Quantum and Probabilistic Computers Rigorously Powerful than Traditional Computers, and Derandomization

In this paper, we extend the techniques used in our previous work to show that there exists a probabilistic Turing machine running within time $O(n^k)$ for all $k\in\mathbb{N}1$ accepting a language $Ld$ which is different from any language in $\mathcal{P}$, and then further to prove that $Ld\in\mathcal{BPP}$, thus separating the complexity class $\mathcal{BPP}$ from the class $\mathcal{P}$ (i.e., $\mathcal{P}\subsetneq\mathcal{BPP}$). Since the complexity class $\mathcal{BQP}$ of $bounded$ $error$ $quantum$ $polynomial$-$time$ contains the complexity class $\mathcal{BPP}$ (i.e., $\mathcal{BPP}\subseteq\mathcal{BQP}$), we thus confirm the widespread-belief conjecture that quantum computers are $rigorously$ $powerful$ than traditional computers (i.e., $\mathcal{P}\subsetneq\mathcal{BQP}$). We further show that (1). $\mathcal{P}\subsetneq\mathcal{RP}$; (2). $\mathcal{P}\subsetneq\text{co-}\mathcal{RP}$; (3). $\mathcal{P}\subsetneq\mathcal{ZPP}$. Previously, whether the above relations hold or not are long-standing open questions in complexity theory. Meanwhile, the result of $\mathcal{P}\subsetneq\mathcal{BPP}$ shows that $randomness$ plays an essential role in probabilistic algorithm design. In particular, we go further to show that: (1). The number of random bits used by any probabilistic algorithm which accepts the language $Ld$ can not be reduced to $O(\log n)$; (2). There exits no efficient (complexity-theoretic) {\em pseudorandom generator} (PRG) $G:{0,1}^{O(\log n)}\rightarrow {0,1}^n$; (3). There exists no quick HSG $H:k(n)\rightarrow n$ such that $k(n)=O(\log n)$.