Quantum State Discrimination Enhanced by FPGA-Based AI Engine Technology
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911371415781376 |
|---|---|
| author | Butko, Anastasiia Marisov, Artem Santiago, David I. Siddiqi, Irfan |
| author_facet | Butko, Anastasiia Marisov, Artem Santiago, David I. Siddiqi, Irfan |
| contents | Identifying the state of a quantum bit (qubit), known as quantum state discrimination, is a crucial operation in quantum computing. However, it has been the most error-prone and time-consuming operation on superconducting quantum processors. Due to stringent timing constraints and algorithmic complexity, most qubit state discrimination methods are executed offline. In this work, we present an enhanced real-time quantum state discrimination system leveraging FPGA-based AI Engine technology. A multi-layer neural network has been developed and implemented on the AMD Xilinx VCK190 FPGA platform, enabling accurate in-situ state discrimination and supporting mid-circuit measurement experiments for multiple qubits. Our approach leverages recent advancements in architecture research and design, utilizing specialized AI/ML accelerators to optimize quantum experiments and reduce the use of FPGA resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08213 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum State Discrimination Enhanced by FPGA-Based AI Engine Technology Butko, Anastasiia Marisov, Artem Santiago, David I. Siddiqi, Irfan Quantum Physics Emerging Technologies Identifying the state of a quantum bit (qubit), known as quantum state discrimination, is a crucial operation in quantum computing. However, it has been the most error-prone and time-consuming operation on superconducting quantum processors. Due to stringent timing constraints and algorithmic complexity, most qubit state discrimination methods are executed offline. In this work, we present an enhanced real-time quantum state discrimination system leveraging FPGA-based AI Engine technology. A multi-layer neural network has been developed and implemented on the AMD Xilinx VCK190 FPGA platform, enabling accurate in-situ state discrimination and supporting mid-circuit measurement experiments for multiple qubits. Our approach leverages recent advancements in architecture research and design, utilizing specialized AI/ML accelerators to optimize quantum experiments and reduce the use of FPGA resources. |
| title | Quantum State Discrimination Enhanced by FPGA-Based AI Engine Technology |
| topic | Quantum Physics Emerging Technologies |
| url | https://arxiv.org/abs/2601.08213 |