In distributed quantum computing architectures, with the network and communications functionalities provided by the Quantum Internet, remote quantum processing units can communicate and cooperate for executing computational tasks that single, noisy, intermediate-scale quantum devices cannot handle by themselves. To this aim, distributed quantum computing requires a new generation of quantum compilers, for mapping any quantum algorithm to any distributed quantum computing architecture. With this perspective, in this article, we first discuss the main challenges arising with compiler design for distributed quantum computing. Then, we analytically derive an upper bound of the overhead induced by quantum compilation for distributed quantum computing. The derived bound accounts for the overhead induced by the underlying computing architecture as well as the additional overhead induced by the suboptimal quantum compiler-expressly designed in this article to achieve three key features, namely, general-purpose, efficient, and effective. Finally, we validate the analytical results, and we confirm the validity of the compiler design through an extensive performance analysis.
In distributed quantum computing architectures, with the network and communications functionalities provided by the Quantum Internet, remote quantum processing units can communicate and cooperate for executing computational tasks that single, noisy, intermediate-scale quantum devices cannot handle by themselves. To this aim, distributed quantum computing requires a new generation of quantum compilers, for mapping any quantum algorithm to any distributed quantum computing architecture. With this perspective, in this article, we first discuss the main challenges arising with compiler design for distributed quantum computing. Then, we analytically derive an upper bound of the overhead induced by quantum compilation for distributed quantum computing. The derived bound accounts for the overhead induced by the underlying computing architecture as well as the additional overhead induced by the suboptimal quantum compiler-expressly designed in this article to achieve three key features, namely, general-purpose, efficient, and effective. Finally, we validate the analytical results, and we confirm the validity of the compiler design through an extensive performance analysis.
For most practical applications, quantum algorithms require large resources in terms of qubit number, much larger than those available with current NISQ processors. With the network and communication functionalities provided by the Quantum Internet, Distributed Quantum Computing (DQC) is considered as a scalable approach for increasing the number of available qubits for computational tasks. For DQC to be effective and efficient, a quantum compiler must find the best partitioning for the quantum algorithm and then perform smart remote operation scheduling to optimize EPR pair consumption. At the same time, the quantum compiler should also find the best local transformation for each partition. In this paper we present a modular quantum compilation framework for DQC that takes into account both network and device constraints and characteristics. We implemented and tested a quantum compiler based on the proposed framework with some circuits of interest, such as the VQE and QFT ones, considering different network topologies, with quantum processors characterized by heavy-hexagon coupling maps. We also devised a strategy for remote scheduling that can exploit both TeleGate and TeleData operations and tested the impact of using either only TeleGates or both. The evaluation results show that TeleData operations can have a positive impact on the number of consumed EPR pairs, depending on the characteristic of compiled circuit. Meanwhile, choosing a more connected network topology helps reduce the number of layers dedicated to remote operations.
Quantum algorithms require large resources in terms of qubit number, much larger than those available with current NISQ processors. With the network and communication functionalities provided by the Quantum Internet, Distributed Quantum Computing (DQC) is considered as a scalable approach for increasing the number of available qubits for computational tasks. For DQC to be effective and efficient, a quantum compiler must find the best partitioning for the quantum algorithm and then perform smart remote operation scheduling to optimize EPR pair consumption. At the same time, the quantum compiler should also find the best local transformation for each partition. In this paper we present a modular quantum compilation framework for DQC that takes into account both network and device constraints and characteristics. We implemented and tested a quantum compiler based on the proposed framework with some circuits of interest, such as the VQE and QFT ones, considering different network topologies, with quantum processors characterized by heavy hexagon coupling maps. We also devised a strategy for remote scheduling that can exploit both TeleGate and TeleData operations and tested the impact of using either only TeleGates or both. The evaluation results show that TeleData operations may have a positive impact on the number of consumed EPR pairs, while choosing a more connected network topology helps reduce the number of layers dedicated to remote operations. Keywords— Distributed quantum computing (DQC), quantum compilation, quantum Internet
Nowadays, quantum computing has reached the engineering phase, with fully-functional quantum processors integrating hundreds of noisy qubits. Yet – to fully unveil the potential of quantum computing out of the labs into the business reality – the challenge ahead is to substantially scale the qubit number, reaching orders of magnitude exceeding thousands of fault-tolerant qubits. To this aim, the distributed quantum computing paradigm is recognized as the key solution for scaling the number of qubits. Indeed, accordingly to such a paradigm, multiple small-to-moderate-scale quantum processors communicate and cooperate for executing computational tasks exceeding the computational power of single processing devices. 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 from a computer and communications engineering perspective. Furthermore, this survey provides an easy access and guide towards the relevant literature and the prominent results in the field.
Quantum computing is a promising paradigm for problems that are challenging for classical machines, but realizing that promise requires far more qubits than a single processor can offer. Distributed quantum computing (DQC) scales out by connecting multiple quantum processing units (QPUs), at the cost of making entanglement the scarce resource: every remote gate consumes a Bell pair, and inter-QPU links generate Bell pairs at finite rates orders of magnitude slower than local gates. Since quantum programs are executed repeatedly, this rate bounds how fast shots complete and thus how fast results are obtained. Existing DQC compilers emit a single implementation per circuit, so throughput is capped by its busiest link while other links stay idle. We observe that alternative compilations of the same circuit are logically equivalent yet stress different links; executing them concurrently and pooling their samples converts idle Bell pairs into additional shots. We formulate the joint selection of compilations and allocation of shots under per-link Bell-pair capacities as Candidate-Constrained Max-Shot Allocation (CMA), prove it NP-hard, and solve it with a dynamic program and its approximate variant AppDP, a compact MILP, and a greedy heuristic Effi. In simulation on six-QPU networks, multi-compilation raises throughput by 2-4.5* over the single compilation and correspondingly improves output fidelity under equal Bell-pair budgets. On hardware, it increases measured fidelity by up to 93% and reaches the single-compilation fidelity in up to 10* less time. Scaling to 36 QPUs and 144-qubit circuits, MILP improves throughput over the single compilation by 76.8% on average across 48 configurations while Effi allocates in milliseconds.
The Quantum Internet, by enabling quantum communications among remote quantum nodes, is a network capable of supporting functionalities with no direct counterpart in the classical world. Indeed, with the network and communications functionalities provided by the Quantum Internet, remote quantum devices can communicate and cooperate for solving challenging computational tasks by adopting a distributed computing approach. The aim of this study is to provide the reader with an overview about the main challenges and open problems arising in the design of a distributed quantum computing ecosystem. For this, the authors provide a survey, following a bottom‐up approach, from a communications engineering perspective. They start by introducing the Quantum Internet as the fundamental underlying infrastructure of the distributed quantum computing ecosystem. Then they go further, by elaborating on a high‐level system abstraction of the distributed quantum computing ecosystem. They describe such an abstraction through a set of logical layers. Thereby, they clarify dependencies among the aforementioned layers and, at the same time, a road‐map emerges.
Practical distributed quantum computing requires the development of efficient compilers, able to make quantum circuits compatible with some given hardware constraints. This problem is known to be tough, even for local computing. Here, we address it on distributed architectures. As generally assumed in this scenario, telegates represent the fundamental remote (inter-processor) operations. Each telegate consists of several tasks: (i) entanglement generation and distribution, (ii) local operations, and (iii) classical communications. Entanglement generations and distribution is an expensive resource, as it is time-consuming. To mitigate its impact, we model an optimization problem that combines running-time minimization with the usage of distributed entangled states. Specifically, we formulated the distributed compilation problem as a dynamic network flow. To enhance the solution space, we extend the formulation, by introducing a predicate that manipulates the circuit given in input and parallelizes telegate tasks. To evaluate our framework, we split the problem into three sub-problems, and solve it by means of an approximation routine. Experiments demonstrate that the run-time is resistant to the problem size scaling. Moreover, we apply the proposed algorithm to compile circuits under different topologies, showing that topologies with a higher ratio between edges and nodes give rise to shallower circuits.