Quantum Circuit Simulation with Fast Tensor Decision Diagram
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916256655867904 |
|---|---|
| author | Zhang, Qirui Saligane, Mehdi Kim, Hun-Seok Blaauw, David Tzimpragos, Georgios Sylvester, Dennis |
| author_facet | Zhang, Qirui Saligane, Mehdi Kim, Hun-Seok Blaauw, David Tzimpragos, Georgios Sylvester, Dennis |
| contents | 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$\times$ over Google's TensorNetwork library on redundancy-rich circuits, and 25$\times$ and 144$\times$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11362 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Circuit Simulation with Fast Tensor Decision Diagram Zhang, Qirui Saligane, Mehdi Kim, Hun-Seok Blaauw, David Tzimpragos, Georgios Sylvester, Dennis Quantum Physics Data Structures and Algorithms Emerging Technologies 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$\times$ over Google's TensorNetwork library on redundancy-rich circuits, and 25$\times$ and 144$\times$ 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. |
| title | Quantum Circuit Simulation with Fast Tensor Decision Diagram |
| topic | Quantum Physics Data Structures and Algorithms Emerging Technologies |
| url | https://arxiv.org/abs/2401.11362 |