This paper provides a new (second) way, which is completely different from Shor's algorithm, to show the optimistic potential of a D-Wave quantum computer for deciphering RSA and successfully factoring all integers within 10000. Our method significantly reduced the local field coefficient h and coupling term coefficient J by more than 33% and 26%, respectively, of those of Ising model, which can further improve the stability of qubit chains and improve the upper bound of integer factorization. In addition, our results obtained the best index (20-bit integer (1028171)) of quantum computing for deciphering RSA via the quantum computing software environment provided by D-Wave. Furthermore, Shor's algorithm requires approximately 40 qubits to factor the integer 1028171, which is far beyond the capacity of universal quantum computers. Thus, post quantum cryptography should further consider the potential of the D-Wave quantum computer for deciphering the RSA cryptosystem in future. The majority of scholars think that Shor's algorithm is a unique and powerful quantum algorithm for the cryptanalysis of RSA. Therefore, the current state of the post quantum cryptography (constructing post quantum public key cryptosystems that would be secure against quantum computers) research has exclusively studied the potential threats to Shor's algorithm. The security of the RSA cryptography system is based on the high complexity and security of the integer factorization problem. Shor's algorithm 1 can attack the RSA cryptosystem in polynomial time. There have been many simulations about quantum computers 2 and attempts to implement Shor's algorithm on quantum computing hardware 3-7. Researchers have developed classic emulators based on reconfigurable technology, enabling efficient simulation of various quantum algorithms and circuits, and they have the potential to simulate number of quits than software based simulators 2. Nuclear Magnetic Resonance (NMR) is the technology that we have for the implementation of small quantum computers. Vandersypen et al. 8 and Lu et al. 9 applied Shor's algorithm to factor the integer 15 via NMR and an optical quantum computer, respectively. Enrique et al. implemented a scalable version of Shor's algorithm via the iterative approach to factor 21 10. Based on the characteristics of the Fermat number 11 , Geller et al. used 8 qubits to successfully factor 51 and 85. The real physical realizations of Shor's algorithm cannot breakthrough the scale of factorization beyond 100 for the moment, as shown by principle-of-proof simulations and experiments 12. Actually, the number of qubits for performing Shor's algorithm to factor an n-bit integer still remains approximately 2n qubits 13. Shor's algorithm requires not only a large number of qubits but also a general-purpose quantum computer with high precision. Achieving practical quantum applications will take longer, perhaps much longer, as said by John Martinis, the physicist who leads Google's efforts 14 , and Science 15 commented that it will be years befor...

Human genomics is witnessing an ongoing paradigm shift from a single reference sequence to a pangenome form, but populations of Asian ancestry are underrepresented. Here we present data from the first phase of the Chinese Pangenome Consortium, including a collection of 116 high-quality and haplotype-phased de novo assemblies based on 58 core samples representing 36 minority Chinese ethnic groups. With an average 30.65× high-fidelity long-read sequence coverage, an average contiguity N50 of more than 35.63 megabases and an average total size of 3.01 gigabases, the CPC core assemblies add 189 million base pairs of euchromatic polymorphic sequences and 1,367 protein-coding gene duplications to GRCh38. We identified 15.9 million small variants and 78,072 structural variants, of which 5.9 million small variants and 34,223 structural variants were not reported in a recently released pangenome reference1. The Chinese Pangenome Consortium data demonstrate a remarkable increase in the discovery of novel and missing sequences when individuals are included from underrepresented minority ethnic groups. The missing reference sequences were enriched with archaic-derived alleles and genes that confer essential functions related to keratinization, response to ultraviolet radiation, DNA repair, immunological responses and lifespan, implying great potential for shedding new light on human evolution and recovering missing heritability in complex disease mapping.

In recent years, the urbanization process has brought modernity while also causing key issues, such as traffic congestion and parking conflicts. Therefore, cities need a more intelligent "brain" to form more intelligent and efficient transportation systems. At present, as a type of machine learning, the traditional clustering algorithm still has limitations. K-means algorithm is widely used to solve traffic clustering problems, but it has limitations, such as sensitivity to initial points and poor robustness. Therefore, based on the hybrid architecture of Quantum Annealing (QA) and brain-inspired cognitive computing, this study proposes QA and Brain-Inspired Clustering Algorithm (QABICA) to solve the problem of urban taxi-stand locations. Based on the traffic trajectory data of Xi'an and Chengdu provided by Didi Chuxing, the clustering results of our algorithm and K-means algorithm are compared. We find that the average taxi-stand location bias of the final result based on QABICA is smaller than that based on K-means, and the bias of our algorithm can effectively reduce the tradition K-means bias by approximately 42%, up to approximately 83%, with higher robustness. QA algorithm is able to jump out of the local suboptimal solutions and approach the global optimum, and brain-inspired cognitive computing provides search feedback and direction. Thus, we will further consider applying our algorithm to analyze urban traffic flow, and solve traffic congestion and other key problems in intelligent transportation.

With the slow progress of universal quantum computers, studies on the feasibility of optimization by a dedicated and quantum-annealing-based annealer are important. The quantum principle is expected to utilize the quantum tunneling effects to find the optimal solutions for the exponential-level problems while classical annealing may be affected by the initializations. This study constructs a new Quantum-Inspired Annealing (QIA) framework to explore the potentials of quantum annealing for solving Ising model with comparisons to the classical one. Through various configurations of the 1D Ising model, the new framework can achieve ground state, corresponding to the optimum of classical problems, with higher probability up to 28% versus classical counterpart (22% in case). This condition not only reveals the potential of quantum annealing for solving the Ising-like Hamiltonian, but also contributes to an improved understanding and use of the quantum annealer for various applications in the future.