947 research outputs found
Good approximate quantum LDPC codes from spacetime circuit Hamiltonians
We study approximate quantum low-density parity-check (QLDPC) codes, which
are approximate quantum error-correcting codes specified as the ground space of
a frustration-free local Hamiltonian, whose terms do not necessarily commute.
Such codes generalize stabilizer QLDPC codes, which are exact quantum
error-correcting codes with sparse, low-weight stabilizer generators (i.e. each
stabilizer generator acts on a few qubits, and each qubit participates in a few
stabilizer generators). Our investigation is motivated by an important question
in Hamiltonian complexity and quantum coding theory: do stabilizer QLDPC codes
with constant rate, linear distance, and constant-weight stabilizers exist?
We show that obtaining such optimal scaling of parameters (modulo
polylogarithmic corrections) is possible if we go beyond stabilizer codes: we
prove the existence of a family of approximate QLDPC
codes that encode logical qubits into physical
qubits with distance and approximation infidelity
. The code space is
stabilized by a set of 10-local noncommuting projectors, with each physical
qubit only participating in projectors. We
prove the existence of an efficient encoding map, and we show that arbitrary
Pauli errors can be locally detected by circuits of polylogarithmic depth.
Finally, we show that the spectral gap of the code Hamiltonian is
by analyzing a spacetime circuit-to-Hamiltonian
construction for a bitonic sorting network architecture that is spatially local
in dimensions.Comment: 51 pages, 13 figure
Tensor and Matrix Inversions with Applications
Higher order tensor inversion is possible for even order. We have shown that
a tensor group endowed with the Einstein (contracted) product is isomorphic to
the general linear group of degree . With the isomorphic group structures,
we derived new tensor decompositions which we have shown to be related to the
well-known canonical polyadic decomposition and multilinear SVD. Moreover,
within this group structure framework, multilinear systems are derived,
specifically, for solving high dimensional PDEs and large discrete quantum
models. We also address multilinear systems which do not fit the framework in
the least-squares sense, that is, when the tensor has an odd number of modes or
when the tensor has distinct dimensions in each modes. With the notion of
tensor inversion, multilinear systems are solvable. Numerically we solve
multilinear systems using iterative techniques, namely biconjugate gradient and
Jacobi methods in tensor format
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