This paper presents a new compressed representation of Boolean functions, called CFLOBDDs (for Context-Free-Language Ordered Binary Decision Diagrams). They are essentially a plug-compatible alternative to BDDs (Binary Decision Diagrams), and hence useful for representing certain classes of functions, matrices, graphs, relations, etc. in a highly compressed fashion. CFLOBDDs share many of the good properties of BDDs, but--in the best case--the CFLOBDD for a Boolean function can be exponentially smaller than any BDD for that function. Compared with the size of the decision tree for a function, a CFLOBDD--again, in the best case--can give a double-exponential reduction in size. They have the potential to permit applications to (i) execute much faster, and (ii) handle much larger problem instances than has been possible heretofore. CFLOBDDs are a new kind of decision diagram that go beyond BDDs (and their many relatives). The key insight is a new way to reuse sub-decision-diagrams: components of CFLOBDDs are structured hierarchically, so that sub-decision-diagrams can be treated as standalone ''procedures'' and reused. We applied CFLOBDDs to the problem of simulating quantum circuits, and found that for several standard problems the improvement in scalability--compared to simulation using BDDs--is quite dramatic. In particular, the number of qubits that could be handled using CFLOBDDs was larger, compared to BDDs, by a factor of 128x for GHZ; 1,024x for BV; 8,192x for DJ; and 128x for Grover's algorithm. (With a 15-minute timeout, the number of qubits that CFLOBDDs can handle are 65,536 for GHZ, 524,288 for BV; 4,194,304 for DJ; and 4,096 for Grover's Algorithm.)
翻译:本文提出了一种新的布尔函数压缩表示,称为CFLOBDDs(上下文自由语言有序二元决策图)。它们本质上是BDDs(二元决策图)的可插拔替代品,因此对于表示某些类别的函数,矩阵,图形,关系等都非常有用。CFLOBDDs具有BDDs的许多优点,但在最佳情况下,布尔函数的CFLOBDD可以比该函数的任何BDD指数级的更小。与函数的决策树的大小相比,CFLOBDD在最佳情况下可以给出双指数级的大小减少。它们有可能允许应用程序(i)执行速度更快,(ii)处理比以前可能的更大的问题实例。CFLOBDDs是一种超越BDDs(和它们的许多亲戚)的新型决策图。关键的洞察力是一种重用子决策图的新方法:CFLOBDD的组件结构化地分层,以便子决策图可以被视为独立的“过程”并重复使用。我们将CFLOBDDs应用于模拟量子电路的问题时发现,在几个标准问题中,与使用BDDs进行模拟相比,可扩展性的改进非常显着。特别地,采用CFLOBDDs可以处理的量子比特数是GHZ的128倍,BV的1024倍,DJ的8192倍,Grover算法的128倍。(使用15分钟的超时限制,CFLOBDDs可以处理的量子比特数为GHZ的65536个,BV的524288个;DJ的4194304个;Grover算法的4096个。)