Despite their widespread use in quantum computing and physics, the relative strengths and weaknesses of Matrix Product States (MPS), Decision Diagrams (DDs), and Restricted Boltzmann Machines (RBMs) remains poorly understood. We analytically compare the succinctness of these quantum state representations and analyze the complexity of key operations on them. To overcome shortcomings of the tractability measure, we introduce `rapidity' conditions that identify when non-canonical representations efficiently simulate each other. Our results reveal that: 1. Most DD variants are redundant with respect to MPS in a strong sense; MPS is more rapid. 2. Only one DD variant, called LIMDD, and RBM have succinctness incomparable to MPS. 3. LIMDD and RBM seem to achieve this by sacrificing tractability of counting queries, as shown by a metatheorem on the conditional hardness of these queries.

Abstract We present , a versatile, open-source Python library for quantum circuit analysis. reduces various simulation, verification, and synthesis tasks to weighted model counting (#SAT). It supports universal quantum circuits and a wide variety of gates. provides multiple encodings based on different algebraic bases and equivalence-checking methods, enabling key performance trade-offs. Moreover, the new version of adds approximate equivalence checking, which is crucial in its synthesis algorithms, since it enables translation between arbitrary gate sets. Its synthesis engine is depth-optimal, making it well-suited to real-world quantum computing. This paper demonstrates the design, extensibility, and use of .

Tensor networks and circuits are widely used data structures to represent pseudo-Boolean functions. These two formalisms have been studied primarily in separate communities, and this paper aims to establish equivalences between them. We show that some classes of tensor networks that are appealing in practice correspond to classes of circuits with specific properties that have been studied in knowledge compilation as tractable circuits. In particular, we prove that matrix product states (tensor trains) coincide with nondeterministic edge-valued decision diagrams and that tree tensor networks exactly correspond to structured-decomposable circuits. These correspondences enable direct transfer of structural and algorithmic results; for example, canonicity and tractability guarantees known for circuits yield analogous guarantees for the associated tensor networks, and vice versa.

We prove the existence of two thresholds regarding the compilability of random 2-CNF formulas to OBDDs. The formulas are drawn from F₂(n,δn), the uniform distribution over all 2-CNFs with δ n clauses and n variables, with δ ≥ 0 a constant. We show that, with high probability, the random 2-CNF admits OBDDs of size polynomial in n if 0 ≤ δ < 1/2 or if δ > 1. On the other hand, for 1/2 < δ < 1, with high probability, the random 2-CNF admits only OBDDs of size exponential in n. It is no coincidence that the two "compilability thresholds" are δ = 1/2 and δ = 1. Both are known thresholds for other CNF properties, namely, δ = 1 is the satisfiability threshold for 2-CNF while δ = 1/2 is the treewidth threshold, i.e., the point where the treewidth of the primal graph jumps from constant to linear in n with high probability.

We study uniformity conditions for parameterized Boolean circuit families. Uniformity conditions require that the infinitely many circuits in a circuit family are in some sense easy to construct from one shared description. For shallow circuit families, logtime-uniformity is often desired but quite technical to prove. Despite that, proving it is often left as an exercise for the reader - even for recently introduced classes in parameterized circuit complexity, where uniformity conditions have not yet been explicitly studied. We formally define parameterized versions of linear-uniformity, logtime-uniformity, and FO-uniformity, and prove that these result in equivalent complexity classes when imposed on para-AC⁰ and para-AC^{0↑}. Overall, we provide a convenient way to verify uniformity for shallow parameterized circuit classes, and thereby substantiate claims of uniformity in the literature.

Abstract Equivalence checking plays a crucial role in quantum circuit compilation, optimization, and verification. Stabilizer circuits can be simulated classically by tracking the so-called stabilizer operators in linear time. But the simulation of large stabilizer circuits with thousands of qubits and gates, arising e.g. in the study of novel quantum error-correction protocols, still poses a challenge. In this work, we propose a GPU-based deterministic algorithm for equivalence checking of stabilizer circuits using the stabilizer tableau formalism. We explore various design choices and implement the most efficient version. Our algorithm significantly outperforms the state-of-the-art CCEC checker (which relies on the Stim simulator) in terms of time, memory, and energy. Our approach demonstrates up to two orders of magnitude speedup over existing methods. Notably, previous attempts at GPU acceleration in this area were unsuccessful, making this the first effective implementation.

Pebble games are used to study space/time trade-offs. Recently, spooky pebble games were introduced to study classical space / quantum space / time trade-offs for simulation of classical circuits on quantum computers. In this paper, the spooky pebble game framework is applied for the first time to general circuits. Using this framework we prove an upper bound for quantum space in the spooky pebble game. We also prove that solving the spooky pebble game is PSPACE-complete. Moreover, we present a solver for the spooky pebble game based on satisfiability solvers combined with heuristic optimizers. This spooky pebble game solver was empirically evaluated by calculating optimal classical space / quantum space / time trade-offs. Within limited runtime, the solver could find a strategy reducing quantum space when classical space is taken into account, showing that the spooky pebble model is useful to reduce quantum space.

Abstract Quantum circuit compilation comprises many computationally hard reasoning tasks that lie inside # $${\textsf{P}}$$ P and its decision counterpart in $${\textsf{PP}}$$ PP . The classical simulation of universal quantum circuits is a core example. We show for the first time that a strong simulation of universal quantum circuits can be efficiently tackled through weighted model counting by providing a linear-length encoding of Clifford+Tcircuits. To achieve this, we exploit the stabilizer formalism by Knill, Gottesmann, and Aaronson by reinterpreting quantum states as a linear combination of stabilizer states. With an open-source simulator implementation, we demonstrate empirically that model counting often outperforms state-of-the-art simulation techniques based on the ZX calculus and decision diagrams. Our work paves the way to apply the existing array of powerful classical reasoning tools to realize efficient quantum circuit compilation; one of the obstacles on the road towards quantum supremacy.

Abstract This tutorial introduces quantum computing with a focus on the applicability of formal methods in this relatively new domain. We describe quantum circuits and convey an understanding of their inherent combinatorial nature and the exponential blow-up that makes them hard to analyze. Then, we show how weighted model counting (#SAT) can be used to solve hard analysis tasks for quantum circuits. This tutorial is aimed at everyone in the formal methods community with an interest in quantum computing. Familiarity with quantum computing is not required, but basic linear algebra knowledge (particularly matrix multiplication and basis vectors) is a prerequisite. The goal of the tutorial is to inspire the community to advance the development of quantum computing with formal methods.

Abstract In this paper, we extend diagrammatic reasoning in monoidal categories with algebraic operations and equations. We achieve this by considering monoidal categories that are enriched in the category of Eilenberg-Moore algebras for a monad. Under the condition that this monad is monoidal and there is an adjunction between the free algebra functor and the underlying category functor, we construct an adjunction between symmetric monoidal categories and symmetric monoidal categories enriched over algebras for the monad. This allows us to devise an extension, and its semantics, of the ZX-calculus with probabilistic choices by freely enriching over convex algebras, which are the algebras of the finite distribution monad. We show how this construction can be used for diagrammatic reasoning of noise in quantum systems.

Abstract Verifying equivalence between two quantum circuits is a hard problem, that is nonetheless crucial in compiling and optimizing quantum algorithms for real-world devices. This paper gives a Turing reduction of the (universal) quantum circuits equivalence problem to weighted model counting (WMC). Our starting point is a folklore theorem showing that equivalence checking of quantum circuits can be done in the so-called Pauli-basis. We combine this insight with a WMC encoding of quantum circuit simulation, which we extend with support for the Toffoli gate. Finally, we prove that the weights computed by the model counter indeed realize the reduction. With an open-source implementation, we demonstrate that this novel approach can outperform a state-of-the-art equivalence-checking tool based on ZX calculus and decision diagrams.

Abstract The 27th International Symposium on Model Checking Software, SPIN 2021, was held online, July 12, 2021. The current special issue contains extended versions of three selected works published at the symposium. This short introduction presents these selected papers and the selection process.

Bayesian networks (BN) are a popular representation for reasoning under uncertainty. The analysis of many real-world use cases, that in principle can be modeled by BNs, suffers however from the computational complexity of inference. Inference methods based on Weighted Model Counting (WMC) reduce the cost of inference by exploiting patterns exhibited by the probabilities associated with BN nodes. However, these methods require a computationally intensive compilation step in search of these patterns, which effectively prohibits the handling of larger BNs. In this paper, we propose a solution to this problem by extending WMC methods with a framework called Compositional Weighted Model Counting (CWMC). CWMC reduces compilation cost by partitioning a BN into a set of subproblems, thereby scaling the application of state-of-the-art innovations in WMC to scenarios where inference cost could previously not be amortized over compilation cost. The framework supports various target representations that are less or equally succinct as decision-DNNF. At the same time, its inference time complexity O(nexp⁡(w)), where n is the number of variables and w is the tree-width, is comparable to mainstream algorithms based on variable elimination, clustering and conditioning.

The main advantages of Tarjan's strongly connected component (SCC) algorithm are its linear time complexity and ability to return SCCs on-the-fly, while traversing or even generating the graph. Until now, most parallel SCC algorithms sacrifice both: they run in quadratic worst-case time and/or require the full graph in advance.