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import streamlit as st |
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import wget, os, io, ast |
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import matplotlib.pyplot as plt |
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from PIL import Image |
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from genQC.pipeline.diffusion_pipeline import DiffusionPipeline |
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from genQC.inference.infer_srv import generate_srv_tensors, convert_tensors_to_srvs |
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from genQC.util import infer_torch_device |
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save_destination = "saves/" |
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url_config = "https://github.com/FlorianFuerrutter/genQC/blob/044f7da6ebe907bd796d3db293024db223cc1852/saves/qc_unet_config_SRV_3to8_qubit/config.yaml" |
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url_weights = "https://github.com/FlorianFuerrutter/genQC/blob/044f7da6ebe907bd796d3db293024db223cc1852/saves/qc_unet_config_SRV_3to8_qubit/model.pt" |
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def download(url, dst_dir): |
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if not os.path.exists(dst_dir): os.mkdir(dst_dir) |
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filename = os.path.join(dst_dir, os.path.basename(url)) |
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if not os.path.exists(filename): filename = wget.download(url + "?raw=true", out=filename) |
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return filename |
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config_file = download(url_config, save_destination) |
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weigths_file = download(url_weights, save_destination) |
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@st.cache_resource |
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def load_pipeline(): |
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pipeline = DiffusionPipeline.from_config_file(save_destination, infer_torch_device()) |
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pipeline.scheduler.set_timesteps(20) |
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return pipeline |
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pipeline = load_pipeline() |
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is_gpu_busy = False |
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def get_qcs(srv, num_of_qubits, max_gates, g): |
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global is_gpu_busy |
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with st.status("Generation started", expanded=True) as status: |
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st.write("Generating tensors...") |
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out_tensor = generate_srv_tensors(pipeline, f"Generate SRV: {srv}", samples=6, system_size=num_of_qubits, num_of_qubits=num_of_qubits, max_gates=max_gates, g=g) |
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st.write("Converting to circuits...") |
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qc_list, _, srv_list = convert_tensors_to_srvs(out_tensor, pipeline.gate_pool) |
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st.write("Plotting...") |
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fig, axs = plt.subplots(3, 2, figsize=(7,10), constrained_layout=True, dpi=120) |
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for ax in axs.flatten(): |
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ax.axis('off') |
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ax.text(0.5, 0.5,"Circuit generated with errors") |
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for qc,is_svr,ax in zip(qc_list, srv_list, axs.flatten()): |
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ax.clear() |
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qc.draw("mpl", plot_barriers=False, ax=ax, style="clifford") |
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ax.set_title(f"{'Correct' if is_svr==srv else 'NOT correct'}, is SRV = {is_svr}") |
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status.update(label="Generation complete!", state="complete", expanded=False) |
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return fig |
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st.title("genQC · Generative Quantum Circuits") |
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st.write(""" |
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Generating quantum circuits with diffusion models. Official demo of [[paper-arxiv]](https://arxiv.org/abs/2311.02041) [[code-repo]](https://github.com/FlorianFuerrutter/genQC). |
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""") |
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col1, col2 = st.columns(2) |
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srv = col1.text_input('SRV', "[1,1,1,2,2]") |
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num_of_qubits = col1.radio('Number of qubits (should match SRV)', [3,4,5,6,7,8], index=2) |
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max_gates = col1.select_slider('Max gates', options=[4,8,12,16,20,24,28], value=16) |
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g = col1.slider('Guidance scale', min_value=0.0, max_value=15.0, value=7.5) |
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srv_list = ast.literal_eval(srv) |
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if len(srv_list)!=num_of_qubits: |
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st.warning(f'Number of qubits does not match with given SRV {srv_list}. This could result in error-circuits!', icon="⚠️") |
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if col1.button('Generate circuits'): |
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fig = get_qcs(srv_list, num_of_qubits, max_gates, g) |
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col2.pyplot(fig) |
