执行量子比特单音能谱扫描。通过扫描 probe 频率并测量响应， 确定 qubit 的基态到第一激发态跃迁频率 (f01)。
import numpy as np
from scipy.signal import find_peaks
def lorentzian(f, f0, A, gamma, offset):
"""洛伦兹线型函数"""
return offset + A * gamma**2 / ((f - f0)**2 + gamma**2)
def run(qubit_id: str, freq_center: float = 5.0e9, freq_span: float = 200.0e6,
num_points: int = 501, drive_power: float = -50.0, ctx=None):
"""执行能谱扫描
Args:
qubit_id: Qubit标识符
freq_center: 扫描中心频率 (Hz)
freq_span: 扫描频率范围 (Hz)
num_points: 扫描点数
drive_power: 驱动功率 (dBm)
ctx: 测量上下文
Returns:
包含测量数据和提取参数的字典（使用单数 dataset 格式）
"""
# 生成频率轴
freqs = np.linspace(freq_center - freq_span/2,
freq_center + freq_span/2, num_points)
# 使用ctx获取仪器并执行测量
# 实际实现中通过ctx访问实验设备
instrument = ctx.get_instrument(f"{qubit_id}_readout")
# 执行测量（实际调用）
# data = instrument.sweep(frequencies=freqs, power=drive_power)
# 模拟数据用于演示
# 实际使用时删除此模拟部分
f01_true = freq_center + 10e6
gamma = 2e6
A = 0.1
offset = 0.5
noise = 0.01
data = lorentzian(freqs, f01_true, A, gamma, offset)
data += np.random.randn(num_points) * noise
# 峰值检测
peaks, properties = find_peaks(np.abs(data - offset), prominence=0.01)
if len(peaks) > 0:
# 取最显著的峰值
main_peak_idx = peaks[np.argmax(properties['prominences'])]
f01 = freqs[main_peak_idx]
# 计算FWHM（基于峰值高度）
peak_height = data[main_peak_idx] - offset
half_max = offset + peak_height / 2
# 找半高位置
above_half = data > half_max
indices = np.where(above_half)[0]
if len(indices) > 0:
fwhm = freqs[indices[-1]] - freqs[indices[0]]
else:
fwhm = gamma * 2 # 默认值
# 计算SNR
signal = np.max(np.abs(data - offset))
noise_std = np.std(data[:50]) # 假设前50点是噪声
snr = 20 * np.log10(signal / noise_std) if noise_std > 0 else 0
else:
# 未检测到峰值，返回中心频率
f01 = freq_center
fwhm = freq_span
snr = 0
return {
'dataset': {
'frequencies': freqs,
'amplitudes': data,
},
'f01': float(f01),
'fwhm': float(fwhm),
'snr': float(snr),
'metadata': {
'qubit_id': qubit_id,
'drive_power': drive_power,
'peak_detected': len(peaks) > 0,
}
}
以下示例展示如何使用 datasets（复数）格式返回多个qubit的测量结果：
def run_multi_qubit(qubit_ids: list, freq_center: float = 5.0e9,
freq_span: float = 200.0e6, num_points: int = 501,
drive_power: float = -50.0, ctx=None):
"""测量多个qubit的能谱
Args:
qubit_ids: Qubit标识符列表，如 ['Q1', 'Q2', 'Q3']
freq_center: 扫描中心频率 (Hz)
freq_span: 扫描频率范围 (Hz)
num_points: 扫描点数
drive_power: 驱动功率 (dBm)
ctx: 测量上下文
Returns:
使用 datasets（复数）格式返回多个qubit的测量数据
"""
datasets = []
summary = {}
for qubit_id in qubit_ids:
# 生成频率轴
freqs = np.linspace(freq_center - freq_span/2,
freq_center + freq_span/2, num_points)
# 获取仪器并执行测量
instrument = ctx.get_instrument(f"{qubit_id}_readout")
# data = instrument.sweep(frequencies=freqs, power=drive_power)
# 模拟数据（实际使用时替换为真实测量）
f01_true = freq_center + np.random.uniform(-50e6, 50e6)
gamma = 2e6
A = 0.1
offset = 0.5
noise = 0.01
data = lorentzian(freqs, f01_true, A, gamma, offset)
data += np.random.randn(num_points) * noise
# 峰值检测
peaks, properties = find_peaks(np.abs(data - offset), prominence=0.01)
if len(peaks) > 0:
main_peak_idx = peaks[np.argmax(properties['prominences'])]
f01 = freqs[main_peak_idx]
peak_height = data[main_peak_idx] - offset
half_max = offset + peak_height / 2
above_half = data > half_max
indices = np.where(above_half)[0]
fwhm = freqs[indices[-1]] - freqs[indices[0]] if len(indices) > 0 else gamma * 2
signal = np.max(np.abs(data - offset))
noise_std = np.std(data[:50])
snr = 20 * np.log10(signal / noise_std) if noise_std > 0 else 0
else:
f01 = freq_center
fwhm = freq_span
snr = 0
# 为每个qubit创建一个dataset
datasets.append({
'frequencies': freqs,
'amplitudes': data,
'qubit_id': qubit_id,
'drive_power': drive_power,
})
# 汇总结果
summary[qubit_id] = {
'f01': float(f01),
'fwhm': float(fwhm),
'snr': float(snr),
'peak_detected': len(peaks) > 0,
}
# 使用 datasets（复数）格式返回
return {
'datasets': datasets, # 多个dataset的列表
'summary': summary,
'measured_qubits': qubit_ids,
}