Datasets:
license: cc-by-sa-4.0
language:
- en
configs:
- config_name: xxz_300_z.seed05
data_files:
- split: train
path: xxz_300_z/seed_05/d_train.parquet
- split: test
path: xxz_300_z/seed_05/d_test.parquet
- config_name: isnnn_400.seed01
data_files:
- split: train
path: isnnn_400/seed_01/d_train.parquet
- split: test
path: isnnn_400/seed_01/d_test.parquet
description: >-
Samples for IsNNN400 statistical manifold generated with seed=1. `sample`
column is formatted as bytearray of length 50 bytes (400 bits) and
represents a spin configuration of 20x20 lattice with bit 0 corresponding
to Z=+1 and bit 1 corresponding to Z=-1.
- config_name: isnnn_400.seed01.gt_fim
data_files:
- split: test
path: isnnn_400/seed_01/gt_fim.parquet
- config_name: isnnn_400.seed02
data_files:
- split: train
path: isnnn_400/seed_02/d_train.parquet
- split: test
path: isnnn_400/seed_02/d_test.parquet
- config_name: isnnn_400.seed02.gt_fim
data_files:
- split: test
path: isnnn_400/seed_02/gt_fim.parquet
- config_name: isnnn_400.seed03
data_files:
- split: train
path: isnnn_400/seed_03/d_train.parquet
- split: test
path: isnnn_400/seed_03/d_test.parquet
- config_name: isnnn_400.seed03.gt_fim
data_files:
- split: test
path: isnnn_400/seed_03/gt_fim.parquet
- config_name: isnnn_400.seed04
data_files:
- split: train
path: isnnn_400/seed_04/d_train.parquet
- split: test
path: isnnn_400/seed_04/d_test.parquet
- config_name: isnnn_400.seed04.gt_fim
data_files:
- split: test
path: isnnn_400/seed_04/gt_fim.parquet
- config_name: isnnn_400.seed05
data_files:
- split: train
path: isnnn_400/seed_05/d_train.parquet
- split: test
path: isnnn_400/seed_05/d_test.parquet
- config_name: isnnn_400.seed05.gt_fim
data_files:
- split: test
path: isnnn_400/seed_05/gt_fim.parquet
- config_name: isnnn_400.seed06
data_files:
- split: train
path: isnnn_400/seed_06/d_train.parquet
- split: test
path: isnnn_400/seed_06/d_test.parquet
- config_name: isnnn_400.seed06.gt_fim
data_files:
- split: test
path: isnnn_400/seed_06/gt_fim.parquet
- config_name: isnnn_400.seed07
data_files:
- split: train
path: isnnn_400/seed_07/d_train.parquet
- split: test
path: isnnn_400/seed_07/d_test.parquet
- config_name: isnnn_400.seed07.gt_fim
data_files:
- split: test
path: isnnn_400/seed_07/gt_fim.parquet
- config_name: isnnn_400.seed08
data_files:
- split: train
path: isnnn_400/seed_08/d_train.parquet
- split: test
path: isnnn_400/seed_08/d_test.parquet
- config_name: isnnn_400.seed08.gt_fim
data_files:
- split: test
path: isnnn_400/seed_08/gt_fim.parquet
- config_name: isnnn_400.seed09
data_files:
- split: train
path: isnnn_400/seed_09/d_train.parquet
- split: test
path: isnnn_400/seed_09/d_test.parquet
- config_name: isnnn_400.seed09.gt_fim
data_files:
- split: test
path: isnnn_400/seed_09/gt_fim.parquet
- config_name: isnnn_400.seed10
data_files:
- split: train
path: isnnn_400/seed_10/d_train.parquet
- split: test
path: isnnn_400/seed_10/d_test.parquet
- config_name: isnnn_400.seed10.gt_fim
data_files:
- split: test
path: isnnn_400/seed_10/gt_fim.parquet
Dataset Card for FIM-Estimation
Dataset Description
Dataset Summary
In a FIM-Estimation task, the input consists of the following components:
- A dataset of the form $\mathcal{D}_ {train} = {(\mathbf{\lambda}_ i, x_ i)}_ {i=1}^{\left|\mathcal{D}\right|}$, where $\vlambda_ i$ is a point in the parameter space and $x_ i$ is a sample.
- The structure of the samples $x_ i$. For example, if $x_ i\in{0,1}^n$ is a bitstring representing a measurement of a quantum system on a lattice with $n$ sites, then the said structure is the lattice and the correspondence between the bitstring bits and the lattice sites.
- Optionally, additional information about the statistical manifold. This could involve, e.g., the symmetries of the system used to generate the statistical manifold.
The task is to estimate Fisher Information Metric (FIM) as a matrix-valued function of parameter $\mathbf{\lambda}$.
In this dataset we present multiple such tasks (parameterised by task name and random seed). E.g. in order to solve the IsNNN400 task with seed=4 one would download config isnnn_400.seed04, split train, estimate the FIM, and compare the results with isnnn_400.seed04.gt_fim.
Additional information
We (TODO:plan to) present datasets corresponding to 6 statistical manifolds: XXZ300_Z, FIL24, etc.
The data is stored as follows:
data/xxz300_z contains the data for XXZ300_Z datasets. Within that directory, there are subdirectories of the form seed_?? each containing a dataset describing the same statistical manifold, but generated using a different seed (e.g. seed_05). We have seed_05/d_train.parquet, which is the dataset as described in #1 above. seed_05/d_test.parquet is a hold-out dataset which should not be used in estimation of $\mathbf{\lambda}$ (not even for hyperparameter tuning).
data/xxz300_z/meta.json (TODO) represents the metadata (including information listed in #2 and #3 above)
data/xxz300_z/fim_ground_truth.json (TODO) represents the ground truth FIM.
An algorithm attempting to solve the FIM-Estimation task XXZ300_Z(seed=05) shall take data/xxz300_z/seed_05/d_train.parquet and data/xxz300_z/meta.json as inputs and produce FIM estimates to be compared with data/xxz300_z/fim_ground_truth.json.