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import streamlit as st
import wget, os, io, ast
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.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, _, svr_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 qc,is_svr,ax in zip(qc_list, svr_list, axs.flatten()):
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).
""")
st.divider()
col1, col2 = st.columns(2)
srv = col1.text_input('SRV', "[1,1,1,2,2]")
num_of_qubits = col1.radio('Number of qubits (should match SRV)', [3,4,5,6,7,8], index=2)
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=7.5)
srv_list = ast.literal_eval(srv)
if len(srv_list)!=num_of_qubits:
st.warning('Number of qubits does not match with given SRV. This will 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) |