Compiling Together: High-Throughput Distributed Quantum Computing via Multi-Compilation

2026-10-02

AI Summary

This study addresses the bottleneck in distributed quantum computing where single-compilation schemes leave communication links idle and constrain throughput to the busiest link. To overcome this, we propose a multi-compilation parallel execution paradigm that concurrently runs multiple logically equivalent compilation variants. Bell pair allocation is formulated as an NP-hard combinatorial optimization problem, solved via dynamic programming, mixed-integer linear programming, and efficient heuristics, thereby converting idle entanglement resources into useful samples. Experimental evaluations demonstrate a 2× to 4.5× throughput improvement in simulations. Hardware implementations achieve up to a 93% increase in fidelity and reduce the time-to-target by an order of magnitude.

Abstract

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.

Problem

• Distributed Quantum Computing
• Quantum Compilation
• Throughput Optimization
• Entanglement Resource
• Multi-Compilation

Innovation

• Distributed Quantum Computing
• Multi-Compilation
• Throughput Optimization
• Bell Pair Allocation
• Quantum Compiler