9,063 research outputs found
2-D Compass Codes
The compass model on a square lattice provides a natural template for
building subsystem stabilizer codes. The surface code and the Bacon-Shor code
represent two extremes of possible codes depending on how many gauge qubits are
fixed. We explore threshold behavior in this broad class of local codes by
trading locality for asymmetry and gauge degrees of freedom for stabilizer
syndrome information. We analyze these codes with asymmetric and spatially
inhomogeneous Pauli noise in the code capacity and phenomenological models. In
these idealized settings, we observe considerably higher thresholds against
asymmetric noise. At the circuit level, these codes inherit the bare-ancilla
fault-tolerance of the Bacon-Shor code.Comment: 10 pages, 7 figures, added discussion on fault-toleranc
Temporal-mode continuous-variable cluster states using linear optics
I present an extensible experimental design for optical continuous-variable
cluster states of arbitrary size using four offline (vacuum) squeezers and six
beamsplitters. This method has all the advantages of a temporal-mode encoding
[Phys. Rev. Lett. 104, 250503], including finite requirements for coherence and
stability even as the computation length increases indefinitely, with none of
the difficulty of inline squeezing. The extensibility stems from a construction
based on Gaussian projected entangled pair states (GPEPS). The potential for
use of this design within a fully fault tolerant model is discussed.Comment: 9 pages, 19 color figure
Embedding cube-connected cycles graphs into faulty hypercubes
We consider the problem of embedding a cube-connected cycles graph (CCC) into a hypercube with edge faults. Our main result is an algorithm that, given a list of faulty edges, computes an embedding of the CCC that spans all of the nodes and avoids all of the faulty edges. The algorithm has optimal running time and tolerates the maximum number of faults (in a worst-case setting). Because ascend-descend algorithms can be implemented efficiently on a CCC, this embedding enables the implementation of ascend-descend algorithms, such as bitonic sort, on hypercubes with edge faults. We also present a number of related results, including an algorithm for embedding a CCC into a hypercube with edge and node faults and an algorithm for embedding a spanning torus into a hypercube with edge faults
Analysing correlated noise on the surface code using adaptive decoding algorithms
Laboratory hardware is rapidly progressing towards a state where quantum
error-correcting codes can be realised. As such, we must learn how to deal with
the complex nature of the noise that may occur in real physical systems. Single
qubit Pauli errors are commonly used to study the behaviour of error-correcting
codes, but in general we might expect the environment to introduce correlated
errors to a system. Given some knowledge of structures that errors commonly
take, it may be possible to adapt the error-correction procedure to compensate
for this noise, but performing full state tomography on a physical system to
analyse this structure quickly becomes impossible as the size increases beyond
a few qubits. Here we develop and test new methods to analyse blue a particular
class of spatially correlated errors by making use of parametrised families of
decoding algorithms. We demonstrate our method numerically using a diffusive
noise model. We show that information can be learnt about the parameters of the
noise model, and additionally that the logical error rates can be improved. We
conclude by discussing how our method could be utilised in a practical setting
blue and propose extensions of our work to study more general error models.Comment: 19 pages, 8 figures, comments welcome; v2 - minor typos corrected
some references added; v3 - accepted to Quantu
A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems
This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version
Sparse visual models for biologically inspired sensorimotor control
Given the importance of using resources efficiently in the competition for survival, it is reasonable to think that natural evolution has discovered efficient cortical coding strategies for representing natural visual information. Sparse representations have intrinsic advantages in terms of fault-tolerance and low-power consumption potential, and can therefore be attractive for robot sensorimotor control with powerful dispositions for decision-making. Inspired by the mammalian brain and its visual ventral pathway, we present in this paper a hierarchical sparse coding network architecture that extracts visual features for use in sensorimotor control. Testing with natural images demonstrates that this sparse coding facilitates processing and learning in subsequent layers. Previous studies have shown how the responses of complex cells could be sparsely represented by a higher-order neural layer. Here we extend sparse coding in each network layer, showing that detailed modeling of earlier stages in the visual pathway enhances the characteristics of the receptive fields developed in subsequent stages. The yield network is more dynamic with richer and more biologically plausible input and output representation
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