TL;DR
This paper introduces a parallel algorithm for local optimization of quantum circuits, significantly reducing computational overhead and enabling efficient, scalable circuit optimization while maintaining local optimality guarantees.
Contribution
It presents a novel parallel algorithm for local quantum circuit optimization that improves efficiency and scalability over sequential methods, with proven theoretical bounds.
Findings
Requires O(n log n) work for circuit size n
Operates with O(r log n) span over r rounds
Ensures local optimality of the optimized circuit
Abstract
Optimization of quantum programs or circuits is a fundamental problem in quantum computing and remains a major challenge. State-of-the-art quantum circuit optimizers rely on heuristics and typically require superlinear, and even exponential, time. Recent work proposed a new approach that pursues a weaker form of optimality called local optimality. Parameterized by a natural number , local optimality insists that each and every -segment of the circuit is optimal with respect to an external optimizer, called the oracle. Local optimization can be performed using only a linear number of calls to the oracle but still incurs quadratic computational overheads in addition to oracle calls. Perhaps most importantly, the algorithm is sequential. In this paper, we present a parallel algorithm for local optimization of quantum circuits. To ensure efficiency, the algorithm operates…
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