|
258 | 258 | ], |
259 | 259 | "source": [ |
260 | 260 | "p0_max, p1_max, p2_max = [], [], []\n", |
261 | | - "ss = simulation_option[\"simulation_step\"]-1\n", |
262 | 261 | "\n", |
263 | 262 | "for k, DRAG_detuning in enumerate(freq_scan):\n", |
264 | | - " p2_max.append(np.max(qs.expect(result_collection[k][0].states, state_2 * qs.dag(state_2))[ss-1:ss])) \n", |
265 | | - " p1_max.append(np.max(qs.expect(result_collection[k][0].states, state_1 * qs.dag(state_1))[ss-1:ss])) \n", |
266 | | - " p0_max.append(np.max(qs.expect(result_collection[k][0].states, state_0 * qs.dag(state_0))[ss-1:ss])) \n", |
| 263 | + " p2_max.append(qs.expect(result_collection[k][0].states, state_2 * qs.dag(state_2))[-1])\n", |
| 264 | + " p1_max.append(qs.expect(result_collection[k][0].states, state_1 * qs.dag(state_1))[-1])\n", |
| 265 | + " p0_max.append(qs.expect(result_collection[k][0].states, state_0 * qs.dag(state_0))[-1])\n", |
267 | 266 | "plt.plot(freq_scan, p2_max, label=r'$\\rho_{22}$')\n", |
268 | 267 | "plt.plot(freq_scan, p1_max, label=r'$\\rho_{11}$')\n", |
269 | 268 | "plt.plot(freq_scan, p0_max, label=r'$\\rho_{00}$')\n", |
|
325 | 324 | ], |
326 | 325 | "source": [ |
327 | 326 | "p0_max, p1_max, p2_max = [], [], []\n", |
328 | | - "ss = simulation_option[\"simulation_step\"]-1\n", |
329 | 327 | "\n", |
330 | 328 | "for k, DRAG_detuning in enumerate(plateau_scan):\n", |
331 | | - " p2_max.append(np.max(qs.expect(result_collection[k][0].states, state_2 * qs.dag(state_2))[ss-1:ss])) \n", |
332 | | - " p1_max.append(np.max(qs.expect(result_collection[k][0].states, state_1 * qs.dag(state_1))[ss-1:ss])) \n", |
333 | | - " p0_max.append(np.max(qs.expect(result_collection[k][0].states, state_0 * qs.dag(state_0))[ss-1:ss])) \n", |
| 329 | + " p2_max.append(qs.expect(result_collection[k][0].states, state_2 * qs.dag(state_2))[-1])\n", |
| 330 | + " p1_max.append(qs.expect(result_collection[k][0].states, state_1 * qs.dag(state_1))[-1])\n", |
| 331 | + " p0_max.append(qs.expect(result_collection[k][0].states, state_0 * qs.dag(state_0))[-1])\n", |
334 | 332 | "plt.plot(plateau_scan, p2_max, label=r'$\\rho_{22}$')\n", |
335 | 333 | "plt.plot(plateau_scan, p1_max, label=r'$\\rho_{11}$')\n", |
336 | 334 | "plt.plot(plateau_scan, p0_max, label=r'$\\rho_{00}$')\n", |
|
391 | 389 | ], |
392 | 390 | "source": [ |
393 | 391 | "p0_max, p1_max, p2_max = [], [], []\n", |
394 | | - "ss = simulation_option[\"simulation_step\"]-1\n", |
395 | 392 | "\n", |
396 | 393 | "for k, DRAG_detuning in enumerate(width_scan):\n", |
397 | | - " p2_max.append(np.max(qs.expect(result_collection[k][0].states, state_2 * qs.dag(state_2))[ss-1:ss])) \n", |
398 | | - " p1_max.append(np.max(qs.expect(result_collection[k][0].states, state_1 * qs.dag(state_1))[ss-1:ss])) \n", |
399 | | - " p0_max.append(np.max(qs.expect(result_collection[k][0].states, state_0 * qs.dag(state_0))[ss-1:ss])) \n", |
| 394 | + " p2_max.append(qs.expect(result_collection[k][0].states, state_2 * qs.dag(state_2))[-1])\n", |
| 395 | + " p1_max.append(qs.expect(result_collection[k][0].states, state_1 * qs.dag(state_1))[-1])\n", |
| 396 | + " p0_max.append(qs.expect(result_collection[k][0].states, state_0 * qs.dag(state_0))[-1])\n", |
400 | 397 | "plt.plot(width_scan, p2_max, label=r'$\\rho_{22}$')\n", |
401 | 398 | "plt.plot(width_scan, p1_max, label=r'$\\rho_{11}$')\n", |
402 | 399 | "plt.plot(width_scan, p0_max, label=r'$\\rho_{00}$')\n", |
|
455 | 452 | ], |
456 | 453 | "source": [ |
457 | 454 | "p0_max, p1_max, p2_max = [], [], []\n", |
458 | | - "ss = simulation_option[\"simulation_step\"]-1\n", |
459 | 455 | "\n", |
460 | 456 | "for k, DRAG_detuning in enumerate(amp_scan):\n", |
461 | | - " p2_max.append(np.max(qs.expect(result_collection[k][0].states, state_2 * qs.dag(state_2))[ss-1:ss])) \n", |
462 | | - " p1_max.append(np.max(qs.expect(result_collection[k][0].states, state_1 * qs.dag(state_1))[ss-1:ss])) \n", |
463 | | - " p0_max.append(np.max(qs.expect(result_collection[k][0].states, state_0 * qs.dag(state_0))[ss-1:ss])) \n", |
| 457 | + " p2_max.append(qs.expect(result_collection[k][0].states, state_2 * qs.dag(state_2))[-1])\n", |
| 458 | + " p1_max.append(qs.expect(result_collection[k][0].states, state_1 * qs.dag(state_1))[-1])\n", |
| 459 | + " p0_max.append(qs.expect(result_collection[k][0].states, state_0 * qs.dag(state_0))[-1])\n", |
464 | 460 | "plt.plot(amp_scan, p2_max, label=r'$\\rho_{22}$')\n", |
465 | 461 | "plt.plot(amp_scan, p1_max, label=r'$\\rho_{11}$')\n", |
466 | 462 | "plt.plot(amp_scan, p0_max, label=r'$\\rho_{00}$')\n", |
|
496 | 492 | "name": "python", |
497 | 493 | "nbconvert_exporter": "python", |
498 | 494 | "pygments_lexer": "ipython3", |
499 | | - "version": "3.10.2" |
| 495 | + "version": "3.10.11" |
500 | 496 | } |
501 | 497 | }, |
502 | 498 | "nbformat": 4, |
|
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