Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2022
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2205.04829 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929747875856384 |
|---|---|
| author | Roy, Anurag Saha Pack, Kevin Wittler, Nicolas Machnes, Shai |
| author_facet | Roy, Anurag Saha Pack, Kevin Wittler, Nicolas Machnes, Shai |
| contents | We present a software tool-set which combines the theoretical, optimal control view of quantum devices with the practical operation and characterization tasks required for quantum computing. In the same framework, we perform model-based simulations to create control schemes, calibrate these controls in a closed-loop with the device (or in this demo \textemdash by emulating the experimental process) and finally improve the system model through minimization of the mismatch between simulation and experiment, resulting in a digital twin of the device. The model based simulator is implemented using TensorFlow, for numeric efficiency, scalability and to make use of automatic differentiation, which enables gradient-based optimization for arbitrary models and control schemes. Optimizations are carried out with a collection of state-of-the-art algorithms originated in the field of machine learning. All of this comes with a user-friendly Qiskit interface, which allows end-users to easily simulate their quantum circuits on a high-fidelity differentiable physics simulator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_04829 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Software tool-set for automated quantum system identification and device bring up Roy, Anurag Saha Pack, Kevin Wittler, Nicolas Machnes, Shai Quantum Physics We present a software tool-set which combines the theoretical, optimal control view of quantum devices with the practical operation and characterization tasks required for quantum computing. In the same framework, we perform model-based simulations to create control schemes, calibrate these controls in a closed-loop with the device (or in this demo \textemdash by emulating the experimental process) and finally improve the system model through minimization of the mismatch between simulation and experiment, resulting in a digital twin of the device. The model based simulator is implemented using TensorFlow, for numeric efficiency, scalability and to make use of automatic differentiation, which enables gradient-based optimization for arbitrary models and control schemes. Optimizations are carried out with a collection of state-of-the-art algorithms originated in the field of machine learning. All of this comes with a user-friendly Qiskit interface, which allows end-users to easily simulate their quantum circuits on a high-fidelity differentiable physics simulator. |
| title | Software tool-set for automated quantum system identification and device bring up |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2205.04829 |