Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Optimization
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916110592376832 |
|---|---|
| author | He, Zichang Shaydulin, Ruslan Chakrabarti, Shouvanik Herman, Dylan Li, Changhao Sun, Yue Pistoia, Marco |
| author_facet | He, Zichang Shaydulin, Ruslan Chakrabarti, Shouvanik Herman, Dylan Li, Changhao Sun, Yue Pistoia, Marco |
| contents | Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent the lessons from the adiabatic regime apply to QAOA as executed in practice with small to moderate depth. In this paper, we demonstrate that the intuition from the adiabatic algorithm applies to the task of choosing the QAOA initial state. Specifically, we observe that the best performance is obtained when the initial state of QAOA is set to be the ground state of the mixing Hamiltonian, as required by the adiabatic algorithm. We provide numerical evidence using the examples of constrained portfolio optimization problems with both low ($p\leq 3$) and high ($p = 100$) QAOA depth. Additionally, we successfully apply QAOA with XY mixer to portfolio optimization on a trapped-ion quantum processor using 32 qubits and discuss our findings in near-term experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_03857 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Optimization He, Zichang Shaydulin, Ruslan Chakrabarti, Shouvanik Herman, Dylan Li, Changhao Sun, Yue Pistoia, Marco Quantum Physics Emerging Technologies Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent the lessons from the adiabatic regime apply to QAOA as executed in practice with small to moderate depth. In this paper, we demonstrate that the intuition from the adiabatic algorithm applies to the task of choosing the QAOA initial state. Specifically, we observe that the best performance is obtained when the initial state of QAOA is set to be the ground state of the mixing Hamiltonian, as required by the adiabatic algorithm. We provide numerical evidence using the examples of constrained portfolio optimization problems with both low ($p\leq 3$) and high ($p = 100$) QAOA depth. Additionally, we successfully apply QAOA with XY mixer to portfolio optimization on a trapped-ion quantum processor using 32 qubits and discuss our findings in near-term experiments. |
| title | Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Optimization |
| topic | Quantum Physics Emerging Technologies |
| url | https://arxiv.org/abs/2305.03857 |