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Quantum Machine Learning
AMARETTO: Enabling Efficient Quantum Algorithm Emulation on Low-Tier FPGAs
arXiv
Authors: Christian Conti, Deborah Volpe, Mariagrazia Graziano, Maurizio Zamboni, Giovanna Turvani
Year
2024
Paper ID
36777
Status
Preprint
Abstract Read
~2 min
Abstract Words
132
Citations
N/A
Abstract
Researchers and industries are increasingly drawn to quantum computing for its computational potential. However, validating new quantum algorithms is challenging due to the limitations of current quantum devices. Software simulators are time and memory-consuming, making hardware emulators an attractive alternative. This article introduces AMARETTO (quAntuM ARchitecture EmulaTion TechnOlogy), designed for quantum computing emulation on low-tier Field-Programmable gate arrays (FPGAs), supporting Clifford+T and rotational gate sets. It simplifies and accelerates the verification of quantum algorithms using a Reduced-Instruction-Set-Computer (RISC)-like structure and efficient handling of sparse quantum gates. A dedicated compiler translates OpenQASM 2.0 into RISC-like instructions. AMARETTO is validated against the Qiskit simulators. Our results show successful emulation of sixteen qubits on a AMD Kria KV260 SoM. This approach rivals other works in emulated qubit capacity on a smaller, more affordable FPGA
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- Researchers and industries are increasingly drawn to quantum computing for its computational potential.
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