MECH: Multi-Entry Communication Highway for Superconducting Quantum Chiplets

• URL: http://arxiv.org/abs/2305.05149v3
• Date: Mon, 26 Feb 2024 23:58:48 GMT
• Title: MECH: Multi-Entry Communication Highway for Superconducting Quantum Chiplets
• Authors: Hezi Zhang, Keyi Yin, Anbang Wu, Hassan Shapourian, Alireza Shabani, Yufei Ding
• Abstract summary: As the computing scale increases, communication between qubits would become a more severe bottleneck. We propose a multi-entry communication highway (MECH) mechanism to trade ancillary qubits for program. This implies a more efficient and less error-prone compilation of quantum programs.
• Score: 8.331379159321271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chiplet architecture is an emerging architecture for quantum computing that could significantly increase qubit resources with its great scalability and modularity. However, as the computing scale increases, communication between qubits would become a more severe bottleneck due to the long routing distances. In this paper, we propose a multi-entry communication highway (MECH) mechanism to trade ancillary qubits for program concurrency, and build a compilation framework to efficiently manage and utilize the highway resources. Our evaluation shows that this framework significantly outperforms the baseline approach in both the circuit depth and the number of operations on typical quantum benchmarks. This implies a more efficient and less error-prone compilation of quantum programs.

Related papers

• Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
  We develop a reinforcement learning-based quantum compiler for a superconducting processor. We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
  arXiv  Detail & Related papers  (2024-06-18T01:49:48Z)
• 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)
• Revisiting the Mapping of Quantum Circuits: Entering the Multi-Core Era [2.465579331213113]
  We introduce the Hungarian Qubit Assignment (HQA) algorithm, a multi-core mapping algorithm designed to optimize qubit assignments to cores with the aim of reducing inter-core communications. Our evaluation of HQA against state-of-the-art circuit mapping algorithms for modular architectures reveals a $4.9times$ and $1.6times$ improvement in terms of execution time and non-local communications.
  arXiv  Detail & Related papers  (2024-03-25T21:31:39Z)
• 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)
• 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)
• Mapping quantum circuits to modular architectures with QUBO [3.0148208709026005]
  In multi-core architectures, it is crucial to minimize the amount of communication between cores when executing an algorithm. We propose for the first time a Quadratic Unconstrained Binary Optimization technique to encode the problem and the solution. Our method showed promising results and performed exceptionally well with very dense and highly-parallelized circuits.
  arXiv  Detail & Related papers  (2023-05-11T09:45:47Z)
• Multi-User Entanglement Distribution in Quantum Networks Using Multipath Routing [55.2480439325792]
  We propose three protocols that increase the entanglement rate of multi-user applications by leveraging multipath routing. The protocols are evaluated on quantum networks with NISQ constraints, including limited quantum memories and probabilistic entanglement generation.
  arXiv  Detail & Related papers  (2023-03-06T18:06:00Z)
• Architectures for Multinode Superconducting Quantum Computers [17.518262577853033]
  One scalable method to construct an MNQC is using superconducting quantum systems with optical interconnects. In this paper, we quantify overall MNQC performance in terms of hardware models of internode links, entanglement distillation, and local architecture. We show how to navigate this tradeoff, lay out how compilers should optimize between local and internode gates, and discuss when noisy quantum links have an advantage over purely classical links.
  arXiv  Detail & Related papers  (2022-12-12T19:00:03Z)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• Fast Swapping in a Quantum Multiplier Modelled as a Queuing Network [64.1951227380212]
  We propose that quantum circuits can be modeled as queuing networks. Our method is scalable and has the potential speed and precision necessary for large scale quantum circuit compilation.
  arXiv  Detail & Related papers  (2021-06-26T10:55:52Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)

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.