import streamlit as st import os, io, ast #wget import matplotlib.pyplot as plt from PIL import Image from genQC.pipeline.diffusion_pipeline import DiffusionPipeline from genQC.inference.infer_srv import generate_srv_tensors, convert_tensors_to_srvs from genQC.util import infer_torch_device #-------------------------------- # download model into storage #save_destination = "saves/" #url_config = "https://github.com/FlorianFuerrutter/genQC/blob/044f7da6ebe907bd796d3db293024db223cc1852/saves/qc_unet_config_SRV_3to8_qubit/config.yaml" #url_weights = "https://github.com/FlorianFuerrutter/genQC/blob/044f7da6ebe907bd796d3db293024db223cc1852/saves/qc_unet_config_SRV_3to8_qubit/model.pt" #def download(url, dst_dir): # if not os.path.exists(dst_dir): os.mkdir(dst_dir) # filename = os.path.join(dst_dir, os.path.basename(url)) # if not os.path.exists(filename): filename = wget.download(url + "?raw=true", out=filename) # return filename #config_file = download(url_config, save_destination) #weigths_file = download(url_weights, save_destination) #-------------------------------- # setup @st.cache_resource def load_pipeline(): #pipeline = DiffusionPipeline.from_config_file(save_destination, infer_torch_device()) pipeline = DiffusionPipeline.from_pretrained("Floki00/qc_srv_3to8qubit", "cpu") pipeline.scheduler.set_timesteps(20) return pipeline pipeline = load_pipeline() is_gpu_busy = False def get_qcs(srv, num_of_qubits, max_gates, g): global is_gpu_busy with st.status("Generation started", expanded=True) as status: st.write("Generating tensors...") 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) st.write("Converting to circuits...") qc_list, _, srv_list = convert_tensors_to_srvs(out_tensor, pipeline.gate_pool) st.write("Plotting...") fig, axs = plt.subplots(3, 2, figsize=(7,10), constrained_layout=True, dpi=120) for ax in axs.flatten(): ax.axis('off') ax.text(0.5, 0.5,"Circuit generated with errors") for qc,is_svr,ax in zip(qc_list, srv_list, axs.flatten()): ax.clear() qc.draw("mpl", plot_barriers=False, ax=ax) ax.set_title(f"{'Correct' if is_svr==srv else 'NOT correct'}, is SRV = {is_svr}") status.update(label="Generation complete!", state="complete", expanded=False) # buf = io.BytesIO() # fig.savefig(buf) # buf.seek(0) # return Image.open(buf) return fig #-------------------------------- # run st.title("genQC · Generative Quantum Circuits") st.write(""" Generating quantum circuits with diffusion models. Official demo of [[paper-arxiv]](https://arxiv.org/abs/2311.02041) [[code-repo]](https://github.com/FlorianFuerrutter/genQC). """) col1, col2 = st.columns(2) srv = col1.text_input('SRV', "[1,1,1,2,2,2]") num_of_qubits = col1.radio('Number of qubits (should match SRV)', [3,4,5,6,7,8], index=3) max_gates = col1.select_slider('Max gates', options=[4,8,12,16,20,24,28], value=16) g = col1.slider('Guidance scale', min_value=0.0, max_value=15.0, value=10.0) srv_list = ast.literal_eval(srv) if len(srv_list)!=num_of_qubits: st.warning(f'Number of qubits does not match with given SRV {srv_list}. This could result in error-circuits!', icon="⚠️") if col1.button('Generate circuits'): fig = get_qcs(srv_list, num_of_qubits, max_gates, g) # col2.image(image, use_column_width=True) col2.pyplot(fig)