Parallelizing the stabilizer formalism for quantum machine learning applications
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910830364196864 |
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| author | Hai, Vu Tuan Duong, Le Vu Trung Luan, Pham Hoai Nakashima, Yasuhiko |
| author_facet | Hai, Vu Tuan Duong, Le Vu Trung Luan, Pham Hoai Nakashima, Yasuhiko |
| contents | The quantum machine learning model is emerging as a new model that merges quantum computing and machine learning. Simulating very deep quantum machine learning models requires a lot of resources, increasing exponentially based on the number of qubits and polynomially based on the depth value. Almost all related works use state-vector-based simulators due to their parallelization and scalability. Extended stabilizer formalism simulators solve the same problem with fewer computations because they act on stabilizers rather than long vectors. However, the gate application sequential property leads to less popularity and poor performance. In this work, we parallelize the process, making it feasible to deploy on multi-core devices. The results show that the proposal implementation on Python is faster than Qiskit, the current fastest simulator, 4.23 times in the case of 4-qubits, 60,2K gates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parallelizing the stabilizer formalism for quantum machine learning applications Hai, Vu Tuan Duong, Le Vu Trung Luan, Pham Hoai Nakashima, Yasuhiko Quantum Physics The quantum machine learning model is emerging as a new model that merges quantum computing and machine learning. Simulating very deep quantum machine learning models requires a lot of resources, increasing exponentially based on the number of qubits and polynomially based on the depth value. Almost all related works use state-vector-based simulators due to their parallelization and scalability. Extended stabilizer formalism simulators solve the same problem with fewer computations because they act on stabilizers rather than long vectors. However, the gate application sequential property leads to less popularity and poor performance. In this work, we parallelize the process, making it feasible to deploy on multi-core devices. The results show that the proposal implementation on Python is faster than Qiskit, the current fastest simulator, 4.23 times in the case of 4-qubits, 60,2K gates. |
| title | Parallelizing the stabilizer formalism for quantum machine learning applications |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2502.10685 |