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TensorCircuit-NG Offers Multiple Methods to Simulate Quantum State Evolution

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Simulating how quantum states evolve over time is a core challenge in quantum computing, requiring trade-offs between memory, accuracy, and computational cost. TensorCircuit-NG supports several distinct approaches, including exact diagonalization, Trotter decomposition, TEBD, and ODE-based integration. Exact diagonalization stores the full Hamiltonian and is efficient for many time points but becomes memory-prohibitive at scale, with a 16-qubit dense Hamiltonian alone requiring around 64 GB. Trotter decomposition avoids building the full Hamiltonian matrix by breaking evolution into small time steps, while TEBD represents quantum states as matrix product states to enable linear memory scaling for low-entanglement one-dimensional systems. ODE-based methods directly integrate the Schrödinger equation and are especially suited for time-dependent Hamiltonians, with step sizes often determined adaptively by the numerical integrator.

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