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IEEE International Conference on Quantum Computing and Engineering (QCE)

A Reference Implementation for a Quantum Message Passing Interface

Tommy Nguyen, Yue Shi, Samuel Stein, Timothy Stavenger, Marvin G. Warner, Martin Roetteler, Torsten Hoefler, Ang Li

Published Sep 17, 2023• 3 citations
Publisher

Abstract

Quantum computers today are noisy and limited in the number of qubits that can be set with reasonable fidelities and gate times for each system. Since the current limit on the number of qubits is difficult to surpass, it is expected that the future of quantum computing will lie in connecting different quantum computers together to form a quantum network, each with its own limitations. Herein lies the field of distributed quantum computing optimized for large-scale communication. One such system that has been designed is the Quantum Message Passing Interface (QMPI). This interface, unlike the classical MPI, takes advantage of quantum teleportation to communicate between different quantum nodes but leverages the framework built for classical MPI. This general interface is essential as we try to connect various quantum computers, as it can be used to develop distributed quantum algorithms across various nodes. In this work, we develop a reference implementation for a QMPI interface in Qiskit and characterize its performance. In our interface, we have implemented point-to-point and optimized collective operations for larger distributed quantum networks. To test our framework, we demonstrate our implementation with applications that can leverage the QMPI framework including Trotterization of the transverse field Ising model and distributive quantum phase estimation. Lastly, we have developed a new technique for decomposing quantum gates across many nodes which leverages multi-controlled Toffolli gates. This technique beats the state of the art for an adder circuit in terms of fidelity and the number of EPR pairs used.

DOI
10.1109/qce57702.2023.10248

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Quantum computers today are noisy and limited in the number of qubits that can be set with reasonable fidelities and gate times for each system. Since the current limit on the number of qubits is difficult to surpass, it is expected that the future of quantum computing will lie in connecting different quantum computers together to form a quantum network, each with its own limitations. Herein lies the field of distributed quantum computing optimized for large-scale communication. One such system that has been designed is the Quantum Message Passing Interface (QMPI). This interface, unlike the classical MPI, takes advantage of quantum teleportation to communicate between different quantum nodes but leverages the framework built for classical MPI. This general interface is essential as we try to connect various quantum computers, as it can be used to develop distributed quantum algorithms across various nodes. In this work, we develop a reference implementation for a QMPI interface in Qiskit and characterize its performance. In our interface, we have implemented point-to-point and optimized collective operations for larger distributed quantum networks. To test our framework, we demonstrate our implementation with applications that can leverage the QMPI framework including Trotterization of the transverse field Ising model and distributive quantum phase estimation. Lastly, we have developed a new technique for decomposing quantum gates across many nodes which leverages multi-controlled Toffolli gates. This technique beats the state of the art for an adder circuit in terms of fidelity and the number of EPR pairs used.
Practical applications of quantum computing are currently limited by the number of qubits that can be set with reasonable fidelity for each system. Therefore, a distributed quantum computing system with multiple quantum computers coherently connected is highly demanding. To realize the inter-node communication of quantum information, the software interface, Quantum Message Passing Interface (QMPI), leveraging the framework built for classical MPI but taking advantage of quantum teleportation to communicate between different quantum nodes was proposed. In this work, we developed a QMPI implementation with a variety of point-to-point and collective operations in Qiskit and characterize its performance by demonstrating the reference implementations on a few benchmark quantum applications. Moreover, we developed a new approach for optimizing collective communications for the distributed quantum programs with multi-controlled Toffoli gates. This approach outperforms current state-of-the-art in terms of fidelity and the number of remote EPR pairs in both simulations and experiments over IBM’s quantum platforms.
Practical applications of quantum computers require millions of physical qubits and it will be challenging for individual quantum processors to reach such qubit numbers. It is therefore timely to investigate the resource requirements of quantum algorithms in a distributed setting, where multiple quantum processors are interconnected by a coherent network. We introduce an extension of the Message Passing Interface (MPI) to enable high-performance implementations of distributed quantum algorithms. In turn, these implementations can be used for testing, debugging, and resource estimation. In addition to a prototype implementation of quantum MPI, we present a performance model for distributed quantum computing, SENDQ. The model is inspired by the classical LogP model, making it useful to inform algorithmic decisions when programming distributed quantum computers. Specifically, we consider several optimizations of two quantum algorithms for problems in physics and chemistry, and we detail their effects on performance in the SENDQ model.
Distributed quantum computing (DQC) is emerging as a practical solution to overcome the scalability limits of individual quantum processors by interconnecting them via quantum networks. However, programming such systems remains a significant challenge due to the need to coordinate quantum operations, manage entanglement, and synchronize classical communication. This paper proposes NetQMPI as a Python library that enables high-level programming of distributed quantum applications by building on top of two key foundations: NetQASM, which abstracts the physical network layer to interface with quantum nodes and simulators such as NetSquid and SimulaQron; and NetQIR, which proposes a layered abstraction model suitable for defining a quantum version of the classical Message Passing Interface (MPI). NetQMPI provides an MPI-style programming model tailored to quantum systems. It introduces primitives such as qsend and qrecv that abstract quantum communication details like entanglement generation, and protocols. Collective operations such as qscatter or qgather are also included. This allows developers to focus on distributed algorithm design while relying on NetQMPI to handle low-level operations efficiently.
Distributed Quantum Computing (DQC) is essential for scaling quantum algorithms beyond the limitations of monolithic NISQ devices. However, the current software ecosystem forces developers to manually orchestrate low-level network resources, such as entanglement generation (EPR pairs) and classical synchronization, leading to verbose, error-prone, and non-scalable code. This paper introduces \textbf{NetQMPI}, a high-level Python framework that adapts the Message Passing Interface (MPI) standard to the quantum domain using the Single Program Multiple Data (SPMD) paradigm. Built as a middleware over the NetQASM SDK, NetQMPI abstracts the underlying physical topology, automating network initialization and resource management through a unified Communicator interface. We propose semantic point-to-point primitives and novel collective operations--such as expose and unexpose--that address the constraints of the No-Cloning Theorem by leveraging multipartite entanglement for data distribution. Our comparative analysis demonstrates that NetQMPI decouples algorithmic logic from network size, reducing the code complexity for generating an NNN-node GHZ state from O(N2)\mathcal{O}(N^2)O(N2) to constant complexity O(1)\mathcal{O}(1)O(1). Furthermore, the framework ensures backend agnosticism, enabling the seamless execution of high-level applications on rigorous physical simulators, such as NetSquid (via SquidASM), and future quantum hardware adhering to the NetQASM standard.
Quantum computing has been an attractive method adapted for increasing the computational speed and it is governed by the laws of quantum mechanics. Quantum computers are not limited to two states as compared with classical systems, but they encode information as quantum bits or qubits. A qubit represents atoms, ions, photons or electrons and their respective control devices that are working together to act as computer memory and processors. The power of quantum computing lies in its multi-state representation which makes it million times more powerful than classical computer systems. Quantum computers also use another aspect of quantum mechanics known as entanglement, which is a property where multiple objects existing in the states that can be linked together. There exists a standard/specification of message passing library, known as message passing interface (MPI) that can be used for achieving high-performance computing. The goal of MPI is to provide a standardized framework for writing message-passing programs which can be used for programming systems that require distributed processing. The superposition principle used by the quantum algorithm can be simulated in the MPI environment with a cluster of computers or multi-CPU systems. This paper focuses on exposing the strength of quantum algorithm in high performance computing by using MPI, and also on comparing the sequential and parallel execution of programs on the basis of execution time and CPU utilization.
The rapid advancement of quantum computing has highlighted the need for scalable and efficient software infrastructures to fully exploit its potential. Current quantum processors face significant scalability constraints due to the limited number of qubits per chip. In response, distributed quantum computing (DQC) —achieved by networking multiple quantum processor units (QPUs)— is emerging as a promising solution. To support this paradigm, robust intermediate representations (IRs) are needed to translate high-level quantum algorithms into executable instructions suitable for distributed systems. This paper presents NetQIR, an extension of Microsoft’s Quantum Intermediate Representation (QIR), specifically designed to facilitate DQC by incorporating new instruction specifications. NetQIR was developed in response to the lack of abstraction at the network and hardware layers identified in the existing literature as a significant obstacle to effectively implementing distributed quantum algorithms. Based on this analysis, NetQIR introduces new essential abstraction features to support compilers in DQC contexts. It defines network communication instructions independent of specific hardware, abstracting the complexities of inter-QPU communication. Although the proposed work allows abstraction of the underlying network, it is important to note that it is intended for the development of high-performance code on future modular quantum architectures. Leveraging the QIR framework, NetQIR aims to bridge the gap between high-level quantum algorithm design and low-level hardware execution, thus promoting modular and scalable approaches to quantum software infrastructures for distributed applications. Furthermore, its design may serve as a foundational component for future implementations of distributed quantum standards such as the Quantum Message Passing Interface (QMPI).