Current Trends in Quantum Emulation and Homomorphic Encryption

Recent developments in quantum emulation and homomorphic encryption (HE) have shown significant advancements, particularly in optimizing memory usage and computational efficiency. In quantum emulation, there is a growing focus on developing memory-efficient methods to support large-scale quantum systems, leveraging techniques like gate fusion and novel matrix storage methods. These innovations aim to enhance the scalability and performance of quantum emulators on FPGA platforms, enabling faster and more efficient quantum circuit simulations.

In the realm of homomorphic encryption, the challenge of evaluating complex functions homomorphically has been addressed through novel algorithms and hardware accelerators. These advancements are crucial for privacy-preserving machine learning and secure data processing. The introduction of multi-modal HE accelerators that support various encryption schemes within a unified architecture represents a notable stride in hardware efficiency and performance. Additionally, the development of more efficient ciphertext multiplication techniques has reduced computational complexity and improved the practicality of HE in real-world applications.

Noteworthy Papers

• Quantum Emulation: The proposed Efficient-Memory Matrix Storage (EMMS) method and its integration into a Quantum Emulator Accelerator (QEA) architecture demonstrate significant performance improvements, particularly in handling larger quantum circuits.
• Homomorphic Encryption: The Trinity accelerator stands out for its unified architecture supporting multiple HE schemes, significantly outperforming existing solutions in both performance and hardware efficiency.
• Ciphertext Multiplication: The introduction of three-input ciphertext multiplication significantly reduces latency and computational overhead, enhancing the practicality of HE in complex computations.