXiv  Detail & Related papers  (2023-10-03T09:54:41Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• A quantum-bit encoding converter [0.0]
  We certify the protocol on a complete set of single-photon qubits, successfully converting them to cat-state qubits with fidelities exceeding the classical limit. Our result demonstrates an essential tool for enabling interconnected quantum devices and architectures with enhanced versatility and scalability.
  arXiv  Detail & Related papers  (2022-11-18T19:00:08Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Characterizing Qubit Traffic of a Quantum Intranet aiming at Modular Quantum Computers [1.8602413562219944]
  Quantum-core processors are envisioned as the ultimate solution for the scalability of quantum computers. We present a technique to perform a-temporal characterization of quantum circuits running in multi-chip interconnected quantum computers.
  arXiv  Detail & Related papers  (2022-08-31T21:33:17Z)
• Quantum Semantic Communications for Resource-Efficient Quantum Networking [52.3355619190963]
  This letter proposes a novel quantum semantic communications (QSC) framework exploiting advancements in quantum machine learning and quantum semantic representations. The proposed framework achieves approximately 50-75% reduction in quantum communication resources needed, while achieving a higher quantum semantic fidelity.
  arXiv  Detail & Related papers  (2022-05-05T03:49:19Z)
• Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
  We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
  arXiv  Detail & Related papers  (2021-12-27T21:15:35Z)

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.