Quantum machine learning with D‐wave quantum computer

Abstract

Summary The new era of artificial intelligence (AI) aims to entangle the relationships among models (characterizations), algorithms, and implementations toward the high‐level intelligence with general cognitive ability, strong robustness, and interpretability, which is intractable for machine learning (ML). Quantum computer provides a new computing paradigm for ML. Although universal quantum computers are still in infancy, special‐purpose D‐Wave machine hopefully becomes the breaking point of commercialized quantum computing. The core principle, quantum annealing (QA), enables the quantum system to naturally evolve toward the low‐energy states. D‐Wave's quantum computer has developed some applications of quantum ML based on quantum‐assisted ML algorithms, quantum Boltzmann machine, etc. Additionally, working with CPUs, quantum processing units is likely to advance ML in a quantum‐inspired way. Thus, a new advanced computing architecture, quantum‐classical hybrid approach consisting of QA, classical computing, and brain‐inspired cognitive science, is required to explore its superiority to universal quantum algorithms and classical ML algorithms. It is important to explore hybrid quantum/classical approaches to overcome the defects of ML such as high dependence on training data, low robustness to the noises, and cognitive impairment. The new framework is expected to gradually form a highly effective, accurate, and adaptive intelligent computing architecture for the next generation of AI.

Quantum annealing distributed integer decomposition study of local field coefficient <italic>h</italic> and coupling coefficient <italic>J</italic> with stability Ising model

“…将待分解整数根据二进制整数分解乘法表进行Is-表 2 143=11×13的二进制乘法表 Table 2 Binary multiplication table for 143=11×13 2 7 2 6 2 5 2 4 2 3 2 2 2 1 2 [18,27] .…”

Recent advances in quantum machine learning

“…The development of quantum computer has made some progress recently. For example, D‐wave special‐purpose quantum computer, which can perform quantum annealing, allows some classical machine learning algorithms to run efficiently . On the other hand, some companies and research institutions have produced actual prototype machines of universal quantum computers based on the quantum circuit model, which make experiments are able to be carried out with quantum computational operations on a small number of qubits via cloud platforms.…”

“…The learning process deal with the quantum data efficiently. 3 On the other hand, some companies and research institutions have produced actual prototype machines of universal quantum computers based on the quantum circuit model, which make experiments are able to be carried out with quantum computational operations on a small number of qubits via cloud platforms. Nowadays, the general large-scale quantum computer is still being developed.…”

Prime factorization algorithm based on parameter optimization of Ising model

“…It has been widely used in sampling, optimization, machine learning, etc. [23][24][25][26][27][28][29] . Raouf Dridi et al 27 applied the computational algebraic geometry to transform the factorization problem to the QUBO model to be solved by the cell algorithm and the column algorithm respectively.…”