Quantum computing (QC) is a rapidly evolving field that leverages quantum mechanics to perform computations that surpass the capabilities of classical computers. We are now transitioning from the Noisy Intermediate-Scale Quantum (NISQ) era towards the advent of large-scale, fault-tolerant quantum computers. This transition signifies the potential supremacy of quantum computers, which could revolutionize various fields by solving complex problems that are intractable for classical computers.

The full potential of quantum computing isn’t solely dependent on the hardware but also significantly relies on classical software and algorithms. They serve as the backbone of quantum computing, translating high-level quantum programs into low-level machine instructions, optimizing quantum circuits to mitigate the effects of errors, and managing the execution of quantum computations on diverse quantum hardware. As quantum devices continue to scale up, the efficiency and performance of classical algorithms become increasingly important for the practical use of quantum computers.

Our lab focuses on accelerating, optimizing and automating the classical software of quantum computers for various quantum hardware. We aim to integrate the insights from the fields of Electrical Design Automation (EDA) and Machine Learning (ML) to enhance the performance and efficiency of algorithms. Our lab is committed to pushing the boundaries of quantum computing and contributing to the ongoing quantum revolution.

• Quantum Computing
• Design Automation for QC
  
• - Quantum circuit compilation
  
• - Qubit mapping and routing algorithms
  
• Machine Learning for QC
  
• - Quantum machine learning algorithms
  
• - ML-based quantum algorithm optimization