Polynomial T-depth quantum solvability of noisy binary linear problem: from quantum-sample preparation to main computation
The noisy binary linear problem (NBLP) is known as a computationally hard problem, and therefore, it offers primitives for post-quantum cryptography. An efficient quantum NBLP algorithm that exhibits a polynomial quantum sample and time complexities has recently been proposed. However, the algorithm...
Main Authors: | Wooyeong Song, Youngrong Lim, Kabgyun Jeong, Jinhyoung Lee, Jung Jun Park, M S Kim, Jeongho Bang |
---|---|
Format: | Article |
Language: | English |
Published: |
IOP Publishing
2022-01-01
|
Series: | New Journal of Physics |
Subjects: | |
Online Access: | https://doi.org/10.1088/1367-2630/ac94ef |
Similar Items
-
Quantum Binary Field Multiplication with Optimized Toffoli Depth and Extension to Quantum Inversion
by: Kyungbae Jang, et al.
Published: (2023-03-01) -
Noisy Quantum Channel Characterization Using Quantum Neural Networks
by: Junyang Song, et al.
Published: (2023-05-01) -
Variational Quantum Optimization of Nonlocality in Noisy Quantum Networks
by: Brian Doolittle, et al.
Published: (2023-01-01) -
The Cost of Improving the Precision of the Variational Quantum Eigensolver for Quantum Chemistry
by: Ivana Miháliková, et al.
Published: (2022-01-01) -
QNet: A Scalable and Noise-Resilient Quantum Neural Network Architecture for Noisy Intermediate-Scale Quantum Computers
by: Mahabubul Alam, et al.
Published: (2022-01-01)