CQCL qbm_benchmark_dataset issues

CQCL / qbm_benchmark_dataset

A simple python package to benchmark Quantum Boltzmann Machine models using Stochastic Gradient Descent. Based on the quantum many-body physics package `quimb`

Apache License 2.0

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Del old files

#28 panasonic-sakata-ryuji closed 3 months ago
0
Analysis notebooks for report

#27 erinaldiq closed 6 months ago
2
set learning_rate by the number of terms in the model Hamiltonian

#26 panasonic-sakata-ryuji closed 7 months ago
1
QRE calculations with the correct partition function

#25 erinaldiq closed 7 months ago
0
QRE calculation is wrong

#24 erinaldiq closed 7 months ago
0
Implement classical Hamiltonian

#23 erinaldiq closed 7 months ago
0
Implement a classical Hamiltonian

#22 panasonic-sakata-ryuji closed 7 months ago
1
change output file names with pretraining

#21 panasonic-sakata-ryuji closed 7 months ago
0
add output files

#20 panasonic-sakata-ryuji closed 7 months ago
0
Can we make the geometrically local 2D Hamiltonian available on non-square lattices

#19 erinaldiq opened 7 months ago
2
Allow for target Gibbs states built from our list of Hamiltonians

#18 erinaldiq closed 7 months ago
3
Allow to train for a different target Gibbs state

#17 erinaldiq closed 7 months ago
1
Update benchmark.py to support pretraining

#16 panasonic-sakata-ryuji closed 8 months ago
6
Pretraining in benchmark.py

#15 erinaldiq closed 8 months ago
1
Gaussian shot noise in gradient

#14 yutakikuchiQ closed 9 months ago
0
Pretraining

#13 erinaldiq closed 8 months ago
0
Add the mean field (single Pauli ops) hamiltonian and implement the analytical optimal parameters

#12 erinaldiq opened 9 months ago
0
Implement shot noise on the gradients

#11 erinaldiq closed 9 months ago
6
XXZ target state notebook

#10 erinaldiq closed 9 months ago
0
update hamiltonians.py

#9 panasonic-sakata-ryuji closed 9 months ago
0
Notebook describing the benchmark script

#8 erinaldiq closed 10 months ago
1
Implement pre-training for a subset of the QBM parameters in `training.py`

#7 erinaldiq closed 8 months ago
0
Geometrically local and fully connected hamiltonians in `hamiltonians.py`

#6 erinaldiq closed 9 months ago
1
update data.py, benchmark.py, training.py

#5 panasonic-sakata-ryuji closed 10 months ago
1
expectation values function in `data.py`

#4 erinaldiq closed 10 months ago
1
Update traning.py

#3 yutakikuchiQ closed 11 months ago
1
Yuta dev

#2 yutakikuchiQ closed 11 months ago
1
Panasonic notebooks added

#1 erinaldiq closed 1 year ago
0