CFLOBDDs: Context-Free-Language Ordered Binary Decision Diagrams

Author:

Sistla Meghana Aparna1ORCID,Chaudhuri Swarat1ORCID,Reps Thomas2ORCID

Affiliation:

1. The University of Texas at Austin, Austin, USA

2. University of Wisconsin-Madison, Madison, USA

Abstract

This article 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 are useful for representing certain classes of functions, matrices, graphs, relations, and so forth 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. We applied CFLOBDDs in quantum-circuit simulation and found that for several standard problems, the improvement in scalability, compared to BDDs, is quite dramatic. With a 15-minute timeout, the number of qubits that CFLOBDDs can handle are 65,536 for Greenberger-Horne-Zellinger, 524,288 for Bernstein-Vazirani, 4,194,304 for Deutsch-Jozsa, and 4,096 for Grover’s algorithm, besting BDDs by factors of 128×, 1,024×, 8,192×, and 128×, respectively.

Funder

Office of Naval Research

National Science Foundation

Missile Defense Agency

Facebook

Publisher

Association for Computing Machinery (ACM)

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Symbolic Execution for Quantum Error Correction Programs;Proceedings of the ACM on Programming Languages;2024-06-20

2. Template Decision Diagrams for Meta Control and Explainability;Communications in Computer and Information Science;2024

3. Simulating Quantum Circuits by Model Counting;Lecture Notes in Computer Science;2024

4. QReach: A Reachability Analysis Tool for Quantum Markov Chains;Lecture Notes in Computer Science;2024

5. Approximate Relational Reasoning for Quantum Programs;Lecture Notes in Computer Science;2024

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