QC-AAN #8
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QC-AAN #8
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To quickly run the script with minimal epochs/samples: python3 train.py --nb-epochs 2 --latent-size 8 --nb-samples 1000 --qcbm-nb-shots 100 --nb-qubits 8 ./particles.yaml |
TDHTTTT
commented
Feb 26, 2021
| # generate a new batch of noise | ||
| noise = np.random.normal(0, 1, (batch_size, latent_size)) | ||
| # sample from QCBM | ||
| if nb_qubits > 0: |
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mainly this part
where it samples from QCBM
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Also, it addresses the first half of #6 |
TDHTTTT
commented
Feb 26, 2021
| @@ -512,3 +531,8 @@ def _load_data(particle, datafile): | |||
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| discriminator.save_weights('./weights/{0}{1:03d}.hdf5'.format(parse_args.d_pfx, epoch), | |||
| overwrite=True) | |||
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| dis_weights_f = h5py.File('./weights/{0}{1:03d}.hdf5'.format(parse_args.d_pfx, epoch), 'r') | |||
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I basically read the weight from that layer and passed it to QCBM
TDHTTTT
commented
Feb 26, 2021
| @@ -295,7 +297,9 @@ def _load_data(particle, datafile): | |||
| mbd_energy | |||
| ]) | |||
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| fake = Dense(1, activation='sigmoid', name='fakereal_output')(p) | |||
| qcbm_w = Dense(2**nb_qubits, activation='linear', name='qcbm')(p) | |||
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I added another layer to align our QCBM dimenstion
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Now the CaloGAN is able to sample from the QCBM.
Assumption:
Next need to train QCBM from the discriminator.