Michele Grossi

Preprint Jul 2026

MPStab: an hybrid stabilizers tensor-network quantum circuit simulator

The development of techniques for simulating quantum systems using classical computers is a paramount task for two primary reasons: i) there exist configurations for which classical computers are remarkably effective and will continue to be so, and ii) exploring the limits of classical computation facilitates the identification of the regimes of competence for quantum computers. In this work, we present MPStab, a quantum circuit simulator based on a hybrid formalism combining stabilizers and tensor networks, recently introduced in Ref. [1]. We present the package, its core functionalities, and explore its performances in a few interesting simulation regimes.

Giulio Crognaletti, Mattia Robbiano, Michele Grossi et al. · 1 citation

Preprint Jul 2026

Universal Optimization and Tighter Fidelity Bounds for Approximate Quantum Error Correction

Approximate quantum error correction (AQEC) not only dictates the performance of discrete- and continuous-variable quantum error correction codes but also serves as a unifying framework across various physical disciplines. Identifying the optimal recovery channel to maximize the entanglement fidelity via standard semidefinite programming is computationally bottlenecked by the exponentially growing number of Kraus operators with system size, rendering large-scale optimization prohibitive. While analytical near-optimal maps exist, they typically work only when the Knill-Laflamme conditions are nearly satisfied. In this Letter, we establish an efficient framework by leveraging the duality between recovery and environment decoupling. This framework yields a tighter analytical lower bound on entanglement fidelity than the conventional limit set by the transpose channel. Furthermore, by exploiting the decayed weights of noise Kraus operators, we introduce a framework based on principal component analysis to reduce the dimension. In thermal loss channels where the weights decay exponentially, our approach achieves a 33-fold computational speedup while maintaining rigorous accuracy. Our approach enables high-precision optimization for AQEC codes that were previously intractable due to the curse of dimensionality.

Jing Wu, Michele Grossi, D. Kurkcuoglu et al. · 0 citations