|
12 | 12 | },
|
13 | 13 | {
|
14 | 14 | "cell_type": "code",
|
15 |
| - "execution_count": 0, |
| 15 | + "execution_count": null, |
16 | 16 | "metadata": {
|
17 | 17 | "cellView": "form",
|
18 | 18 | "colab": {},
|
|
91 | 91 | },
|
92 | 92 | {
|
93 | 93 | "cell_type": "code",
|
94 |
| - "execution_count": 0, |
| 94 | + "execution_count": null, |
95 | 95 | "metadata": {
|
96 | 96 | "colab": {},
|
97 | 97 | "colab_type": "code",
|
98 | 98 | "id": "TorxE5tnkvb2"
|
99 | 99 | },
|
100 | 100 | "outputs": [],
|
101 | 101 | "source": [
|
102 |
| - "!pip install tensorflow==2.7.0" |
| 102 | + "!pip install tensorflow==2.11.0" |
103 | 103 | ]
|
104 | 104 | },
|
105 | 105 | {
|
|
114 | 114 | },
|
115 | 115 | {
|
116 | 116 | "cell_type": "code",
|
117 |
| - "execution_count": 0, |
| 117 | + "execution_count": null, |
118 | 118 | "metadata": {
|
119 | 119 | "colab": {},
|
120 | 120 | "colab_type": "code",
|
121 | 121 | "id": "saFHsRDpkvkH"
|
122 | 122 | },
|
123 | 123 | "outputs": [],
|
124 | 124 | "source": [
|
125 |
| - "!pip install tensorflow-quantum==0.7.2" |
| 125 | + "!pip install tensorflow-quantum==0.7.3" |
126 | 126 | ]
|
127 | 127 | },
|
128 | 128 | {
|
129 | 129 | "cell_type": "code",
|
130 |
| - "execution_count": 0, |
| 130 | + "execution_count": null, |
131 | 131 | "metadata": {
|
132 | 132 | "colab": {},
|
133 | 133 | "colab_type": "code",
|
|
152 | 152 | },
|
153 | 153 | {
|
154 | 154 | "cell_type": "code",
|
155 |
| - "execution_count": 0, |
| 155 | + "execution_count": null, |
156 | 156 | "metadata": {
|
157 | 157 | "colab": {},
|
158 | 158 | "colab_type": "code",
|
|
187 | 187 | },
|
188 | 188 | {
|
189 | 189 | "cell_type": "code",
|
190 |
| - "execution_count": 0, |
| 190 | + "execution_count": null, |
191 | 191 | "metadata": {
|
192 | 192 | "colab": {},
|
193 | 193 | "colab_type": "code",
|
|
212 | 212 | },
|
213 | 213 | {
|
214 | 214 | "cell_type": "code",
|
215 |
| - "execution_count": 0, |
| 215 | + "execution_count": null, |
216 | 216 | "metadata": {
|
217 | 217 | "colab": {},
|
218 | 218 | "colab_type": "code",
|
|
236 | 236 | },
|
237 | 237 | {
|
238 | 238 | "cell_type": "code",
|
239 |
| - "execution_count": 0, |
| 239 | + "execution_count": null, |
240 | 240 | "metadata": {
|
241 | 241 | "colab": {},
|
242 | 242 | "colab_type": "code",
|
|
269 | 269 | },
|
270 | 270 | {
|
271 | 271 | "cell_type": "code",
|
272 |
| - "execution_count": 0, |
| 272 | + "execution_count": null, |
273 | 273 | "metadata": {
|
274 | 274 | "colab": {},
|
275 | 275 | "colab_type": "code",
|
|
302 | 302 | },
|
303 | 303 | {
|
304 | 304 | "cell_type": "code",
|
305 |
| - "execution_count": 0, |
| 305 | + "execution_count": null, |
306 | 306 | "metadata": {
|
307 | 307 | "colab": {},
|
308 | 308 | "colab_type": "code",
|
|
331 | 331 | },
|
332 | 332 | {
|
333 | 333 | "cell_type": "code",
|
334 |
| - "execution_count": 0, |
| 334 | + "execution_count": null, |
335 | 335 | "metadata": {
|
336 | 336 | "colab": {},
|
337 | 337 | "colab_type": "code",
|
|
361 | 361 | },
|
362 | 362 | {
|
363 | 363 | "cell_type": "code",
|
364 |
| - "execution_count": 0, |
| 364 | + "execution_count": null, |
365 | 365 | "metadata": {
|
366 | 366 | "colab": {},
|
367 | 367 | "colab_type": "code",
|
|
370 | 370 | "outputs": [],
|
371 | 371 | "source": [
|
372 | 372 | "# Make input_points = [batch_size, 1] array.\n",
|
373 |
| - "input_points = np.linspace(0, 5, 200)[:, np.newaxis].astype(float)\n", |
| 373 | + "input_points = np.linspace(0, 5, 200)[:, np.newaxis].astype(np.float32)\n", |
374 | 374 | "exact_outputs = expectation_calculation(my_circuit,\n",
|
375 | 375 | " operators=pauli_x,\n",
|
376 | 376 | " symbol_names=['alpha'],\n",
|
|
390 | 390 | },
|
391 | 391 | {
|
392 | 392 | "cell_type": "code",
|
393 |
| - "execution_count": 0, |
| 393 | + "execution_count": null, |
394 | 394 | "metadata": {
|
395 | 395 | "colab": {},
|
396 | 396 | "colab_type": "code",
|
|
439 | 439 | },
|
440 | 440 | {
|
441 | 441 | "cell_type": "code",
|
442 |
| - "execution_count": 0, |
| 442 | + "execution_count": null, |
443 | 443 | "metadata": {
|
444 | 444 | "colab": {},
|
445 | 445 | "colab_type": "code",
|
|
494 | 494 | },
|
495 | 495 | {
|
496 | 496 | "cell_type": "code",
|
497 |
| - "execution_count": 0, |
| 497 | + "execution_count": null, |
498 | 498 | "metadata": {
|
499 | 499 | "colab": {},
|
500 | 500 | "colab_type": "code",
|
|
518 | 518 | },
|
519 | 519 | {
|
520 | 520 | "cell_type": "code",
|
521 |
| - "execution_count": 0, |
| 521 | + "execution_count": null, |
522 | 522 | "metadata": {
|
523 | 523 | "colab": {},
|
524 | 524 | "colab_type": "code",
|
|
548 | 548 | },
|
549 | 549 | {
|
550 | 550 | "cell_type": "code",
|
551 |
| - "execution_count": 0, |
| 551 | + "execution_count": null, |
552 | 552 | "metadata": {
|
553 | 553 | "colab": {},
|
554 | 554 | "colab_type": "code",
|
|
577 | 577 | },
|
578 | 578 | {
|
579 | 579 | "cell_type": "code",
|
580 |
| - "execution_count": 0, |
| 580 | + "execution_count": null, |
581 | 581 | "metadata": {
|
582 | 582 | "colab": {},
|
583 | 583 | "colab_type": "code",
|
|
636 | 636 | },
|
637 | 637 | {
|
638 | 638 | "cell_type": "code",
|
639 |
| - "execution_count": 0, |
| 639 | + "execution_count": null, |
640 | 640 | "metadata": {
|
641 | 641 | "colab": {},
|
642 | 642 | "colab_type": "code",
|
|
694 | 694 | },
|
695 | 695 | {
|
696 | 696 | "cell_type": "code",
|
697 |
| - "execution_count": 0, |
| 697 | + "execution_count": null, |
698 | 698 | "metadata": {
|
699 | 699 | "colab": {},
|
700 | 700 | "colab_type": "code",
|
|
750 | 750 | },
|
751 | 751 | {
|
752 | 752 | "cell_type": "code",
|
753 |
| - "execution_count": 0, |
| 753 | + "execution_count": null, |
754 | 754 | "metadata": {
|
755 | 755 | "colab": {},
|
756 | 756 | "colab_type": "code",
|
|
812 | 812 | "display_name": "Python 3",
|
813 | 813 | "language": "python",
|
814 | 814 | "name": "python3"
|
| 815 | + }, |
| 816 | + "language_info": { |
| 817 | + "name": "python", |
| 818 | + "version": "3.10.9 (main, Dec 7 2022, 13:47:07) [GCC 12.2.0]" |
| 819 | + }, |
| 820 | + "vscode": { |
| 821 | + "interpreter": { |
| 822 | + "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" |
| 823 | + } |
815 | 824 | }
|
816 | 825 | },
|
817 | 826 | "nbformat": 4,
|
|
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