Late Breaking Results: Hardware-Aware Compilation Reshapes Trainability in Variational Quantum Circuits

TL;DR

This paper investigates how hardware-aware compilation affects the trainability of variational quantum circuits by altering gradient statistics and the optimization landscape across different circuit architectures.

Contribution

It provides the first systematic analysis of transpilation's impact on VQC trainability, revealing architecture-dependent effects and emphasizing the need for compilation-aware design.

Findings

Densely entangling circuits show significant gradient reshaping in shallow regimes.

Structured tensor-network circuits are relatively robust to transpilation effects.

Deep circuits exhibit minimal sensitivity to hardware-aware compilation.

Abstract

Variational quantum circuits (VQCs) are typically evaluated at the logical design level when analyzing trainability. However, execution on real quantum devices requires hardware-aware compilation (transpilation) to satisfy qubit connectivity and native gate-set constraints. In this paper, we examine how transpilation can alter the gradient statistics. Using parameter-shift differentiation and gradient variance estimation, we compare logical and transpiled circuits across three representative ansatz families: EfficientSU2 (dense entanglement), TTN (tree tensor network), and RealAmplitudes (linear entanglement). We observe architecture-dependent trainability shifts where densely entangling circuits exhibit pronounced gradient reshaping in shallow regimes, structured tensor-network circuits remain comparatively robust, and linear architectures show mixed behavior. Deep circuits across all families display minimal sensitivity to hardware-aware compilation. These findings demonstrate that transpilation acts as an implicit structural transformation of the optimization landscape, motivating compilation-aware analysis and co-design for VQCs.