Search for: All records

Classical and quantum technologies have traditionally been viewed as orthogonal, with classical systems being deterministic and quantum systems inherently probabilistic. This distinction hinders the development of a scalable quantum internet even as the global internet continues expanding. We report a classical-decisive quantum internet architecture in which the integration of quantum information into advanced photonic technologies enables efficient entanglement distribution over a commercially deployed fiber network. On-chip precise synchronization between classical headers and quantum payloads enables dynamic routing and networking of high-fidelity entanglement guided by classical light. The quantum states are preserved through real-time error mitigation, relying solely on classical signal readout without disturbing quantum information. These classical-decisive features demonstrate a practical path to a scalable quantum internet using existing network infrastructure and operating systems. 

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.