Research Area

QCSS's research focuses on quantum computing systems such as.

• Quantum Software

  • Quantum Circuit Mapping for New Qubit Topology (e.g., multi-QPU, neutral atoms, general model)

• Quantum Computer Systems

  • Quantum Operating System

  • Quantum Process Scheduling, Quantum Intermediate Representation

• Quantum HW-SW Co-Optimization

  • Error Correction and Management

• Integration of Quantum Computing and Machine Learning 

  • Machine Learning for Quantum Computing

  • Quantum Machine Learning (QML)

• Quantum Software

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 (FTQC). 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 Circuit Mapping

To enable quantum computations, a quantum computer must operate the quantum logic gates step-by-step. At that time, a pair of qubits interacting in the quantum logic gate must be physically connected on the quantum hardware. However, modern quantum computers have limitations in operating quantum logic gates; because not every physical qubit is always connected. Therefore, to make the quantum circuit executable on a target quantum hardware, quantum algorithms must be modified; this procedure is known as quantum circuit mapping (i.e., qubit mapping or quantum circuit compilation).

In qubit mapping, additional quantum gates, e.g., SWAP or BRIDGE operations, are used to modify the quantum circuit. However, these extra quantum gates can increase the circuit depth and induce errors. The additional gates have higher error rate than do existing gates [9], so the computation result of the algorithm eventually fails due to accumulation of errors. The increased circuit depth also decreases the algorithmic accuracy due to physical limits in NISQ devices [10]. Therefore, a qubit-mapping algorithm that can effi- ciently reduce the inserted gate count (i.e., ‘overhead’) is required.

• Quantum Machine Learning (QML)

Quantum machine learning is the integration of quantum algorithms within machine learning programs. 

The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning. quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program. These routines can be more complex in nature and executed faster on a quantum computer. Furthermore, quantum algorithms can be used to analyze quantum states instead of classical data.