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CQCL
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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