Application

from qgear.runner import generate_random_run
for i in range(15, 19):
    outf = generate_random_run(numCX=4, numCirc=1, numQubits=i, backend='nvidia', shots=1024)
saving data as hdf5: /qgear/circ/rcirc_759e5d.gate_list.h5
h5-write : circ_type (1, 2) int32
h5-write : gate_type (1, 12, 3) int32
h5-write : gate_param (1, 12) float32
h5-write : meta.JSON as string (1,) object
closed  hdf5: /qgear/circ/rcirc_759e5d.gate_list.h5  size=0.01 MB, elaT=0.0 sec
  closed  yaml: /qgear/meas/rcirc_759e5d_gpu_fp32.yaml  size=0.3 kB   elaT=0.0 sec
saving data as hdf5: /qgear/circ/rcirc_1d8331.gate_list.h5
h5-write : circ_type (1, 2) int32
h5-write : gate_type (1, 12, 3) int32
h5-write : gate_param (1, 12) float32
h5-write : meta.JSON as string (1,) object
closed  hdf5: /qgear/circ/rcirc_1d8331.gate_list.h5  size=0.01 MB, elaT=0.0 sec
  closed  yaml: /qgear/meas/rcirc_1d8331_gpu_fp32.yaml  size=0.3 kB   elaT=0.0 sec
saving data as hdf5: /qgear/circ/rcirc_895368.gate_list.h5
h5-write : circ_type (1, 2) int32
h5-write : gate_type (1, 12, 3) int32
h5-write : gate_param (1, 12) float32
h5-write : meta.JSON as string (1,) object
closed  hdf5: /qgear/circ/rcirc_895368.gate_list.h5  size=0.01 MB, elaT=0.0 sec
  closed  yaml: /qgear/meas/rcirc_895368_gpu_fp32.yaml  size=0.3 kB   elaT=0.0 sec
saving data as hdf5: /qgear/circ/rcirc_2f136e.gate_list.h5
h5-write : circ_type (1, 2) int32
h5-write : gate_type (1, 12, 3) int32
h5-write : gate_param (1, 12) float32
h5-write : meta.JSON as string (1,) object
closed  hdf5: /qgear/circ/rcirc_2f136e.gate_list.h5  size=0.01 MB, elaT=0.0 sec
  closed  yaml: /qgear/meas/rcirc_2f136e_gpu_fp32.yaml  size=0.3 kB   elaT=0.0 sec
from qgear.plotter import metrics_plot

# Only specify the date folder(s)
metrics_plot()
  read  yaml: /qgear/meas/rcirc_1e488a_gpu_fp32.yaml
  read  yaml: /qgear/meas/rcirc_2f136e_gpu_fp32.yaml
  read  yaml: /qgear/meas/rcirc_759e5d_gpu_fp32.yaml
  read  yaml: /qgear/meas/rcirc_895368_gpu_fp32.yaml
  read  yaml: /qgear/meas/rcirc_1cd31f_gpu_fp32.yaml
  read  yaml: /qgear/meas/rcirc_c66957_gpu_fp32.yaml
  read  yaml: /qgear/meas/rcirc_1d8331_gpu_fp32.yaml
  read  yaml: /qgear/meas/rcirc_970685_gpu_fp32.yaml
PlotterBackbone: Running in Jupyter → using inline backend
MetricsPlotter : Graphics started
Graphics saving to  out/metrics_f1.png

from qgear.image import create_img
from qgear.runner import run_qcrank
from qgear.plotter import process_qcrank_experiment

# Step 1: Create input
create_img(tag="b2", inp_path=None, out_path="out")

# Step 2: Run simulation
run_qcrank("canImg_b2_32_32", inp_path="out", out_path="out", exp_name="canImg_b2_32_32")

# Step 3: Post-process and plot
process_qcrank_experiment("canImg_b2_32_32", inp_path="out", out_path="out", show_plots="abc")
Loading built-in image 'high-heels_x32_y32.png' from qgear/data/
Metadata: {'image_name': 'high-heels_x32_y32', 'image_shape_xy': [32, 32], 'image_pixels': 1024, 'canned_type': 'gray_image'}
Normalized image min/max: -1.0, 1.0
Flattened image shape: (1024,), reshaped: (2, 512)
saving data as hdf5: out/canImg_b2_32_32.qcrank_inp.h5
h5-write : phys_image (32, 32) uint8
h5-write : image_name as string (1,) object
h5-write : norm_image (32, 32) float32
h5-write : inp_udata (1, 2, 512) float32
h5-write : inp_fdata (512, 2, 1) float32
h5-write : meta.JSON as string (1,) object
closed  hdf5: out/canImg_b2_32_32.qcrank_inp.h5  size=0.02 MB, elaT=0.0 sec
Saved QCrank input: out/canImg_b2_32_32.qcrank_inp.h5
read data from hdf5: out/canImg_b2_32_32.qcrank_inp.h5
read str: image_name 1 <class 'numpy.ndarray'>
read obj: inp_fdata (512, 2, 1) float32
read obj: inp_udata (1, 2, 512) float32
read str: meta.JSON 1 <class 'numpy.ndarray'>
read obj: norm_image (32, 32) float32
read obj: phys_image (32, 32) uint8
 done h5, num rec:5  elaT=0.0 sec
{'canned': {'canned_type': 'gray_image',
            'image_name': 'high-heels_x32_y32',
            'image_pixels': 1024,
            'image_shape_xy': [32, 32]},
 'payload': {'nq_addr': 9,
             'nq_fdata': 2,
             'num_clbit': 11,
             'num_sample': 1,
             'qcrank_max_fval': 3.141592653589793,
             'seq_len': 512},
 'short_name': 'canImg_b2_32_32',
 'submit': {'num_shots': 204800}}
M: using backend=nvidia, total shots=204800
M: circuit has 11 qubits
circ_orig depth_aziz: {'cx': 512, '2q': 512, '3q': 0, '4q+': 2, '1q': 514} , ops: {'ry': 1024, 'cx': 1024, 'measure': 11, 'h': 9, 'barrier': 2, 'qubits': 11}
/usr/local/lib/python3.10/dist-packages/qiskit/compiler/transpiler.py:269: UserWarning: Providing `coupling_map` and/or `basis_gates` along with `backend` is not recommended, as this will invalidate the backend's gate durations and error rates.
  pm = generate_preset_pass_manager(
circ_transpMeta:
{'1q_gate_count': 1033,
 '2q_gate_count': 1024,
 '2q_gate_depth': 512,
 'num_qubit': 11,
 'phys_qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
 'transpile_backend': 'nvidia'}
M: execution-ready 1 circuits on 11 qubits on nvidia
qiskit_to_gateList: nGate 2070
saving data as hdf5: out/canImg_b2_32_32.gate_list.h5
h5-write : circ_type (1, 2) int32
h5-write : gate_type (1, 2070, 3) int32
h5-write : gate_param (1, 2070, 3) float32
h5-write : meta.JSON as string (1,) object
closed  hdf5: out/canImg_b2_32_32.gate_list.h5  size=0.06 MB, elaT=0.0 sec
read data from hdf5: out/canImg_b2_32_32.gate_list.h5
read obj: circ_type (1, 2) int32
read obj: gate_param (1, 2070, 3) float32
read obj: gate_type (1, 2070, 3) int32
read str: meta.JSON 1 <class 'numpy.ndarray'>
 done h5, num rec:3  elaT=0.0 sec
M: job canImg_b2_32_32 started, nCirc=1, nq=11, shots/circ=400, target=nvidia
RCQ: done 901 nvidia, elapsed 1.32s
Job QA: {'status': 'JobStatus.DONE', 'num_circ': 1, 'num_clbits': 11, 'device': 'GPU', 'method': 'statevector', 'noise': 'ideal', 'shots': 204800, 'time_taken': 1.3186101913452148}
saving data as hdf5: out/canImg_b2_32_32.h5
h5-write : image_name (1,) object
h5-write : inp_fdata (512, 2, 1) float32
h5-write : inp_udata (1, 2, 512) float32
h5-write : norm_image (32, 32) float32
h5-write : phys_image (32, 32) uint8
h5-write : raw_nkey (1,) int32
h5-write : raw_ikey (1, 901) int32
h5-write : raw_mshot (1, 901) int32
h5-write : meta.JSON as string (1,) object
closed  hdf5: out/canImg_b2_32_32.h5  size=0.03 MB, elaT=0.0 sec
read data from hdf5: out/canImg_b2_32_32.h5
read str: image_name 1 <class 'numpy.ndarray'>
read obj: inp_fdata (512, 2, 1) float32
read obj: inp_udata (1, 2, 512) float32
read str: meta.JSON 1 <class 'numpy.ndarray'>
read obj: norm_image (32, 32) float32
read obj: phys_image (32, 32) uint8
read obj: raw_ikey (1, 901) int32
read obj: raw_mshot (1, 901) int32
read obj: raw_nkey (1,) int32
 done h5, num rec:8  elaT=0.0 sec
rec_udata: (1, 2, 512) addrBitsL: [2, 3, 4, 5, 6, 7, 8, 9, 10]
ic=0 marginal ibit=0 done, elaT=0.0 min
ic=0 marginal ibit=1 done, elaT=0.0 min
saving data as hdf5: out/canImg_b2_32_32.post.h5
h5-write : image_name (1,) object
h5-write : inp_fdata (512, 2, 1) float32
h5-write : inp_udata (1, 2, 512) float32
h5-write : norm_image (32, 32) float32
h5-write : phys_image (32, 32) uint8
h5-write : raw_ikey (1, 901) int32
h5-write : raw_mshot (1, 901) int32
h5-write : raw_nkey (1,) int32
h5-write : rec_udata (1, 2, 512) float64
h5-write : rec_norm_image (32, 32) float64
h5-write : meta.JSON as string (1,) object
closed  hdf5: out/canImg_b2_32_32.post.h5  size=0.04 MB, elaT=0.0 sec
PlotterBackbone: Running in Jupyter → using inline backend
Plotter : Graphics started
plotted  high-heels_x32_y32
Graphics saving to  out/canImg_b2_32_32_f1.png
Graphics saving to  out/canImg_b2_32_32_f2.png
Graphics saving to  out/canImg_b2_32_32_f3.png

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         ...,
  
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         [[0.   ],
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          167, 166, 165, 165, 164, 164, 163, 162, 162, 161, 161, 160, 160,
          159, 157, 156, 156, 154, 152, 151, 151, 150, 150, 150, 147, 146,
          146, 146, 143, 141, 141, 140, 140, 139, 139, 139, 138, 138, 137,
          137, 135, 134, 133, 133, 132, 132, 131, 130, 130, 130, 129, 128,
          128, 127, 127, 126, 125, 125, 124, 124, 122, 121, 121, 120, 120,
          120, 120, 119, 119, 119, 118, 118, 117, 116, 116, 116, 115, 115,
          115, 115, 114, 113, 113, 112, 110, 110, 109, 108, 108, 106, 106,
          106, 105, 104, 103, 103, 103, 103, 102, 102, 102, 101, 100,  99,
           98,  98,  98,  97,  97,  97,  96,  95,  95,  94,  94,  93,  93,
           93,  93,  92,  90,  90,  90,  90,  89,  89,  89,  88,  86,  83,
           83,  80,  79,  79,  79,  79,  78,  78,  78,  78,  78,  78,  75,
           75,  75,  73,  73,  73,  72,  72,  72,  71,  71,  71,  70,  70,
           69,  69,  69,  69,  68,  68,  68,  67,  67,  67,  66,  66,  66,
           66,  65,  65,  65,  65,  64,  64,  64,  61,  61,  60,  60,  60,
           59,  58,  58,  58,  57,  56,  56,  54,  54,  54,  54,  53,  53,
           53,  52,  52,  52,  51,  51,  51,  51,  50,  48,  48,  47,  47,
           46,  46,  46,  45,  44,  43,  43,  43,  42,  42,  42,  41,  41,
           40,  40,  39,  37,  37,  35,  35,  34,  34,  33,  33,  32,  32,
           30,  30,  29,  28,  28,  27,  25,  25,  24,  23,  22,  22,  22,
           21,  21,  20,  19,  19,  18,  18,  17,  17,  15,  15,  14,  14,
           12,  10,   9,   8,   8,   8,   8,   8,   8,   7,   7,   6,   6,
            6,   6,   6,   6,   6,   6,   6,   5,   5,   5,   5,   5,   5,
            5,   5,   5,   5,   5,   5,   5,   4,   4,   4,   4,   4,   4,
            4,   4,   4,   4,   4,   4,   3,   3,   3,   3,   3,   3,   3,
            3,   3,   3,   3,   3,   3,   3,   3,   3,   3,   2,   2,   2,
            2,   2,   2,   2,   2,   2,   2,   2,   2,   2,   2,   2,   2,
            2,   2,   2,   2,   2,   2,   2,   2,   1,   1,   1,   1,   1,
            1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
            1,   1,   1,   1]], dtype=int32),
  'raw_nkey': array([901], dtype=int32),
  'rec_udata': array([[[ 1.   ,  1.   ,  1.   , ..., -0.505,  0.653,  1.   ],
          [ 1.   ,  1.   ,  1.   , ...,  1.   ,  1.   ,  1.   ]]],
        shape=(1, 2, 512)),
  'rec_norm_image': array([[1.  , 1.  , 1.  , ..., 1.  , 1.  , 1.  ],
         [1.  , 1.  , 1.  , ..., 1.  , 1.  , 1.  ],
         [1.  , 1.  , 1.  , ..., 1.  , 1.  , 1.  ],
         ...,
         [1.  , 1.  , 0.98, ..., 1.  , 1.  , 1.  ],
         [1.  , 1.  , 1.  , ..., 1.  , 1.  , 1.  ],
         [1.  , 1.  , 1.  , ..., 1.  , 1.  , 1.  ]], shape=(32, 32)),
  'meta.JSON': '{"payload": {"num_sample": 1, "nq_addr": 9, "seq_len": 512, "nq_fdata": 2, "num_clbit": 11, "qcrank_max_fval": 3.141592653589793, "num_qubit": 11}, "canned": {"image_name": "high-heels_x32_y32", "image_shape_xy": [32, 32], "image_pixels": 1024, "canned_type": "gray_image"}, "short_name": "canImg_b2_32_32", "submit": {"num_shots": 204800, "backend": "nvidia", "date": "20250827_202026_PDT", "unix_time": 1756351226}, "transpile": {"num_qubit": 11, "phys_qubits": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "transpile_backend": "nvidia", "2q_gate_depth": 512, "1q_gate_count": 1033, "2q_gate_count": 1024}, "hash": "b2575e", "job_qa": {"status": "JobStatus.DONE", "num_circ": 1, "num_clbits": 11, "device": "GPU", "method": "statevector", "noise": "ideal", "shots": 204800, "time_taken": 1.3186101913452148}}'},
 {'payload': {'num_sample': 1,
   'nq_addr': 9,
   'seq_len': 512,
   'nq_fdata': 2,
   'num_clbit': 11,
   'qcrank_max_fval': 3.141592653589793,
   'num_qubit': 11},
  'canned': {'image_name': 'high-heels_x32_y32',
   'image_shape_xy': [32, 32],
   'image_pixels': 1024,
   'canned_type': 'gray_image'},
  'short_name': 'canImg_b2_32_32',
  'submit': {'num_shots': 204800,
   'backend': 'nvidia',
   'date': '20250827_202026_PDT',
   'unix_time': 1756351226},
  'transpile': {'num_qubit': 11,
   'phys_qubits': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
   'transpile_backend': 'nvidia',
   '2q_gate_depth': 512,
   '1q_gate_count': 1033,
   '2q_gate_count': 1024},
  'hash': 'b2575e',
  'job_qa': {'status': 'JobStatus.DONE',
   'num_circ': 1,
   'num_clbits': 11,
   'device': 'GPU',
   'method': 'statevector',
   'noise': 'ideal',
   'shots': 204800,
   'time_taken': 1.3186101913452148},
  'plot': {'resid_max_range': 0.4}})