836 research outputs found
On the symbol error probability of regular polytopes
An exact expression for the symbol error probability of the four-dimensional
24-cell in Gaussian noise is derived. Corresponding expressions for other
regular convex polytopes are summarized. Numerically stable versions of these
error probabilities are also obtained
The Small Stellated Dodecahedron Code and Friends
We explore a distance-3 homological CSS quantum code, namely the small
stellated dodecahedron code, for dense storage of quantum information and we
compare its performance with the distance-3 surface code. The data and ancilla
qubits of the small stellated dodecahedron code can be located on the edges
resp. vertices of a small stellated dodecahedron, making this code suitable for
3D connectivity. This code encodes 8 logical qubits into 30 physical qubits
(plus 22 ancilla qubits for parity check measurements) as compared to 1 logical
qubit into 9 physical qubits (plus 8 ancilla qubits) for the surface code. We
develop fault-tolerant parity check circuits and a decoder for this code,
allowing us to numerically assess the circuit-based pseudo-threshold.Comment: 19 pages, 14 figures, comments welcome! v2 includes updates which
conforms with the journal versio
Counting faces of randomly-projected polytopes when the projection radically lowers dimension
This paper develops asymptotic methods to count faces of random
high-dimensional polytopes. Beyond its intrinsic interest, our conclusions have
surprising implications - in statistics, probability, information theory, and
signal processing - with potential impacts in practical subjects like medical
imaging and digital communications. Three such implications concern: convex
hulls of Gaussian point clouds, signal recovery from random projections, and
how many gross errors can be efficiently corrected from Gaussian error
correcting codes.Comment: 56 page
Compressed Sensing Using Binary Matrices of Nearly Optimal Dimensions
In this paper, we study the problem of compressed sensing using binary
measurement matrices and -norm minimization (basis pursuit) as the
recovery algorithm. We derive new upper and lower bounds on the number of
measurements to achieve robust sparse recovery with binary matrices. We
establish sufficient conditions for a column-regular binary matrix to satisfy
the robust null space property (RNSP) and show that the associated sufficient
conditions % sparsity bounds for robust sparse recovery obtained using the RNSP
are better by a factor of compared to the
sufficient conditions obtained using the restricted isometry property (RIP).
Next we derive universal \textit{lower} bounds on the number of measurements
that any binary matrix needs to have in order to satisfy the weaker sufficient
condition based on the RNSP and show that bipartite graphs of girth six are
optimal. Then we display two classes of binary matrices, namely parity check
matrices of array codes and Euler squares, which have girth six and are nearly
optimal in the sense of almost satisfying the lower bound. In principle,
randomly generated Gaussian measurement matrices are "order-optimal". So we
compare the phase transition behavior of the basis pursuit formulation using
binary array codes and Gaussian matrices and show that (i) there is essentially
no difference between the phase transition boundaries in the two cases and (ii)
the CPU time of basis pursuit with binary matrices is hundreds of times faster
than with Gaussian matrices and the storage requirements are less. Therefore it
is suggested that binary matrices are a viable alternative to Gaussian matrices
for compressed sensing using basis pursuit. \end{abstract}Comment: 28 pages, 3 figures, 5 table
Golden codes: quantum LDPC codes built from regular tessellations of hyperbolic 4-manifolds
We adapt a construction of Guth and Lubotzky [arXiv:1310.5555] to obtain a
family of quantum LDPC codes with non-vanishing rate and minimum distance
scaling like where is the number of physical qubits. Similarly as
in [arXiv:1310.5555], our homological code family stems from hyperbolic
4-manifolds equipped with tessellations. The main novelty of this work is that
we consider a regular tessellation consisting of hypercubes. We exploit this
strong local structure to design and analyze an efficient decoding algorithm.Comment: 30 pages, 4 figure
Low-Complexity Approaches to Slepian–Wolf Near-Lossless Distributed Data Compression
This paper discusses the Slepian–Wolf problem of distributed near-lossless compression of correlated sources. We introduce practical new tools for communicating at all rates in the achievable region. The technique employs a simple “source-splitting” strategy that does not require common sources of randomness at the encoders and decoders. This approach allows for pipelined encoding and decoding so that the system operates with the complexity of a single user encoder and decoder. Moreover, when this splitting approach is used in conjunction with iterative decoding methods, it produces a significant simplification of the decoding process. We demonstrate this approach for synthetically generated data. Finally, we consider the Slepian–Wolf problem when linear codes are used as syndrome-formers and consider a linear programming relaxation to maximum-likelihood (ML) sequence decoding. We note that the fractional vertices of the relaxed polytope compete with the optimal solution in a manner analogous to that observed when the “min-sum” iterative decoding algorithm is applied. This relaxation exhibits the ML-certificate property: if an integral solution is found, it is the ML solution. For symmetric binary joint distributions, we show that selecting easily constructable “expander”-style low-density parity check codes (LDPCs) as syndrome-formers admits a positive error exponent and therefore provably good performance
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