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Trapped Ion Quantum Computing Quantum Machine Learning

Quantum Algorithms for Realizing Symmetric, Asymmetric, and Antisymmetric Projectors

arXiv
Authors: Margarite L. LaBorde, Soorya Rethinasamy, Mark M. Wilde

Year

2024

Paper ID

65006

Status

Preprint

Abstract Read

~2 min

Abstract Words

173

Citations

N/A

Abstract

In quantum computing, knowing the symmetries a given system or state obeys or disobeys is often useful. For example, Hamiltonian symmetries may limit allowed state transitions or simplify learning parameters in machine learning applications, and certain asymmetric quantum states are known to be resourceful in various applications. Symmetry testing algorithms provide a means to identify and quantify these properties with respect to a representation of a group. In this paper, we present a collection of quantum algorithms that realize projections onto the symmetric subspace, as well as the asymmetric subspace, of quantum systems. We describe how this can be modified to realize an antisymmetric projection as well, and we show how projectors can be combined in a systematic way to effectively measure various projections in a single quantum circuit. Using these constructions, we demonstrate applications such as testing for Werner-state symmetry and estimating Schmidt ranks of bipartite states, supported by experimental data from IBM Quantum systems. This work underscores the pivotal role of symmetry in simplifying quantum calculations and advancing quantum information tasks.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • In quantum computing, knowing the symmetries a given system or state obeys or disobeys is often useful.

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