Using the Anticrossing Detector | Conductor Quantum

Input Format

The detector expects a visualization-ready tensor shaped (2, n_freqs, n_sweeps):

Axis	Index	Dimension	Description
Channel	`data[0]`	—	Magnitude (dB)
Channel	`data[1]`	—	Phase (degrees)
Rows	`data[c, :, :]`	`n_freqs`	Frequency bins (y-axis when plotted)
Columns	`data[c, :, :]`	`n_sweeps`	Sweep voltages (x-axis when plotted)

This is the same layout you’d pass to imshow() or pcolormesh() — frequency on the vertical axis, sweep voltage on the horizontal axis.

Coming from acquisition-format data? If your data is stored as (n_sweeps, n_freqs) — one frequency trace per sweep point — transpose before calling the API. This matches the typical visualization convention with sweep voltage on the x-axis and frequency on the y-axis:

1 # If mags.shape == (n_sweeps, n_freqs):
2 api_input = np.stack([mags.T, phases.T], axis=0)  # → (2, n_freqs, n_sweeps)

Output Format

All indices are returned as [freq_idx, sweep_idx] pairs — matching [row, col] in your input array:

Key	Type	Description
`frequency_fit_indices`	`list[list[int]]`	Fitted resonance position at each sweep: `[[freq_idx, sweep_idx], ...]`
`anticrossing_indices`	`list[list[int]]`	Detected anticrossing centers
`peak_indices`	`list[list[int]]`	Local frequency maxima near anticrossings
`valley_indices`	`list[list[int]]`	Local frequency minima near anticrossings
`score`	`float`	Quality score (0–1, higher = stronger anticrossing signal)

Usage Example

Python

1 import os
2 import numpy as np
3 from dotenv import load_dotenv
4 from conductorquantum import ConductorQuantum
5 
6 load_dotenv()
7 client = ConductorQuantum(token=os.getenv("CONDUCTOR_API_KEY"))
8 
9 # Load your measurement data: shape (2, n_freqs, n_sweeps)
10 data = np.load("path/to/your/data.npy")
11 magnitudes = data[0]  # shape: (n_freqs, n_sweeps)
12 phases = data[1]
13 
14 results = client.models.execute(
15     model="anticrossing-detector-v0",
16     data=data,
17 )
18 
19 print(f"Results output: {results.output}")
20 output = results.output
21 frequency_fit_indices = np.asarray(output["frequency_fit_indices"])
22 anticrossing_indices = np.asarray(output["anticrossing_indices"])
23 peak_indices = np.asarray(output["peak_indices"])
24 valley_indices = np.asarray(output["valley_indices"])

Sample output

1 Results output: {'frequency_fit_indices': [[392, 0], [392, 1], [393, 2], [391, 3], [394, 4], [390, 5], [391, 6], [391, 7], [393, 8], [392, 9], [391, 10], [393, 11], [392, 12], [390, 13], [392, 14], [390, 15], [391, 16], [389, 17], [390, 18], [387, 19], [389, 20], [390, 21], [389, 22], [389, 23], [385, 24], [385, 25], [385, 26], [383, 27], [381, 28], [379, 29], [375, 30], [364, 31], [359, 32], [326, 33], [489, 34], [425, 35], [420, 36], [412, 37], [415, 38], [412, 39], [408, 40], [408, 41], [409, 42], [409, 43], [409, 44], [410, 45], [411, 46], [409, 47], [411, 48], [409, 49], [416, 50], [414, 51], [414, 52], [415, 53], [416, 54], [415, 55], [417, 56], [418, 57], [417, 58], [417, 59], [422, 60], [419, 61], [420, 62], [421, 63], [424, 64], [423, 65], [427, 66], [430, 67], [432, 68], [430, 69], [435, 70], [442, 71], [444, 72], [450, 73], [459, 74], [465, 75], [471, 76], [481, 77], [493, 78], [525, 79], [572, 80], [647, 81], [767, 82], [800, 83], [176, 84], [231, 85], [267, 86], [298, 87], [306, 88], [322, 89], [332, 90], [340, 91], [344, 92], [349, 93], [354, 94], [361, 95], [364, 96], [365, 97], [368, 98], [369, 99], [370, 100], [374, 101], [374, 102], [373, 103], [376, 104], [376, 105], [377, 106], [380, 107], [379, 108], [380, 109], [380, 110], [382, 111], [382, 112], [381, 113], [383, 114], [384, 115], [384, 116], [386, 117], [384, 118], [385, 119], [386, 120], [385, 121], [386, 122], [388, 123], [388, 124], [387, 125], [388, 126]], 'anticrossing_indices': [[489, 34], [800, 83]], 'peak_indices': [[489, 34], [800, 83]], 'valley_indices': [[326, 33], [176, 84]], 'score': 0.6434958298396819}

Visualize the detections

With the arrays from the previous step, overlay the detector outputs on the magnitude channel:

Python

1 import matplotlib.pyplot as plt
2 
3 freq_axis = np.arange(magnitudes.shape[0])
4 sweep_axis = np.arange(magnitudes.shape[1])
5 
6 fig, ax = plt.subplots(figsize=(10, 6))
7 im = ax.imshow(
8     magnitudes,
9     origin="lower",
10     aspect="auto",
11     extent=[sweep_axis.min(), sweep_axis.max(), freq_axis.min(), freq_axis.max()],
12     cmap="viridis",
13 )
14 fig.colorbar(im, ax=ax, label="Magnitude (a.u.)")
15 
16 
17 def scatter_indices(indices_array, color, label, marker="o"):
18     if indices_array.size == 0:
19         return
20     rows = indices_array[:, 0]
21     cols = indices_array[:, 1]
22     ax.scatter(
23         cols,
24         rows,
25         s=80,
26         c=color,
27         marker=marker,
28         edgecolor="white",
29         linewidth=2,
30         label=label,
31     )
32 
33 
34 scatter_indices(frequency_fit_indices, color="red", label="Frequency fit", marker=".")
35 scatter_indices(peak_indices, color="cyan", label="Peak", marker="^")
36 scatter_indices(valley_indices, color="magenta", label="Valley", marker="v")
37 
38 if anticrossing_indices.size:
39     unique_columns = np.unique(anticrossing_indices[:, 1])
40     for idx, column in enumerate(unique_columns):
41         ax.axvline(
42             x=sweep_axis[column],
43             color="black",
44             linestyle="--",
45             linewidth=2,
46             alpha=0.9,
47             label="Anticrossing" if idx == 0 else "_nolegend_",
48         )
49 
50 ax.set_xlabel("Sweep voltage (a.u.)")
51 ax.set_ylabel("Frequency (a.u.)")
52 ax.set_title(f"Anticrossing Detector v0\nScore = {output['score']:.3f}")
53 ax.legend(loc="upper right", frameon=False)
54 plt.tight_layout()
55 plt.show()

Scatter overlays highlight each feature family while dashed vertical lines mark anticrossings, making it easy to validate frequency fits alongside neighboring peak and valley structures directly on the measurement.

Using physical units

To plot with real voltage and frequency values, use extent and convert indices:

Python

1 # Your physical axes
2 sweep_voltages = np.linspace(-3.5, -3.3, magnitudes.shape[1])  # (n_sweeps,)
3 frequencies_ghz = np.linspace(7.1, 7.2, magnitudes.shape[0])   # (n_freqs,)
4 
5 fig, ax = plt.subplots(figsize=(10, 6))
6 im = ax.imshow(
7     magnitudes,
8     origin="lower",
9     aspect="auto",
10     extent=[sweep_voltages.min(), sweep_voltages.max(),
11             frequencies_ghz.min(), frequencies_ghz.max()],
12     cmap="viridis",
13 )
14 
15 # Convert indices to physical coordinates
16 if peak_indices.size:
17     peak_freqs = frequencies_ghz[peak_indices[:, 0]]
18     peak_volts = sweep_voltages[peak_indices[:, 1]]
19     ax.scatter(peak_volts, peak_freqs, c="cyan", marker="^", s=150,
20                edgecolor="black", label="Peak")
21 
22 ax.set_xlabel("Sweep Voltage (V)")
23 ax.set_ylabel("Frequency (GHz)")