Analog quantum simulation leverages native hardware dynamics to emulate complex quantum systems with great efficiency by bypassing the quantum circuit abstraction. However, conventional compilation methods for analog simulators are typically labor-intensive, prone to errors, and computationally demanding. This paper introduces QTurbo, a powerful analog quantum simulation compiler designed to significantly enhance compilation efficiency and optimize hardware execution time. By generating precise and noise-resilient pulse schedules, our approach ensures greater accuracy and reliability, outperforming the existing state-of-the-art approach.

This paper introduces Fermihedral, a compiler framework focusing on discovering the optimal Fermion-to-qubit encoding for targeted Fermionic Hamiltonians. Fermion-to-qubit encoding is a crucial step in harnessing quantum computing for efficient simulation of Fermionic quantum systems. Utilizing Pauli algebra, Fermihedral redefines complex constraints and objectives of Fermion-to-qubit encoding into a Boolean Satisfiability problem which can then be solved with high-performance solvers. To accommodate larger-scale scenarios, this paper proposed two new strategies that yield approximate optimal solutions mitigating the overhead from the exponentially large number of clauses. Evaluation across diverse Fermionic systems highlights the superiority of Fermihedral, showcasing substantial reductions in implementation costs, gate counts, and circuit depth in the compiled circuits. Real-system experiments on IonQ's device affirm its effectiveness, notably enhancing simulation accuracy.

Silicon quantum dot devices stand as promising candidates for large scale quantum computing due to their extended coherence times compact size, and recent experimental demonstrations of sizable qubit arrays. Despite the great potential, controlling these arrays remains a significant challenge. This paper introduces a new virtual gate extraction method to quickly establish orthogonal control on the potentials for individual quantum dots. Leveraging insights from the device physics, the proposed approach significantly re duces the experimental overhead by focusing on crucial regions around charge state transition. Furthermore, by employing an efficient voltage sweeping method, we can efficiently pinpoint these charge state transition lines and filter out erroneous points. Exper imental evaluation using real quantum dot chip datasets demon strates a substantial 5.84× to 19.34× speedup over conventional methods, thereby showcasing promising prospects for accelerating the scaling of silicon spin qubit devices.