Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware
The Sachdev–Ye–Kitaev (SYK) model, known for its strong quantum correlations and chaotic behavior, serves as a key platform for quantum gravity studies. However, variationally preparing thermal states on near-term quantum processors for large systems ( N > 12, where N is the number of Majorana f...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ade361 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849422044873097216 |
|---|---|
| author | Akash Kundu |
| author_facet | Akash Kundu |
| author_sort | Akash Kundu |
| collection | DOAJ |
| description | The Sachdev–Ye–Kitaev (SYK) model, known for its strong quantum correlations and chaotic behavior, serves as a key platform for quantum gravity studies. However, variationally preparing thermal states on near-term quantum processors for large systems ( N > 12, where N is the number of Majorana fermions) presents a significant challenge due to the rapid growth in the complexity of parameterized quantum circuits. This paper addresses this challenge by integrating reinforcement learning (RL) with convolutional neural networks, employing an iterative approach to optimize the quantum circuit and its parameters. The refinement process is guided by a composite reward signal derived from entropy and the expectation values of the SYK Hamiltonian. This approach reduces the number of controlled-NOT gates by two orders of magnitude for systems $N\unicode{x2A7E}12$ compared to traditional methods like first-order Trotterization. We demonstrate the effectiveness of the RL framework in both noiseless and noisy quantum hardware environments, maintaining high accuracy in thermal state preparation. This work advances a scalable, RL-based framework with applications for quantum gravity studies and out-of-time-ordered thermal correlators computation in quantum many-body systems on near-term quantum hardware. The code is available at https://github.com/Aqasch/solving_SYK_model_with_RL repository. |
| format | Article |
| id | doaj-art-c7bb75c37a92474dbf657fa9b3591420 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-c7bb75c37a92474dbf657fa9b35914202025-08-20T03:31:15ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016202506610.1088/2632-2153/ade361Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardwareAkash Kundu0https://orcid.org/0000-0002-3540-1061QTF Centre of Excellence, Department of Physics, University of Helsinki , Helsinki, FinlandThe Sachdev–Ye–Kitaev (SYK) model, known for its strong quantum correlations and chaotic behavior, serves as a key platform for quantum gravity studies. However, variationally preparing thermal states on near-term quantum processors for large systems ( N > 12, where N is the number of Majorana fermions) presents a significant challenge due to the rapid growth in the complexity of parameterized quantum circuits. This paper addresses this challenge by integrating reinforcement learning (RL) with convolutional neural networks, employing an iterative approach to optimize the quantum circuit and its parameters. The refinement process is guided by a composite reward signal derived from entropy and the expectation values of the SYK Hamiltonian. This approach reduces the number of controlled-NOT gates by two orders of magnitude for systems $N\unicode{x2A7E}12$ compared to traditional methods like first-order Trotterization. We demonstrate the effectiveness of the RL framework in both noiseless and noisy quantum hardware environments, maintaining high accuracy in thermal state preparation. This work advances a scalable, RL-based framework with applications for quantum gravity studies and out-of-time-ordered thermal correlators computation in quantum many-body systems on near-term quantum hardware. The code is available at https://github.com/Aqasch/solving_SYK_model_with_RL repository.https://doi.org/10.1088/2632-2153/ade361SYK modelreinforcement learningconvoulational neural networkquantum gravityhigher energy physics |
| spellingShingle | Akash Kundu Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware Machine Learning: Science and Technology SYK model reinforcement learning convoulational neural network quantum gravity higher energy physics |
| title | Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware |
| title_full | Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware |
| title_fullStr | Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware |
| title_full_unstemmed | Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware |
| title_short | Improving thermal state preparation of Sachdev–Ye–Kitaev model with reinforcement learning on quantum hardware |
| title_sort | improving thermal state preparation of sachdev ye kitaev model with reinforcement learning on quantum hardware |
| topic | SYK model reinforcement learning convoulational neural network quantum gravity higher energy physics |
| url | https://doi.org/10.1088/2632-2153/ade361 |
| work_keys_str_mv | AT akashkundu improvingthermalstatepreparationofsachdevyekitaevmodelwithreinforcementlearningonquantumhardware |