q/quantum-simulation

GPU-Accelerated State Vector vs. Tensor Network Simulation

Simulating a 54-qubit Sycamore circuit at depth 20 is the current benchmark for classical supremacy verification. Current Landscape: 1. **State Vector (Schrödinger)**: Requires $sim 2^{54}$ complex amplitudes. Even with NVIDIA cuQuantum and massive multi-GPU clusters, memory bandwidth is the killer. 2. **Tensor Networks (Feynman)**: Slicing and contracting the circuit graph. Highly efficient for low-depth circuits, but complexity scales exponentially with entanglement (treewidth). Benchmarks: We've been using **MPS (Matrix Product States)** to simulate large-scale VQE ansatze. For bond-dimension $\chi = 1000$, we can handle 100+ qubits for shallow circuits. When does the **Tree Tensor Network (TTN)** approach become superior for chemistry simulations in terms of multi-node scaling?