Fadhil Fatih Shiddiq | Searcharxiv

arXiv2026

Predicting Multipartite Entanglement in Quantum Circuits using Transformer

Multipartite entanglement is a critical property of parameterized quantum circuits (PQCs), particularly for near-term hybrid quantum-classical algorithms, as it characterizes their ability to generate highly entangled states. However, measuring entanglement remains computationally expensive because conventional Monte Carlo sampling scales unfavorably with system size. To overcome this challenge, we introduce a graph-based transformer surrogate that predicts both the first-order Meyer-Wallach measure ($Q_1$) and the second-order Scott measure ($Q_2$), resolving entanglement structures indistinguishable under $Q_1$ alone. Our central contribution is the qubit-interconnected graph (QIG) encoding for transformers, where each node represents a qubit and weighted adjacencies record entangling-gate multiplicities. Fused with a gate-level DAG encoder, this yields the QIG-Fusion model. Evaluated on 50,000 circuits spanning 4- to 8-qubit systems across a ten-seed protocol, QIG-Fusion achieves an RMSE as low as 0.037 ($Q_2$) and 0.038 ($Q_1$), with a Spearman rank correlation up to 0.95. This framework significantly reduces the computational cost of Quantum Architecture Search (QAS), enabling efficient entanglement estimation for large-scale PQCs.

quant-ph↗

arXiv2026

Pauli Weight Hamiltonian Term Selection for Optimized Machine Learning Based Quantum Error Mitigation

Machine learning provides a scalable solution for quantum error mitigation. However, the selection of appropriate Pauli strings for inclusion in training data remains a challenge. Current methods rely on heuristic or uniform random sampling, requiring data for every Pauli string in the Hamiltonian, a process that scales linearly with measurements and grows with system size. To address this, we introduce quantum error mitigation with prior knowledge of Pauli weights (Pauli weight quantum error mitigation (Pi-QEM)), a systematic framework that selects training observables based on Pauli weight. By leveraging the relationship between variance and locality in parameterized quantum circuits, Pi-QEM trains on a small subset of dominant, low-weight Pauli strings. In numerical simulations of molecular systems on a noisy IBM quantum backend, Pi-QEM reduces ground-state energy estimation error by up to 34.01% using just a single dominant local observable, offering an efficient, scalable pathway for high-precision error mitigation on NISQ devices.

quant-ph↗