Session

DescriptionThis session explores new ideas in quantum computing, focusing on qubit routing, qubit readout, reducing errors, error correction, checking the equivalence of quantum circuits, and using quantum machine learning. The first paper aims to solve the qubit routing problem through a novel heuristic algorithm. Quantum readout error is the most significant source of error; therefore, the second, third, and fourth papers present qubit readout architecture leveraging lightweight neural networks, scalable high-fidelity three-level readout, and a software-hardware co-design approach that mitigates readout errors with an embedded accelerator. The fifth paper presents a fast-Ising model-based approach for efficient quantum error correction. The sixth paper discusses a transformative framework for quantum circuit equivalence checking using ZX calculus-based graph abstractions. The seventh and eighth papers focus on quantum machine learning (QML) through amplitude embedding and a framework for efficient and high-accuracy training and inference on heterogeneous quantum processing units.