Compilation, Optimization, Error Mitigation, and Machine Learning in Quantum Algorithms
This work addresses compilation and optimization issues for quantum algorithms, potentially benefiting researchers and practitioners in quantum computing, though it appears incremental as it builds on existing quantum Fourier transform methods.
The paper tackles the challenge of executing real-world quantum algorithms by proposing an approximate quantum Fourier transform (AQFT) for optimization, which improves circuit execution on top of the exponential speedups provided by quantum Fourier transform.
This paper discusses the compilation, optimization, and error mitigation of quantum algorithms, essential steps to execute real-world quantum algorithms. Quantum algorithms running on a hybrid platform with QPU and CPU/GPU take advantage of existing high-performance computing power with quantum-enabled exponential speedups. The proposed approximate quantum Fourier transform (AQFT) for quantum algorithm optimization improves the circuit execution on top of an exponential speed-ups the quantum Fourier transform has provided.