We introduce ${\rm C{\small LEVER}}$, a high-quality, manually curated benchmark of 161 problems for end-to-end verified code generation in Lean. Each problem consists of (1) the task of generating a specification that matches a held-out ground-truth specification, and (2) the task of generating a Lean implementation that provably satisfies this specification. Unlike prior benchmarks,${\rm C{\small LEVER}}$ avoids test-case supervision, LLM-generated annotations, and specifications that leak implementation logic or allow vacuous solutions. All outputs are verified post-hoc using Lean's type checker to ensure machine-checkable correctness. We use ${\rm C{\small LEVER}}$ to evaluate several few-shot and agentic approaches based on state-of-the-art language models. These methods all struggle to achieve full verification, establishing it as a challenging frontier benchmark for program synthesis and formal reasoning. Our benchmark can be found on [GitHub](https://github.com/trishullab/clever) as well as [HuggingFace](https://huggingface.co/datasets/amitayusht/clever). All our evaluation code is also available [online](https://github.com/trishullab/clever-prover).

This paper presents a new data structure, called Weighted Context-Free-Language Ordered BDDs (WCFLOBDDs), which are a hierarchically structured decision diagram, akin to Weighted BDDs (WBDDs) enhanced with a procedure-call mechanism. For some functions, WCFLOBDDs are exponentially more succinct than WBDDs. They are potentially beneficial for representing functions of type B n → D , when a function’s image V ⊆ D has many different values. We apply WCFLOBDDs in quantum-circuit simulation, and find that they perform better than WBDDs on certain benchmarks. With a 15-minute timeout, the number of qubits that can be handled by WCFLOBDDs is 1 − 64 × that of WBDDs (and 1 − 128 × that of CFLOBDDs, which are an unweighted version of WCFLOBDDs). These results support the conclusion that for this application—from the standpoint of problem size, measured as the number of qubits—WCFLOBDDs provide the best of both worlds: performance roughly matches whichever of WBDDs and CFLOBDDs is better. (From the standpoint of running time, the results are more nuanced.)

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.

Abstract The simulation of quantum circuits on classical computers is an important problem in quantum computing. Such simulation requires representations of distributions over very large sets of basis vectors, and recent work has used symbolic data-structures such as Binary Decision Diagrams (BDDs) for this purpose. In this tool paper, we present Quasimodo, an extensible, open-source Python library for symbolic simulation of quantum circuits. Quasimodo is specifically designed for easy extensibility to other backends. Quasimodo allows simulations of quantum circuits, checking properties of the outputs of quantum circuits, and debugging quantum circuits. It also allows the user to choose from among several symbolic data-structures—both unweighted and weighted BDDs, and a recent structure called Context-Free-Language Ordered Binary Decision Diagrams (CFLOBDDs)—and can be easily extended to support other symbolic data-structures.