README updated.

README.md

@@ -10,4 +10,19 @@ pinned: false
 license: cc-by-4.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Structure of the repository
+
+The repository contains the following:
+
+- data: the benchmark data that generates the plots both in the corresponding manuscript and also in the data explorer at: qucos.qchub.ch
+- exp_data: the expectation values of the final evolved wavefunction used to validate the toolchain
+- plot_scripts: the scripts used to generate the various plots displayed at the data explorer: qucos.qchub.ch
+- qasm_files: the qasm files corresponding to the quantum algorithms that are used to profile the various simulators
+- qasm_parser: the translators used to translate the qasm file into the instruction set of the simulator of choice while allowing to choose the HPC compute capability
+- toolchain: the toolchain used to automate the benchmarking i.e., generates the structure to collect benchmarks while also generating the corresponding run files, job script files and scope linker files
+- plots_display_all.ipynb: the notebook used to display plots of benchmarks (mainly extracting the data from 'data' folder as above and integrating them into the files in 'plot_scripts')
+- expectation_plots.ipynb: the notebook used to display plots corresponding to expectation values (mainly extracting the data from 'exp_data' folder and integrating them into the plot files)
+
+# Zenodo repository
+
+The container images used to generate the benchmarks and their usage are shared at the Zenodo repository: https://zenodo.org/records/10376217

qasm_parser/qiskit/qasm_qiskit_parser_2pt0_new.py

@@ -16,8 +16,6 @@ def parse_qasm_to_package_gen(N, mpt, spt, f_precision, input_filename, output_f
         f.write('from time import process_time, perf_counter\n')
         f.write('import numpy as np\n\n')
 
-        f.write('from qiskit.quantum_info import Statevector, shannon_entropy\n')
-
         f.write('def U2(p, l):\n')
         f.write('    mat = ((np.cos(0.5*(p + l)) - 1j*np.sin(0.5*(p + l)))/np.sqrt(2))*np.array([[1, -np.cos(l)-1j*np.sin(l)], [np.cos(p) + 1j*np.sin(p), np.cos(l + p) + 1j*np.sin(l + p)]])\n')
         f.write('    return Operator(mat)\n\n')
@@ -129,17 +127,11 @@ def parse_qasm_to_package_gen(N, mpt, spt, f_precision, input_filename, output_f
         f.write('result = job.result()\n')
         f.write('outputstate = result.get_statevector(cir)\n')
 
-        f.write('probs = Statevector(outputstate).probabilities()\n')
-        f.write('print(sum(probs))\n')
-
-        f.write('ent = shannon_entropy(probs)\n')
-        f.write('print(ent)\n')
-
-        # f.write('t_e = perf_counter()\n')
-        # f.write('t_ep = process_time()\n')
+        f.write('t_e = perf_counter()\n')
+        f.write('t_ep = process_time()\n')
 
-        # f.write('os.chdir(\'{}\')\n'.format(save_data_path))
-        # f.write('np.save(\'time_n{}.npy\', [t_e - t_s, t_ep - t_sp])\n'.format(N))
+        f.write('os.chdir(\'{}\')\n'.format(save_data_path))
+        f.write('np.save(\'time_n{}.npy\', [t_e - t_s, t_ep - t_sp])\n'.format(N))
         # f.write('print(t_e - t_s)\n')
         # f.write('print(t_ep - t_sp)\n')
 
@@ -185,16 +177,12 @@ com_cap = 'st'
 prec = 'sp'
 
 for N in range(6, 40, 2):
-    input_file_path = '/home/amit/Documents/PSI_m/project_files_final/{}_singular/qasm_files/'.format(task)
+    input_file_path = '/data/user/gangap_a/{}_singular/qasm_files/'.format(task)
     input_file = input_file_path + 'qasm_N_{}.qasm'.format(N)
 
-    # save_file_path = '/data/user/gangap_a/{}_singular/{}/data_{}_{}'.format(task, sim_pack, com_cap, prec)
-
-    # output_file_path = '/data/user/gangap_a/{}_singular/{}/run_files_{}_{}/'.format(task, sim_pack, com_cap, prec)
-
-    save_file_path = None
+    save_file_path = '/data/user/gangap_a/{}_singular/{}/data_{}_{}'.format(task, sim_pack, com_cap, prec)
 
-    output_file_path = '/home/amit/Documents/PSI_m/project_files_final/{}_ent/{}/'.format(task, sim_pack)
+    output_file_path = '/data/user/gangap_a/{}_singular/{}/run_files_{}_{}/'.format(task, sim_pack, com_cap, prec)
 
     output_file = output_file_path + '{}_n{}.py'.format(task, N)
     parse_qasm_to_package_gen(N, 1, 40, "single", input_file, output_file, save_file_path)