Quantum Artificial Intelligence on Cryptanalysis

With the recent development of quantum computers, various studies on quantum artificial intelligence technology are being conducted. Quantum artificial intelligence can improve performance in terms of accuracy and memory usage compared to deep learning on classical computers. In this work, we proposed an attack technique that recovers keys by learning patterns in cryptographic algorithms by applying quantum artificial intelligence to cryptanalysis. Cryptanalysis was performed in the current practically usable quantum computer environment, and this is the world\u27s first study to the best of our knowledge. As a result, we reduced 70 epochs and reduced the parameters by 19.6%. In addition, higher average BAP (Bit Accuracy Probability) was achieved despite using fewer epochs and parameters. For the same epoch, the method using a quantum neural network achieved a 2.8% higher BAP with fewer parameters. In our approach, quantum advantages in accuracy and memory usage were obtained with quantum neural networks. It is expected that the cryptanalysis proposed in this work will be better utilized if a larger-scale stable quantum computer is developed in the future

Quantum Implementation of AIM: Aiming for Low-Depth

Security vulnerabilities in the symmetric-key primitives of a cipher can undermine the overall security claims of the cipher. With the rapid advancement of quantum computing in recent years, there is an increasing effort to evaluate the security of symmetric-key cryptography against potential quantum attacks. This paper focuses on analyzing the quantum attack resistance of AIM, a symmetric-key primitive used in the AIMer digital signature scheme. We presents the first quantum circuit implementation of AIM and estimates its complexity (such as qubit count, gate count, and circuit depth) with respect to Grover\u27s search algorithm. For Grover\u27s key search, the most important optimization metric is the depth, especially when considering parallel search. Our implementation gathers multiple methods for a low-depth quantum circuit of AIM in order to reduce the Toffoli depth and full depth

Potential Instability and Malfunction of Knee Joints with Vastus Medialis Impairment after Total Knee Arthroplasty

Four pairs of fresh-frozen cadaver knees (eight knees, four male knees) with a mean age of 72 ± 7 years were used for tests involving a customized simulator capable of controlling quadriceps loading conditions. The muscle force distribution of the quadriceps for the normal loading condition was applied on the basis of muscle cross-sectional area data, as previously reported (VM: 31 N; RF/VI: 49 N; VL: 45 N). To simulate vastus medialis (VM) impairment, we set the muscle force for VM in the muscle force distribution of the quadriceps at zero (VM: 0 N; RF/VI: 49 N; VL: 45 N). The joint reaction forces and moments on knee joints that underwent total knee arthroplasty (TKA) did not differ significantly according to VM impairment status for all flexion angles (p > 0.05). Nevertheless, the vectors of internal–external moments mostly showed a tendency for alteration from external to internal due to VM impairment. This tendency was evident in 9 cases in 12 total test pairs (with and without VM impairment). Furthermore, the vectors of the anterior–posterior reaction forces mostly showed a tendency to increase anteriorly due to VM impairment. This tendency was also evident in 9 cases in 12 total test pairs (with and without VM impairment). These results indicate that posterior dislocation of the tibia may be induced if VM impairment occurs after TKA. In conclusion, VM impairment in knee joints undergoing TKA may contribute to posterior dislocation of the tibia by a paradoxical roll-back with enhancements of the anterior joint reaction force and external moment during knee-joint flexion. Our findings may be valuable for understanding the mechanism of potential instability and malfunction due to VM impairment in knee joints after TKA, and may help to optimize clinical/rehabilitation training plans to improve the prognosis (stability and function) of knee joints undergoing TKA