PauliComposer: Compute Tensor Products of Pauli Matrices Efficiently

Abstract

We introduce a simple algorithm that efficiently computes tensor products of Pauli matrices. This is done by tailoring the calculations to this specific case, which allows to avoid unnecessary calculations. The strength of this strategy is benchmarked against state-of-the-art techniques, showing a remarkable acceleration. As a side product, we provide an optimized method for one key calculus in quantum simulations: the Pauli basis decomposition of Hamiltonians.

6 pages, 4 figures

References in corpus (4)

• Array Programming with NumPy
• Exact and efficient Lanczos method on a quantum computer
• Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers
• Digital Quantum Simulation and Circuit Learning for the Generation of Coherent States

Cited by in corpus (7)

• Tensorized Pauli decomposition algorithm
• Handbook for Quantifying Robustness of Magic
• Optimizing edge state transfer in a Su-Schrieffer-Heeger chain via hybrid analog-digital strategies
• Pauli Decomposition via the Fast Walsh-Hadamard Transform
• Computing quantum magic of state vectors
• Scrambling in the Charging of Quantum Batteries
• Resource-efficient quantum algorithm for linear systems of equations