A quantum evolution method includes steps of: according to the quantum evolution method, initializing a generation number t=0, and initializing a population Q(t)={q1t, q2t, . . . , qnt}; observing Q(t) and generating P(t)={x 1t, x2t, . . . , xnt}, wherein represents strings comprising 0 or 1 with a length of m; evaluating each xit with an evaluation function, and inputting evaluating results into a fitness function F(t), F(t)={f1t, f2t. . . , fnt}, wherein fit represents a fitness of each individual; selecting an elite group E(t) from P(t) according to the fitness; evolving Q(t) through U(Δθijt); inputting an optimal solution b of P(t) into B(t), wherein if the optimal solution is better than an original optimal solution in B(t), then replacing the original optimal solution; otherwise remaining the original optimal solution; and judging a shutdown condition, if satisfied, outputting the optimal solution; otherwise returning to the step (2) for further evolution. The method can effectively control a quantum evolution direction and improve method stability.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-4. (canceled) 5. A quantum evolution method, comprising steps of: (1) according to the quantum evolution method, initializing a generation number t=0, and initializing a population Q(t)={q1t, q2t, . . . , qnt}, wherein n is a population size, t is the generation number, qit is a No. i individual in a No. t generation, and i ∈[1, n]; defining q i t = [ α i   1 t β i   1 t | α i   2 t β i   2 t | … α im t … β im t | ] , wherein qit comprises m quantum bits, a represents a probability of each of the quantum bits that a state thereof is 0, β represents a probability of each of the quantum bits that the state thereof is 1, and |α|2+|β|2=1; wherein the quantum bits are randomly generated, and satisfy an equation: (αijt, βijt)=(sign(rand[0,1]−0.5)*1/√{square root over (2)}, sign(rand[0,1]−0.5)*1/√{square root over (2)}), wherein αijt represents a probability of a No. j quantum bit of the No. i individual in the No. t generation that a state thereof is 0, and βijt represents a probability of the No. j quantum bit of the No. i individual in the No. t generation that a state thereof is 1; initializing an optimal solution collection B(t), and inputting a string b, which comprises m 0-characters, into B(t) as an initial optimal solution; (2) observing Q(t), and observing all individuals in the No. t generation, wherein for qit, the m quantum bits are all observed for generating a string xit with a length of m, wherein i is a corresponding individual, t is the generation number, and all individuals in the string xit correspond to the quantum bits of qit; if a quantum bit is 0, then 0 is written to a corresponding location in the string xit, and if the quantum bit is 1, the 1 is written to the corresponding location in the string xit; finally generating P(t)={x1t, x2t, . . . , xnt}; (3) evaluating each xit with an evaluation function, and inputting evaluating results into a fitness function F(t), F(t)={f1t, f2t, . . . , fnt}, wherein fit represents a fitness of qit which is the No. i individual in the No. t generation, and n is the population size of the No. t generation; (4) selecting an elite group E(t) from P(t), specifically comprising steps of: (4.1) comparing all the individuals in the No. t generation with a worst individual of the No. t generation which is evaluated by the fitness function in the step (3), constructing {tilde over (f)}it=abs(fit−min(F(t))); (4.2) representing a probability that xit the elite group by a probability function Sit, s i t = f ~ i t / ∑ i = 1 n  f ~ i t , and constructing S(t)={s1t, s2t, . . . , snt}; and (4.3) based on S(t), deciding whether the individuals in P(t) are selected to enter the elite group E(t) by a roulette method, E(t)={e1t, e2t, . . . , ept}, wherein p is a total individual quantity in the elite group; (5) evolving the No. t generation population Q(t) through U  ( Δθ ij t ) = [ cos  ( Δθ ij t ) - sin  ( Δθ ij t ) sin  ( Δθ ij t ) cos  ( Δθ ij t ) ] , so as to obtain a No. t+1 generation population Q(t+1), Δθ ij t = sign  ( α ij t  β ij t )  1 p  ∑ k = 1 p  Δφ ij k , wherein sign(αijtβijt) represents a quadrant location of a current quantum bit, sign  ( α ij t  β ij t ) = { 1 1  st   or   3  r   d   quadrant - 1 2  nd   or   4  th   quadrant , and   1 p  ∑ k = 1 p  Δφ ij k is a phase angle rotation weight of the elite group E(t), so the elite group actively guides evolution of the whole population; a value of Δφijk is selected according to: 1) if the individual qit in the No. t generation enters the elite group, then Δφijk=0; 2) if the individual qt in the No. t generation fails to enter the elite group and xijt=ekjt, then Δφijk=0; 3) if the individual qit in the No. t generation fails to enter the elite group while xijt is in a ‘0’ state and ekjt is in a ‘1’ state, then Δφijk=φ1, wherein φ1 is a rotation value evolving towards the ‘1’ state, so as to increase a probability that xijt evolves from the ‘0’ state to the ‘1’ state; and 4) if the individual qit the No. t generation fails to enter the elite group while xijt is in the ‘1’ state and ekjt is in the ‘0’ state, then Δφijk=φ0 , wherein φ0 is a rotation value evolving towards the ‘0’ state, so as to increase a probability that xijt evolves from the ‘1’ state to the ‘0’ state; wherein xit is the quantum bits of the individual qit in the No. t generation, which is determined in the step (2); and ekt is all individuals of the elite group E(t), which is determined in the step (4), k ∈[1, p], xijt and ekjt respectively represent the No. j quantum bit of xit and ekt in the No. t generation; for controlling an evolution direction so as to uniformly evolve towards the ‘1’ state, introducing a state preference for further weighting, specifically comprising steps of: when the individual qit the No. t generation fails to enter the elite group while xijt is in the ‘0’ state and ekjt is in the ‘1’ state, increasing a value of φ1 so as to increase a probability that xijt evolves from the ‘0’ state to the ‘1’ state; when the individual qit in the No. t generation fails to enter the elite group while xijt is in the ‘1’ state and ekjt is in the ‘0’ state, decreasing a value of φ0 so as to decrease a probability that xijt evolves from the ‘1’ state to the ‘0’ state; in such a manner that total evolution is towards the ‘1’ state; (6) using xit with a highest fitness, which is selected from P(t) by the fitness function F(t) in the step (3), as an optimal solution of the No. t generation; comparing the optimal solution of the No. t generation with an optimal solution b obtained before the No. t generation, wherein if the optimal solution of the No. t generation is better than the optimal solution before the No. t generation, then the optimal solution of the No. t generation is inputted into B(t−1) for replacing b, so as to obtain B(t); otherwise, the original optimal solution b in B(t−1) remains, so as to obtain B(t); and (7) judging a shutdown condition, specifically: when the optimal solution b in the B(t) is not a globally optimal solution, b is a string comprising m 1-characters and the generation number t is lower than a certain limit, executing t=t+1, and returning to the step (2) for further evolution; otherwise, outputting the optimal solution b in the B(t). 6. The quantum evolution method, as recited in claim 5, wherein in the step (3), for evaluating each xit with the evaluation function, all quantum bits of xit are added together, and a result thereof is inputted into F(t) as a fitness fit of xit. 7. The quantum evolution method, as recited in claim 5, wherein in the step (4.3), for deciding whether the individuals in P(t) are selected to enter the elite group E(t) by the roulette method based on S(t), the fitness fit of all the individuals in the No. t generation is calculated, then a fitness sum ∑ i = 1 n  f i t of all the individuals in the No. t generation is calculated, probabilities that the individuals in P(t) enter the elite group E(t) is f i t / ∑ i = 1 n  f i t , p individuals with highest probabilities are selected to enter the elite group E(t). 8. The quantum evolution method, as recited in claim 6, wherein in the step (4.3), for deciding whether the individuals in P(t) are selected to enter the elite group E(t) by the roulette method based on S(t), the fitness fit of all the individuals in the No. t generation is calculated, then a fitness sum ∑ i = 1 n  f i t of all the individuals in the No. t generation is calculated, probabilities that the individuals in P(t) enter the elite group E(t) is f i t / ∑ i = 1 n  f i t , p individuals with highest probabilities are selected to enter the elite group E(t). 9. The quantum evolution method, as recited in claim 5, wherein in the step (6), b in the optimal solution collection B(t) is the optimal solution of the No. t generation, and an updating process thereof is: during initializing, the optimal solution b is the string comprising m 0-characters; when the generation number t=0, the optimal solution obtained through the step (2) and the step (3) is surely better than the initial optimal solution; as a result, replacing the initial optimal solution by the optimal solution, and inputting in the optimal solution collection B(t) as the optimal solution b, so as to obtain a current generation optimal solution collection B(0); when the generation number t=1, repeating the step (2) and the step (3), comparing an obtained optimal solution with the optimal solution in B(0), wherein if the optimal solution when t=1 is better than the optimal solution in B(0), then the optimal solution when t=1 is inputted into B(t) as b, so as to obtain a current generation optimal solution collection B(1); if the optimal solution when t=1 is worse than the optimal solution in B(0), then the original optimal solution b in B(1) remains, so as to obtain B(1); when the generation number is t, comparing the optimal solution of the No. t generation with the optimal solution b in B(t−1), so as to obtain B(t).