Quantum Hamiltonian simulation, fundamental in quantum algorithm design, extends far beyond its foundational roots, powering diverse quantum computing applications. However, optimizing the compilation of quantum Hamiltonian simulation poses significant challenges. Existing approaches fall short in reconciling deterministic and randomized compilation, lack appropriate intermediate representations, and struggle to guarantee correctness. Addressing these challenges, we present MarQSim, a novel compilation framework. MarQSim leverages a Markov chain-based approach, encapsulated in the Hamiltonian Term Transition Graph, adeptly reconciling deterministic and randomized compilation benefits. Furthermore, we formulate a Minimum-Cost Flow model that can tune transition matrices to enforce correctness while accommodating various optimization objectives. Experimental results demonstrate MarQSim’s superiority in generating more efficient quantum circuits for simulating various quantum Hamiltonians while maintaining precision.

The rise of metaverse has driven the burst of 3D Web apps to provide immersive experience across heterogeneous devices. However, loading 3D models in Web apps is usually slow especially on mobile devices with limited computation capability and dynamic network condition, harming the experience of 3D mobile Web apps. In this paper, we propose an approach called 3Dispatcher to reducing the 3D model loading time by adaptively compressing the 3D models. Preliminary results show that 3Dispatcher can speed up the 3D model load by 15%-41% in different scenarios.