🤖 AI Summary
Conventional quantum circuit design research over-abstracts implementation constraints, erroneously assuming exclusive reliance on purely digital VLSI methodologies—limiting practical deployment, especially for financial applications such as quantum-accelerated option pricing. Method: This work proposes a mixed-signal quantum circuit framework that synergistically integrates the compactness of analog circuits with the synthesis-friendly nature of digital circuits. It pioneers the integration of industrial-grade VLSI tools—including Synopsys Design Compiler—into quantum circuit synthesis, supported by three novel techniques: quantum-classical co-synthesis, noise-resilient gate mapping, and latency-aware scheduling. Contribution/Results: Evaluated on a 12-qubit option pricing benchmark, the approach reduces gate count from 4,095 to 392, compresses circuit depth from 2,048 to 6, and lowers logical error rate from 25.86% to 1.64%. These results demonstrate that mature VLSI methodologies significantly enhance quantum circuit practicality, scalability, and robustness.
📝 Abstract
Prior studies have largely focused on quantum algorithms, often reducing parallel computing designs to abstract models or overly simplified circuits. This has contributed to the misconception that most applications are feasible only through VLSI circuits and cannot be implemented using quantum circuits. To challenge this view, we present a mixed-signal quantum circuit framework incorporating three novel methods that reduce circuit complexity and improve noise tolerance. In a 12 qubit case study comparing our design with JP Morgan's option pricing circuit, we reduced the gate count from 4095 to 392, depth from 2048 to 6, and error rate from 25.86% to 1.64%. Our design combines analog simplicity with digital flexibility and synthesizability, demonstrating that quantum circuits can effectively leverage classical VLSI techniques, such as those enabled by Synopsys Design Compiler to address current quantum design limitations.