The computational power of quantum computers poses major challenges to new design tools since representing pure quantum states typically requires exponentially large memory. As shown previously, decision diagrams can reduce these memory requirements by exploiting redundancies. In this work, we demonstrate further reductions by allowing for small inaccuracies in the quantum state representation. Such inaccuracies are legitimate since quantum computers themselves experience gate and measurement errors and since quantum algorithms are somewhat resistant to errors (even without error correction). We develop four dedicated schemes that exploit these observations and effectively approximate quantum states represented by decision diagrams. We empirically show that the proposed schemes reduce the size of decision diagrams by up to several orders of magnitude while controlling the fidelity of approximate quantum state representations.
Loading classical data into quantum registers is one of the most important primitives of quantum computing. While the complexity of preparing a generic quantum state is exponential in the number of qubits, in many practical tasks the state to prepare has a certain structure that allows for faster preparation. In this paper, we consider quantum states that can be efficiently represented by (reduced) decision diagrams, a versatile data structure for the representation and analysis of Boolean functions. We design an algorithm that utilises the structure of decision diagrams to prepare their assoc…
Quantum computing promises to solve some important problems faster than conventional computations ever could. Currently available NISQ devices on which first practical applications are already executed demonstrate the potential -- with future fault-tolerant quantum hardware for more demanding applications on the horizon. Nonetheless, the advantages in computing power come with challenges to be addressed in the design automation and software development community. In particular, non-quantum representations of states and operations, which provide the basis, e.g., for quantum circuit simulation o…
Due to the rapid development of quantum computing, the compact representation of quantum operations based on decision diagrams has been received more and more attraction. Since variable orders have a significant impact on the size of the decision diagram, identifying a good variable order is of paramount importance. In this paper, we integrate linear transformations into an efficient and canonical form of quantum computing: Quantum Multiple-Valued Decision Diagrams (QMDDs) and develop a novel canonical representation, namely linearly transformed QMDDs (LTQMDDs). We design a linear sifting algo…
Variational decision diagrams for quantum-inspired machine learning applications
D-Wave Systems（D-Wave系统）；Zapata Computing Inc.（Zapata计算公司）；Grupo de Superconductividad y Nanotecnología, Departamento de Física, Universidad Nacional de Colombia（超导与纳米技术小组，物理系，哥伦比亚国家大学）
Quantum computing is a hotspot technology for its potential to accelerate specific applications by exploiting quantum parallelism. However, current physical quantum computers are limited to a relatively small scale, simulators based on conventional machines are significantly relied on to perform quantum computing research. The straightforward array-based simulators require a tremendous amount of memory that increases exponentially with respect to the number of qubits. To mitigate such computing resource concerns, decision diagram based simulators were proposed that can efficiently exploit data…
Classically simulating quantum circuits is crucial when developing or testing quantum algorithms. Due to the underlying exponential complexity, efficient data structures are key for performing such simulations. To this end, tensor networks and decision diagrams have independently been developed with differing perspectives, terminologies, and backgrounds in mind. Although this left designers with two complementary data structures for quantum circuit simulation, thus far it remains unclear which one is the better choice for a given use case. In this work, we (1) consider how these techniques app…
Simulating quantum circuits on classical computers is a notoriously hard, yet increasingly important task for the development and testing of quantum algorithms. In order to alleviate this inherent complexity, efficient data structures and methods such as tensor networks and decision diagrams have been proposed. However, their efficiency heavily depends on the order in which the individual computations are performed. For tensor networks the order is defined by so-called contraction plans and a plethora of methods has been developed to determine suitable plans. On the other hand, simulation base…
Quantum computers promise to efficiently solve important problems classical computers never will. However, in order to capitalize on these prospects, a fully automated quantum software stack needs to be developed. This involves a multitude of complex tasks from the classical simulation of quantum circuits, over their compilation to specific devices, to the verification of the circuits to be executed as well as the obtained results. All of these tasks are highly non-trivial and necessitate efficient data structures to tackle the inherent complexity. Starting from rather straight-forward arrays …
In this paper, we study quantum Ordered Binary Decision Diagrams($OBDD$) model; it is a restricted version of read-once quantum branching programs, with respect to "width" complexity. It is known that the maximal gap between deterministic and quantum complexities is exponential. But there are few examples of functions with such a gap. We present a new technique ("reordering") for proving lower bounds and upper bounds for OBDD with an arbitrary order of input variables if we have similar bounds for the natural order. Using this transformation, we construct a total function $REQ$ such that the d…
