🤖 AI Summary
To address the practical deployment challenges of quantum algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices, this work proposes a full-stack execution framework encompassing compilation, optimization, and error mitigation. Methodologically, it introduces an Approximate Quantum Fourier Transform (AQFT) that preserves the quantum advantage of key algorithms—including Shor’s and HHL—while substantially reducing circuit depth and noise sensitivity. The framework integrates lightweight quantum circuit compilation, variational parameter optimization, zero-noise extrapolation, and machine learning–driven error modeling. Experimental evaluation on real quantum hardware demonstrates significant improvements in algorithmic fidelity and scalability. Moreover, the framework enables quantum-classical heterogeneous execution, effectively balancing hardware constraints with computational acceleration requirements. By unifying these techniques into a cohesive pipeline, it delivers a systematic solution toward realizing exponential quantum speedup in practice.
📝 Abstract
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.