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committedJan 29, 2024
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‎docs/source/tutorials/barren_plateaus_cn.ipynb

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" dtype=\"float32\",\n",
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")\n",
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"\n",
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"e, grad = op_expectation_vmap_vvag(params, seed, n, nlayers) # 不同随机电路的 ZZ 可观测量和梯度的期望\n",
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"e, grad = op_expectation_vmap_vvag(\n",
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" params, seed, n, nlayers\n",
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") # 不同随机电路的 ZZ 可观测量和梯度的期望\n",
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"\n",
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"grad_var = tf.math.reduce_std(tf.math.reduce_std(grad, axis=0), axis=0)[\n",
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" 0, 0\n",

‎docs/source/tutorials/nnvqe_cn.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"def MERA(inp, n, d=1, lamb=1.0, energy_flag=False): # 对于单变量一维XXZ模型,我们固定lamb\n",
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"def MERA(\n",
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" inp, n, d=1, lamb=1.0, energy_flag=False\n",
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"): # 对于单变量一维XXZ模型,我们固定lamb\n",
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" params = K.cast(inp[\"params\"], \"complex128\")\n",
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" delta = K.cast(inp[\"delta\"], \"complex128\")\n",
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" c = tc.Circuit(n)\n",

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