On Optimal Subarchitectures for Quantum Circuit Mapping

TL;DR

This paper investigates the optimal subarchitecture selection for quantum circuit mapping, refutes previous conjectures, and introduces methods for efficiently identifying near-optimal subarchitectures to improve quantum circuit compilation.

Contribution

It challenges the assumption that only minimal subarchitectures are sufficient for optimal mapping and proposes new methods for selecting near-optimal larger subarchitectures.

Findings

Refutes the conjecture that minimal subarchitectures suffice for optimal mapping.

Introduces criteria and methods for identifying near-optimal subarchitectures.

Demonstrates improved quantum circuit mapping on IBM, Google, and Rigetti hardware.

Abstract

Compiling a high-level quantum circuit down to a low-level description that can be executed on state-of-the-art quantum computers is a crucial part of the software stack for quantum computing. One step in compiling a quantum circuit to some device is quantum circuit mapping, where the circuit is transformed such that it complies with the architecture's limited qubit connectivity. Because the search space in quantum circuit mapping grows exponentially in the number of qubits, it is desirable to consider as few of the device's physical qubits as possible in the process. Previous work conjectured that it suffices to consider only subarchitectures of a quantum computer composed of as many qubits as used in the circuit. In this work, we refute this conjecture and establish criteria for judging whether considering larger parts of the architecture might yield better solutions to the mapping problem. We show that determining subarchitectures that are of minimal size, i.e., of which no physical qubit can be removed without losing the optimal mapping solution for some quantum circuit, is a very hard problem. Based on a relaxation of the criteria for optimality, we introduce a relaxed consideration that still maintains optimality for practically relevant quantum circuits. Eventually, this results in two methods for computing near-optimal sets of subarchitectures$\unicode{x2014}$providing the basis for efficient quantum circuit mapping solutions. We demonstrate the benefits of this novel method for state-of-the-art quantum computers by IBM, Google and Rigetti.