Quantum computation on defective circuits

Jincheng Zhang

This paper proposes a novel architecture for adaptive quantum circuits leveraging the protective effects of topological defects in superconducting quantum circuits. The core concept involves constructing superconducting circuits incorporating multiple topological defects, wherein the circuit's quantum states can be dynamically reconfigured upon encountering noise, preserving quantum computation accuracy. This approach addresses the critical challenge of noise resilience in quantum computing by exploiting the inherent robustness of topological protection. The design emphasizes adaptability, allowing the circuit to self-correct and maintain fidelity under fluctuating environmental conditions. Mathematical formulations describe the circuit dynamics and the topological protection mechanism. The proposed architecture offers a promising pathway toward robust and scalable quantum computation. ---

François Chapeau‐Blondeau

Alexandru Paler, Austin G. Fowler, Robert Wille

The first large-scale practical quantum computer is within reach. Coming to grips with the strategy and challenges of preparing reliable executions of an arbitrary quantum computation is not difficult. In fact, defects are good.

Daniele Cuomo

Yuan-Fu Yang, Min Sun

With the rapid development of artificial intelligence and autonomous driving technology, the demand for semiconductors is projected to rise substantially. However, the massive expansion of semiconductor manufacturing and the development of new technology will bring many defect wafers. If these defect wafers have not been correctly inspected, the ineffective semiconductor processing on these defect wafers will cause additional impact to our environment, such as excessive carbon dioxide emission and energy consumption. In this paper, we utilize the information processing advantages of quantum computing to promote the defect learning defect review (DLDR). We propose a classical-quantum hybrid algorithm for deep learning on near-term quantum processors. By tuning parameters implemented on it, quantum circuit driven by our framework learns a given DLDR task, include of wafer defect map classification, defect pattern classification, and hotspot detection. In addition, we explore parametrized quantum circuits with different expressibility and entangling capacities. These results can be used to build a future roadmap to develop circuit-based quantum deep learning for semiconductor defect detection.

Joel Basile Varley