Quantum Circuit Simulation with Fast Tensor Decision Diagram
CoRR(2024)
摘要
Quantum circuit simulation is a challenging computational problem crucial for
quantum computing research and development. The predominant approaches in this
area center on tensor networks, prized for their better concurrency and less
computation than methods using full quantum vectors and matrices. However, even
with the advantages, array-based tensors can have significant redundancy. We
present a novel open-source framework that harnesses tensor decision diagrams
to eliminate overheads and achieve significant speedups over prior approaches.
On average, it delivers a speedup of 37× over Google's TensorNetwork
library on redundancy-rich circuits, and 25× and 144× over
quantum multi-valued decision diagram and prior tensor decision diagram
implementation, respectively, on Google random quantum circuits. To achieve
this, we introduce a new linear-complexity rank simplification algorithm,
Tetris, and edge-centric data structures for recursive tensor decision diagram
operations. Additionally, we explore the efficacy of tensor network contraction
ordering and optimizations from binary decision diagrams.
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