Automated detection of symmetry-protected subspaces in quantum
simulations
- URL: http://arxiv.org/abs/2302.08586v3
- Date: Mon, 27 Mar 2023 21:17:02 GMT
- Title: Automated detection of symmetry-protected subspaces in quantum
simulations
- Authors: Caleb Rotello, Eric B. Jones, Peter Graf, Eliot Kapit
- Abstract summary: We introduce two classical algorithms, which efficiently compute and elucidate features of symmetry-protected subspaces.
These algorithms lend themselves to the post-selection of quantum computer data, optimized classical simulation of quantum systems, and the discovery of previously hidden symmetries in quantum mechanical systems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The analysis of symmetry in quantum systems is of utmost theoretical
importance, useful in a variety of applications and experimental settings, and
is difficult to accomplish in general. Symmetries imply conservation laws,
which partition Hilbert space into invariant subspaces of the time-evolution
operator, each of which is demarcated according to its conserved quantity. We
show that, starting from a chosen basis, any invariant, symmetry-protected
subspaces which are diagonal in that basis are discoverable using transitive
closure on graphs representing state-to-state transitions under $k$-local
unitary operations. Importantly, the discovery of these subspaces relies
neither upon the explicit identification of a symmetry operator or its
eigenvalues nor upon the construction of matrices of the full Hilbert space
dimension. We introduce two classical algorithms, which efficiently compute and
elucidate features of these subspaces. The first algorithm explores the entire
symmetry-protected subspace of an initial state in time complexity linear to
the size of the subspace by closing local basis state-to-basis state
transitions. The second algorithm determines, with bounded error, if a given
measurement outcome of a dynamically-generated state is within the
symmetry-protected subspace of the state in which the dynamical system is
initialized. We demonstrate the applicability of these algorithms by performing
post-selection on data generated from emulated noisy quantum simulations of
three different dynamical systems: the Heisenberg-XXX model and the $T_6$ and
$F_4$ quantum cellular automata. Due to their efficient computability and
indifference to identifying the underlying symmetry, these algorithms lend
themselves to the post-selection of quantum computer data, optimized classical
simulation of quantum systems, and the discovery of previously hidden
symmetries in quantum mechanical systems.
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