Synthesizing Quantum-Circuit Optimizers

TL;DR

The paper introduces QUESO, an automated method for synthesizing quantum-circuit optimizers tailored to specific quantum devices, significantly improving optimization speed and quality over existing tools.

Contribution

The paper presents QUESO, a novel approach that automatically synthesizes quantum-circuit optimizers using algebraic, probabilistic, and search techniques, adaptable to new quantum hardware.

Findings

QUESO can synthesize optimizers in 1.2 minutes for IBM devices.

Outperforms IBM's Qiskit and TKET on 85% of benchmark circuits.

Provides high-probability correctness guarantees for optimized circuits.

Abstract

Near-term quantum computers are expected to work in an environment where each operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied to minimize the number of noisy operations. Today, physicists are constantly experimenting with novel devices and architectures. For every new physical substrate and for every modification of a quantum computer, we need to modify or rewrite major pieces of the optimizer to run successful experiments. In this paper, we present QUESO, an efficient approach for automatically synthesizing a quantum-circuit optimizer for a given quantum device. For instance, in 1.2 minutes, QUESO can synthesize an optimizer with high-probability correctness guarantees for IBM computers that significantly outperforms leading compilers, such as IBM's Qiskit and TKET, on the majority (85%) of the circuits in a diverse benchmark suite. A number of theoretical and algorithmic insights underlie QUESO: (1) An algebraic approach for representing rewrite rules and their semantics. This facilitates reasoning about complex symbolic rewrite rules that are beyond the scope of existing techniques. (2) A fast approach for probabilistically verifying equivalence of quantum circuits by reducing the problem to a special form of polynomial identity testing. (3) A novel probabilistic data structure, called a polynomial identity filter (PIF), for efficiently synthesizing rewrite rules. (4) A beam-search-based algorithm that efficiently applies the synthesized symbolic rewrite rules to optimize quantum circuits.