REVIEW 2 cited by
Efficiently manipulating Pauli strings with PauliArray
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Pauli matrices and Pauli strings are widely used in quantum computing. These mathematical objects are useful to describe or manipulate the quantum state of qubits. They offer a convenient basis to express operators and observables used in different problem instances such as molecular simulation and combinatorial optimization. Therefore, it is important to have a well-rounded, versatile and efficient tool to handle a large number of Pauli strings and operators expressed in this basis. This is the objective behind the development of the PauliArray library presented in this work. This library introduces data structures to represent arrays of Pauli strings and operators as well as various methods to modify and combine them. Built using NumPy, PauliArray offers fast operations and the ability to use broadcasting to easily carry out otherwise cumbersome manipulations. Applications to the fermion-to-qubit mapping, to the estimation of expectation values and to the computation of commutators are considered to illustrate how PauliArray can simplify some relevant tasks and accomplish them faster than current libraries.
Forward citations
Cited by 2 Pith papers
-
A High-Performance Pauli-Algebra Framework for Large-Scale Quantum Simulations
BinSim accelerates Pauli multiplication, Hamiltonian construction, and operator–state products via binary symplectic encoding and bit-flip grouping, enabling large-active-space VQE and dynamics on multicore CPUs and GPUs.
-
HattriQ: Designing Integrated Gradients for Feature Attribution in Quantum Machine Learning
HattriQ computes input-feature attributions for amplitude-encoded quantum classifiers by estimating amplitude gradients with Hadamard-test circuits and integrating them from a baseline image.
Discussion (0). Continue with ORCID to comment.