Synergistic multi-doping effects on the Li7La3Zr2O12 solid electrolyte for fast lithium ion conduction.

Here, we investigate the doping effects on the lithium ion transport behavior in garnet Li7La3Zr2O12 (LLZO) from the combined experimental and theoretical approach. The concentration of Li ion vacancy generated by the inclusion of aliovalent dopants such as Al(3+) plays a key role in stabilizing the cubic LLZO. However, it is found that the site preference of Al in 24d position hinders the three dimensionally connected Li ion movement when heavily doped according to the structural refinement and the DFT calculations. In this report, we demonstrate that the multi-doping using additional Ta dopants into the Al-doped LLZO shifts the most energetically favorable sites of Al in the crystal structure from 24d to 96 h Li site, thereby providing more open space for Li ion transport. As a result of these synergistic effects, the multi-doped LLZO shows about three times higher ionic conductivity of 6.14 × 10(-4) S cm(-1) than that of the singly-doped LLZO with a much less efforts in stabilizing cubic phases in the synthetic condition

Quantum Neural Network based Distinguisher for Differential Cryptanalysis on Simplified Block Ciphers

Differential cryptanalysis is a block cipher analysis technology that infers a key by using the difference characteristics. Input differences can be distinguished using a good difference characteristic, and this distinguishing task can lead to key recovery. Artificial neural networks are a good solution for distinguishing tasks. For this reason, recently, neural distinguishers have been actively studied. We propose a distinguisher based on a quantum-classical hybrid neural network by utilizing the recently developed quantum neural network. To our knowledge, we are the first attempt to apply quantum neural networks for neural distinguisher. The target ciphers are simplified ciphers (S-DES, S-AES, S-PRESENT-[4]), and a quantum neural distinguisher that classifies the input difference from random data was constructed using the Pennylane library. Finally, we obtained quantum advantages in this work: improved accuracy and reduced number of parameters. Therefore, our work can be used as a quantum neural distinguisher with high reliability for simplified ciphers

Parallel Quantum Addition for Korean Block Cipher

Adversaries using quantum computers can employ new attacks on cryptography that are not possible with classical computers. Grover\u27s search algorithm, a well-known quantum algorithm, can reduce the search complexity of $O(2^n)$ to $\sqrt{2^n}$ for symmetric key cryptography using an $n$-bit key. To apply the Grover search algorithm, the target encryption process must be implemented as a quantum circuit. In this paper, we present optimized quantum circuits for Korean block ciphers based on ARX architectures. We adopt the optimal quantum adder and design in parallel way with only a few trade-offs between quantum resources. As a result, we provide a performance improvement of 78\% in LEA, 85\% in HIGHT, and 70\% in CHAM in terms of circuit depth, respectively. Finally, we estimate the cost of the Grover key search for Korean block ciphers and evaluate the post-quantum security based on the criteria presented by NIST

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

Improved Quantum Analysis of SPECK and LowMC (Full Version)

As the prevalence of quantum computing is growing in leaps and bounds over the past few years, there is an ever-growing need to analyze the symmetric-key ciphers against the upcoming threat. Indeed, we have seen a number of research works dedicated to this. Our work delves into this aspect of block ciphers, with respect to the SPECK family and LowMC family. The SPECK family received two quantum analysis till date (Jang et al., Applied Sciences, 2020; Anand et al., Indocrypt, 2020). We revisit these two works, and present improved benchmarks SPECK (all 10 variants). Our implementations incur lower full depth compared to the previous works. On the other hand, the quantum circuit of LowMC was explored earlier in Jaques et al.\u27s Eurocrypt 2020 paper. However, there is an already known bug in their paper, which we patch. On top of that, we present two versions of LowMC (on L1, L3 and L5 variants) in quantum, both of which incur significantly less full depth than the bug-fixed implementation

Quantum NV Sieve on Grover for Solving Shortest Vector Problem

Quantum computers can efficiently model and solve several challenging problems for classical computers, raising concerns about potential security reductions in cryptography. NIST is already considering potential quantum attacks in the development of post-quantum cryptography by estimating the quantum resources required for such quantum attacks. In this paper, we present quantum circuits for the NV sieve algorithm to solve the Shortest Vector Problem (SVP), which serves as the security foundation for lattice-based cryptography, achieving a quantum speedup of the square root. Although there has been extensive research on the application of quantum algorithms for lattice-based problems at the theoretical level, specific quantum circuit implementations for them have not been presented yet. Notably, this work demonstrates that the required quantum complexity for the SVP in the lattice of rank 70 and dimension 70 is $2^{43}$ (a product of the total gate count and the total depth) with our optimized quantum implementation of the NV sieve algorithm. This complexity is significantly lower than the NIST post-quantum security standard, where level 1 is $2^{157}$, corresponding to the complexity of Grover\u27s key search for AES-128

Quantum Analysis of AES

Quantum computing is considered among the next big leaps in computer science. While a fully functional quantum computer is still in the future, there is an ever-growing need to evaluate the security of the symmetric key ciphers against a potent quantum adversary. Keeping this in mind, our work explores the key recovery attack using the Grover\u27s search on the three variants of AES (-128, -192, -256). In total, we develop a pool of 20 implementations per AES variant (thus totaling in 60), by taking the state-of-the-art advancements in the relevant fields into account. In a nutshell, we present the least Toffoli depth and full depth implementations of AES, thereby improving from Zou et al.\u27s Asiacrypt\u2720 paper by more than 97 percent for each variant of AES. We show that the qubit count - Toffoli depth product is reduced from theirs by more than 86 percent. Furthermore, we analyze the Jaques et al.\u27s Eurocrypt\u2720 implementations in details, fix the bugs (arising from some problem of the quantum computing tool used and not related to their coding) and report corrected benchmarks. To the best of our finding, our work improves from all the previous works (including the Asiacrypt\u2722 paper by Huang and Sun and the Asiacrypt\u2723 paper by Liu et al.) in terms of various quantum circuit complexity metrics (Toffoli depth, full depth, Toffoli/full depth - qubit count product, full depth - gate count product, etc.). Also, our bug-fixing of Jaques et al.\u27s Eurocrypt\u2720 implementations seem to improve from the authors\u27 own bug-fixing, thanks to our architecture consideration. Equipped with the basic AES implementations, we further investigate the prospect of the Grover\u27s search. We also propose three new implementations of the S-box, one new implementation of the MixColumn; as well as five new architecture (one is motivated by the architecture by Jaques et al. in Eurocrypt’20, and the rest four are entirely our innovation). Under the MAXDEPTH constraint (specified by NIST), the circuit depth metrics (Toffoli depth, T-depth and full depth) become crucial factors and parallelization for often becomes necessary. We provide the least depth implementation in this respect, that offers the best performance in terms of metrics for circuit complexity (like, depth-squared - qubit count product, depth - gate count product)

Grover on Caesar and Vigenère Ciphers

Quantum computers can solve or accelerate specific problems that were not possible with classical computers. Grover\u27s search algorithm, a representative quantum algorithm, finds a specific solution from $N$ unsorted data with $O(\sqrt{N})$ queries. This quantum algorithm can be used to recover the key of symmetric cryptography. In this paper, we present a practical quantum attack using Grover\u27s search to recover the key of ciphers ({\tt Caesar} and {\tt Vigenère}). The proposed quantum attack is simulated with quantum programming tools (ProjectQ and Qiskit) provided by IBM. Finally, we minimize the use of quantum resources and recover the key with a high probability