Optimized Compilation for Distributed Quantum Computing

TL;DR

This paper introduces a greedy compilation algorithm for distributed quantum computing that reduces EPR pair usage and circuit depth by smart gate grouping and ordering, improving scalability with limited EPR lifetime.

Contribution

It presents a novel greedy algorithm for optimizing non-local gate scheduling and EPR pair sharing in distributed quantum circuits, considering EPR pair lifetime constraints.

Findings

Reduced circuit depth and EPR pair consumption.

Effective sharing of EPR pairs among multiple gates.

Benefits observed even with low EPR pair lifetime.

Abstract

In many practical applications, quantum algorithms require several qubits, significantly more than those available with current noisy intermediate-scale quantum processors. Distributed quantum computing (DQC) is considered a scalable approach to increasing the number of available qubits for computational tasks. In the DQC setting, a quantum compiler must find the best partitioning for the quantum algorithm and then perform smart non-local operations scheduling to optimize the consumption of Einstein-Podolsky-Rosen (EPR) pairs. In this work, the focus is on minimizing the use of EPR pairs when the circuit structure allows for multiple non-local gates to utilize a single TeleGate operation. This is achieved by using a greedy algorithm that explores the circuit and groups together the gates that could share an EPR pair while also changing the order of commutative gates when necessary. With this preliminary pass, the compiled circuits show reduced depth and EPR usage. Since the quality of each EPR pair quickly deteriorates, the number of non-local gates using the same EPR pair should also be bounded. This means that, depending on the features of the target quantum network, the user can achieve different levels of optimization. Here, it is shown that this approach brings benefits even while assuming a low EPR pair lifetime.