Studies on Resource-Efficient Quantum Algorithm for Data Representation and Variational Quantum Classifier

다국어 초록 (Multilingual Abstract)

Nowadays, semiconductor technology faces many challenges because of quantum effects such as quantum tunneling. Furthermore, as technology ad-vances, solving optimization problems becomes increasingly critical. However, the computational growth rate of classical computers has begun to decline due to issues such as thermal dissipation and the physical limits of semiconductor chip integration. Consequently, many researchers have turned to quantum computers as an alternative, and related technologies are now evolving at an accelerated pace. However, quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era are constrained in the algorithms they can execute due to significant noise and a limited number of qubits. Crucially, the issue concerning the qubit count is expected to remain largely unchanged even in the Fault-Tolerant Quantum Computing (FTQC) era. Consequently, there is a necessity for resource-efficient quantum algorithms. This has led to substantial interest in quantum-classical hybrid algorithms, where the quantum computer executes only the computations where it offers a significant advantage, and a classical computer handles the remainder.
This thesis consists of four chapter. Chapter 1 introduced the backgrounds of quantum computing and some algorithms for calculation using quantum computers. Chapter 2 explained an algorithm that applied the principle of Grover search algorithm and its mathematical calculation. Chapter 3 suggested Variational Quantum Classifier (VQC) using Mixture of Quantum Experts (MoQE). In this case, quantum algorithm is used as expert of MoQE. Finally, chapter 4 is the integrated conclusion of this thesis and provides directions for future research.

목차 (Table of Contents)

• Chapter 1. General Introduction １
• 1.1 Quantum Computers １
• 1.2 Quantum Algorithms ３
• Chapter 2. Resource-efficient Quantum Algorithm for Data Representation Using Grover’s Algorithm ６
• 2.1 Introduction ６
• 2.2 Background of Grover’s Algorithm ８
• 2.3 Superdense Data Representation １５
• 2.3.1 Calculation of Number of Iterations １５
• 2.3.2 Amplitude Calculation １８
• 2.4 Conclusion ２６
• Chapter 3. Quantum-Classical Hybrid Resource-Efficient Variational Quantum Classifier Using Mixture of Quantum Experts ２８
• 3.1 Introduction ２８
• 3.2 Background of MoQE. ３０
• 3.3 Quantum-classical Hybrid Variational Quantum Classifier to Reduce Hardware Resource ３３
• 3.4 Results and Discussion ３７
• Chapter 4. Conclusion And Future Direction ４５
• 4.1 Conclusion ４５
• 4.2 Future Direction ４７
• References ４８
• 국문초록 ５２