Distributed Quantum Computing: a Survey

TL;DR

This survey analyzes distributed quantum computing (DQC) as a scalable path beyond single-chip quantum processors by connecting modular QPUs through quantum networking. It structures the discussion around four interdependent pillars—algorithms, networking, compiling, and simulation—and explains foundational concepts (qubits, circuits, measurement), nonlocal communication primitives (teleportation, teledata, telegate), and resource trade-offs (entanglement, ebits, qubit assignment). It highlights open problems including formal distributability metrics, noise-aware compilation, and the need for standardized hardware abstractions (Quantum-HAL) and industrial adoption. The work emphasizes practical engineering perspectives, outlining methodical approaches, evaluation frameworks, and future directions for integrating DQC into cloud/ICT ecosystems with standardized interfaces and scalable simulation tools.

Abstract

Nowadays, quantum computing has reached the engineering phase, with fully-functional quantum processors integrating hundred of noisy qubits available. Yet -- to fully unveil the potential of quantum computing out of the labs and into business reality -- the challenge ahead is to substantially scale the qubit number, reaching orders of magnitude exceeding the thousands (if not millions) of noise-free qubits. To this aim, there exists a broad consensus among both academic and industry communities about considering the distributed computing paradigm as the key solution for achieving such a scaling, by envision multiple moderate-to-small-scale quantum processors communicating and cooperating to execute computational tasks exceeding the computational resources available within a single processing device. The aim of this survey is to provide the reader with an overview about the main challenges and open problems arising with distributed quantum computing, and with an easy access and guide towards the relevant literature and the prominent results from a computer/communications engineering perspective.

Figures (22)

• Figure 1: Quantum computing power of isolated vs interconnected processors. The volume of each cube graphically represents the ideal -- i.e., noise free -- quantum computing power as the number of qubits within each processor scales. Figure reproduced from CuoCalCac-20.
• Figure 2: Structure of the Survey
• Figure 3: Distributed Quantum Computing. The four different perspectives overviewed within the survey, with algorithms, compiling and networking represented as interdependent layers of a distributed quantum computing architecture, and simulation represented as an inter-layer enabler, covering layers exhibiting quantumness, namely, algorithms and networking.
• Figure 4: Bloch sphere: geometrical representation of a qubit in spherical coordinates. A pure state $\ket{\varphi} = \alpha \ket{0} + \beta \ket{1}$ is represented by a point on the sphere surface, with $\alpha = \cos{\frac{\theta}{2}}$ and $\beta = e^{i \phi} \sin{\frac{\theta}{2}}$.
• Figure 5: Quantum circuit for generating a two-qubit entangled state -- namely, the Bell state in \ref{['Eq:2']} -- starting from the input state $\ket{00}$. Time flows from left to right: the first qubit undergoes through a single-qubit Hadamard gate -- denoted with H -- followed by a two-qubit CNOT gate -- represented by $\bullet$ and $\oplus$ symbols interconnected by a vertical line -- both defined in Table \ref{['Tab:01']}.