Probabilistic unitary synthesis with optimal accuracy

TL;DR

This paper establishes fundamental bounds for probabilistic unitary synthesis, demonstrating its potential for significantly reducing approximation errors compared to deterministic methods, especially for single-qubit unitaries.

Contribution

It derives tight bounds for optimal probabilistic synthesis, introduces an SDP-based method to compute optimal distributions, and presents an efficient algorithm with quadratic error reduction.

Findings

Tight lower and upper bounds for probabilistic synthesis error.

Optimal probability distribution computed via SDP.

Quadratic reduction in approximation error for single-qubit unitaries.

Abstract

The purpose of unitary synthesis is to find a gate sequence that optimally approximates a target unitary transformation. A new synthesis approach, called probabilistic synthesis, has been introduced, and its superiority has been demonstrated over traditional deterministic approaches with respect to approximation error and gate length. However, the optimality of current probabilistic synthesis algorithms is unknown. We obtain the tight lower bound on the approximation error obtained by the optimal probabilistic synthesis, which guarantees the sub-optimality of current algorithms. We also show its tight upper bound, which improves and unifies current upper bounds depending on the class of target unitaries. These two bounds reveal the fundamental relationship of approximation error between probabilistic approximation and deterministic approximation of unitary transformations. From a computational point of view, we show that the optimal probability distribution can be computed by the semidefinite program (SDP) we construct. We also construct an efficient probabilistic synthesis algorithm for single-qubit unitaries, rigorously estimate its time complexity, and show that it reduces the approximation error quadratically compared with deterministic algorithms.