LIMDD: A Decision Diagram for Simulation of Quantum Computing Including Stabilizer States

Lieuwe Vinkhuijzen1, Tim Coopmans1,2, David Elkouss2,3, Vedran Dunjko1, and Alfons Laarman1

1Leiden University, The Netherlands
2Delft University of Technology, The Netherlands
3Networked Quantum Devices Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan

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Abstract

Efficient methods for the representation and simulation of quantum states and quantum operations are crucial for the optimization of quantum circuits. Decision diagrams (DDs), a well-studied data structure originally used to represent Boolean functions, have proven capable of capturing relevant aspects of quantum systems, but their limits are not well understood. In this work, we investigate and bridge the gap between existing DD-based structures and the stabilizer formalism, an important tool for simulating quantum circuits in the tractable regime. We first show that although DDs were suggested to succinctly represent important quantum states, they actually require exponential space for certain stabilizer states. To remedy this, we introduce a more powerful decision diagram variant, called Local Invertible Map-DD (LIMDD). We prove that the set of quantum states represented by poly-sized LIMDDs strictly contains the union of stabilizer states and other decision diagram variants. Finally, there exist circuits which LIMDDs can efficiently simulate, while their output states cannot be succinctly represented by two state-of-the-art simulation paradigms: the stabilizer decomposition techniques for Clifford + $T$ circuits and Matrix-Product States. By uniting two successful approaches, LIMDDs thus pave the way for fundamentally more powerful solutions for simulation and analysis of quantum computing.

Classical simulation of a quantum circuit is a computationally difficult task. In a straightforward approach, the memory requirements for storing a description of a quantum state grow as $2^n$ for an $n$-qubit circuit. Decision diagrams address this problem by providing a compressed representation of a quantum state. However, the limits of DD-based methods were not well understood. In this work, we investigate and bridge the gap between existing DD-based structures and the stabilizer formalism, another important tool for simulating quantum circuits. We first show that although DDs were suggested to succinctly represent important quantum states, they actually require exponential space for certain stabilizer states. To remedy this, we introduce a more powerful decision diagram variant, called Local Invertible Map-DD (LIMDD). We prove that there are quantum circuits which can be efficiently analyzed by LIMDDs, but not by existing DD-based methods, nor stabilizer decomposition techniques, nor matrix product states. By leveraging the strengths of both DD and the stabilizer formalism in a strictly more succinct data structure, LIMDDs thus pave the way for fundamentally more powerful simulation and analysis of quantum computing.

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Cited by

[1] Dimitrios Thanos, Tim Coopmans, and Alfons Laarman, Lecture Notes in Computer Science 14216, 199 (2023) ISBN:978-3-031-45331-1.

[2] Wang Fang and Mingsheng Ying, "Symbolic Execution for Quantum Error Correction Programs", Proceedings of the ACM on Programming Languages 8 PLDI, 1040 (2024).

[3] Nadish de Silva, Ming Yin, and Sergii Strelchuk, "Bases for optimising stabiliser decompositions of quantum states", arXiv:2311.17384, (2023).

[4] Dimitrios Thanos, Tim Coopmans, and Alfons Laarman, "Fast equivalence checking of quantum circuits of Clifford gates", arXiv:2308.01206, (2023).

[5] Wang Fang and Mingsheng Ying, "Symbolic Execution for Quantum Error Correction Programs", arXiv:2311.11313, (2023).

[6] Robert Wille, Stefan Hillmich, and Lukas Burgholzer, "Tools for Quantum Computing Based on Decision Diagrams", arXiv:2108.07027, (2021).

[7] Hoa T. Nguyen, Prabhakar Krishnan, Dilip Krishnaswamy, Muhammad Usman, and Rajkumar Buyya, "Quantum Cloud Computing: A Review, Open Problems, and Future Directions", arXiv:2404.11420, (2024).

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