Data compression simulation

Definition.
Data compression of Network GIS refers to the compression of geospatial data within a network GIS so that the volume of data transmitted across the network can be reduced.

This paper examines whether lossy compression can be used effectively in physics simulations as a possible strategy to combat the expected data-movement

Can Compression errors be used to simulate noise on real devices?

The compression errors are not correlated to the data, and hence the errors might be used to further simulate noise on real devices.
The modern noise simulations add errors to perfect simulations.
However, we could further adapt our lossy compression errors to noise models and then build a simulation which models noise naturally.

The Wave Equation

The global low-rank properties of the logarithmic kernel example are normally not present in typical data sets.
To look into the compression behavior of HLRcompress for more realistic data we choose the time dependent wave equation defined by \\(\\frac{\\partial ^2 u}{\\partial t^2} - \\varDelta u = f \\; \\text { in } \\varOmega \\times [0,T], u(x,t) = g \\.

What is data prediction based compression model?

Data-prediction-based compression model.
This model tries to predict each data point as accurately as possible based on its neighborhood in spatial or temporal dimension and then shrinks the data size by some coding algorithm such as:

data quantization and bit-plane truncation

Why do we use data compression techniques?

Specifically, we apply data compression techniques to the quantum state vector during the simulation.
Since we aim to simulate intermediate-scale general quantum circuits, we have to achieve a data compression ratio as high as possible, because the compression ratio is the key to in- creasing the number of qubits in the simulation.

Why is data compression important for quantum circuit simulations?

Quantum circuit simulations are critical for evaluating quantum algorithms and machines.
However, the number of state ampli- tudes required for full simulation increases exponentially with the number of qubits.
In this study, we leverage data compression to re- duce memory requirements, trading computation time and fidelity for memory space.

Logarithmic Kernel

This example is a modified version of the classical 1D model problem for \(\mathcal {H} \)-matrices [10]. The dataset \(D^{\log } = (d^{\log }_{ij})_{i

The Wave Equation

The global low-rank properties of the logarithmic kernel example are normally not present in typical data sets

Turbulent Combustion

Large-scale turbulence

What is a data compression Turing test?

A data compression Turing test is proposed to optimize compressibility while minimizing information loss for the end use of weather and climate forecast data

Many supercomputing centers in the world perform operational weather and climate simulations several times per day 1

What is the compression factor of a CAMS dataset?

Compression factors are between 3× and 60× for most variables, with a geometric mean of 6× when preserving 100% of information

On accepting a 1% information loss, the geometric mean reaches 17×, which is the overall compression factor for the entire CAMS dataset achieved with this method