|
30 | 30 | }, |
31 | 31 | { |
32 | 32 | "cell_type": "code", |
33 | | - "execution_count": 19, |
| 33 | + "execution_count": 1, |
34 | 34 | "metadata": { |
35 | 35 | "tags": [] |
36 | 36 | }, |
|
62 | 62 | }, |
63 | 63 | { |
64 | 64 | "cell_type": "code", |
65 | | - "execution_count": 3, |
| 65 | + "execution_count": 8, |
66 | 66 | "metadata": { |
67 | 67 | "tags": [] |
68 | 68 | }, |
|
86 | 86 | " # create entanglement generation protocol with proper parameters\n", |
87 | 87 | " memories = [info.memory for info in memories_info]\n", |
88 | 88 | " memory = memories[0]\n", |
89 | | - " protocol = EntanglementGenerationA(None, \"EGA.\" + memory.name, \"m1\", \"r2\", memory)\n", |
| 89 | + " protocol = EntanglementGenerationA.create(None, \"EGA.\" + memory.name, \"m1\", \"r2\", memory)\n", |
90 | 90 | " protocol.primary = True\n", |
91 | 91 | " \n", |
92 | 92 | " # return values for a rule are: the protocol created, the destination node,\n", |
|
97 | 97 | "def eg_rule_action2(memories_info, args):\n", |
98 | 98 | " memories = [info.memory for info in memories_info]\n", |
99 | 99 | " memory = memories[0]\n", |
100 | | - " protocol = EntanglementGenerationA(None, \"EGA.\" + memory.name, \"m1\", \"r1\", memory)\n", |
| 100 | + " protocol = EntanglementGenerationA.create(None, \"EGA.\" + memory.name, \"m1\", \"r1\", memory)\n", |
101 | 101 | " return [protocol, [None], [None], [None]]" |
102 | 102 | ] |
103 | 103 | }, |
|
122 | 122 | }, |
123 | 123 | { |
124 | 124 | "cell_type": "code", |
125 | | - "execution_count": 17, |
| 125 | + "execution_count": 9, |
126 | 126 | "metadata": { |
127 | 127 | "tags": [] |
128 | 128 | }, |
|
229 | 229 | }, |
230 | 230 | { |
231 | 231 | "cell_type": "code", |
232 | | - "execution_count": 18, |
| 232 | + "execution_count": 10, |
233 | 233 | "metadata": { |
234 | 234 | "tags": [] |
235 | 235 | }, |
236 | 236 | "outputs": [ |
237 | 237 | { |
238 | 238 | "data": { |
239 | 239 | "application/vnd.jupyter.widget-view+json": { |
240 | | - "model_id": "61aa03177b88435c92b8187585d1acdb", |
| 240 | + "model_id": "3f2d5a9f55364774a32773978974385e", |
241 | 241 | "version_major": 2, |
242 | 242 | "version_minor": 0 |
243 | 243 | }, |
|
254 | 254 | "<function __main__.test(sim_time, cc_delay, qc_atten, qc_dist)>" |
255 | 255 | ] |
256 | 256 | }, |
257 | | - "execution_count": 18, |
| 257 | + "execution_count": 10, |
258 | 258 | "metadata": {}, |
259 | 259 | "output_type": "execute_result" |
260 | 260 | } |
|
294 | 294 | "name": "python", |
295 | 295 | "nbconvert_exporter": "python", |
296 | 296 | "pygments_lexer": "ipython3", |
297 | | - "version": "3.13.2" |
| 297 | + "version": "3.13.5" |
298 | 298 | } |
299 | 299 | }, |
300 | 300 | "nbformat": 4, |
|
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