Titlebook: Quantum Inspired Intelligent Systems; Nadia Nedjah,Leandro dos Santos Coelho,Luiza de Ma Book 2008 Springer-Verlag Berlin Heidelberg 2008

異教徒

歡呼

Quantum Intelligent Mobile System, A quantum intelligent mobile system (QIMS) is essentially a complex quantum-classical hybrid autonomous system which generally consists of four fundamental components: quantum information processing units (QIPU), multi-sensor system, controller/actuator, and quantum/classical information convertors

大范圍流行

不可接觸

HUMID

Calibration of the , Option Pricing Model using a Quantum-inspired Evolutionary Algorithm,g algorithms. This chapter illustrates how a quantum-inspired evolutionary algorithm (QIEA) using real number encodings can be constructed and examines the utility of the resulting algorithm on an important real-world problem, namely the calibration of an Option Pricing model. The results from the a

多山

micturition

Book 2008nt research field concentrates on studying on quantum computing that is characterized by certain principles of quantum mechanics such as standing waves, interference, quantum bits, coherence, superposition of states, and concept of interference, combined with computational intelligence or soft compu

peptic-ulcer

Quantum-Inspired Evolutionary Algorithm for Numerical Optimization,uantum computing that presents faster convergence time for the benchmark problems. Results show that this algorithm can find better solutions, with less evaluations, when compared with similar algorithms, which greatly reduces the convergence time.

Fresco

Quantum Simulataneous Recurrent Networks for Content Addressable Memory, show that a spatial array of qubits can be trained to perform the CNOT, which with a rotation is a universal quantum gate. This means that a large enough array can in principle be trained to do any calculation.

Expiration

Quantum Associative Pattern Retrieval,ensity α = . = 1, after which a phase transition to a quantum spin glass state implies total amnesia. At these loading factors, however the retrieval quality is poor; admitting only a few percent of errors requires lower memory loading factors, comparable with the classical Hopfield model.