The noisy and lengthy nature of quantum communication hinders the development of distributed quantum computing. The inefficient design of existing compilers for distributed quantum computing worsens the situation. Previous compilation frameworks couple communication hardware with the implementation of expensive remote gates. However, we discover that the efficiency of quantum communication, especially collective communication, can be significantly boosted by decoupling communication resources from remote operations, that is, the communication hardware would be used only for preparing remote entanglement, and the computational hardware, the component used to store program information, would be used for conducting remote gates. Based on the observation, we develop a compiler framework to optimize the collective communication happening in distributed quantum programs. In this framework, we decouple the communication preparation process in communication hardware from the remote gates conducted in computational hardware by buffering EPR pairs generated by communication hardware in qubits of the computational hardware. Experimental results show that the proposed framework can halve the communication cost of various distributed quantum programs, compared to state-of-the-art compilers for distributed quantum computing.
The noisy and lengthy nature of quantum communication hinders the development of distributed quantum computing. The inefficient design of existing compilers for distributed quantum computing worsens the situation. Previous compilation frameworks couple communication hardware with the implementation of expensive remote gates. However, we discover that the efficiency of quantum communication, especially collective communication, can be significantly boosted by decoupling communication resources from remote operations, that is, the communication hardware would be used only for preparing remote entanglement, and the computational hardware, the component used to store program information, would be used for conducting remote gates. Based on the observation, we develop a compiler framework to optimize the collective communication happening in distributed quantum programs. In this framework, we decouple the communication preparation process in communication hardware from the remote gates conducted in computational hardware by buffering EPR pairs generated by communication hardware in qubits of the computational hardware. Experimental results show that the proposed framework can halve the communication cost of various distributed quantum programs, compared to state-of-the-art compilers for distributed quantum computing.
Distributed quantum computing (DQC) is a scalable way to build a large-scale quantum computing system. Previous compilers for DQC focus on either qubit-to-qubit inter-node gates or qubit-to-node nonlocal circuit blocks, missing opportunities of optimizing collective communication which consists of nonlocal gates over multiple nodes. In this paper, we observe that by utilizing patterns of collective communication, we can greatly reduce the amount of inter-node communication required to implement a group of nonlocal gates. We propose QuComm, the first compiler framework which unveils and analyzes collective communication patterns hidden in distributed quantum programs and efficiently routes inter-node gates on any DQC architecture based on discovered patterns, cutting down the overall communication cost of the target program. We also provide the first formalization of the communication buffer concept in DQC compiling. The communication buffer utilizes data qubits to store remote entanglement so that we can ensure enough communication resources on any DQC architecture to support the proposed optimizations for collective communication. Experimental results show that, compared to the state-of-the-art baseline, QuComm reduces the amount of inter-node communication by 54.9% on average, over various distributed quantum programs and DQC hardware configurations.
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.
Distributed quantum computing (DQC) is a promising approach to extending the computational power of near-term quantum hardware. However, the non-local quantum communication between quantum nodes is much more expensive and error-prone than the local quantum operation within each quantum device. Previous DQC compilers focus on optimizing the implementation of each non-local gate and adopt similar compilation designs to single-node quantum compilers. The communication patterns in distributed quantum programs remain unexplored, leading to a far-from-optimal communication cost. In this paper, we identify burst communication, a specific qubit-node communication pattern that widely exists in various distributed quantum programs and can be leveraged to guide communication overhead optimization. We then propose AutoComm, an automatic compiler framework to extract burst communication patterns from input programs and then optimize the communication steps of burst communication discovered. Compared to state-of-the-art DQC compilers, experimental results show that our proposed AutoComm can reduce the communication resource consumption and the program latency by 72.9% and 69.2% on average, respectively.
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.
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.
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.