Loading python/DW_2000Q_5_annealing_schedule.csv 0 → 100644 +402 −0 Original line number Diff line number Diff line s A(s) (GHz) B(s) (GHz) C (normalized) 0.000 10.3214800 0.4927432 0.0000000 0.003 10.1947600 0.4988469 0.0078662 0.005 10.0735300 0.5048283 0.0152582 0.008 9.9566560 0.5107309 0.0223571 0.010 9.8419060 0.5166607 0.0292755 0.013 9.7299380 0.5225796 0.0359539 0.015 9.6234840 0.5283326 0.0423123 0.018 9.5186060 0.5341243 0.0485244 0.020 9.4187370 0.5397574 0.0543493 0.023 9.3202780 0.5454268 0.0600967 0.025 9.2256720 0.5509864 0.0655595 0.028 9.1328000 0.5565538 0.0709139 0.030 9.0429910 0.5620444 0.0760155 0.033 8.9550300 0.5675262 0.0810330 0.035 8.8707240 0.5728801 0.0857985 0.038 8.7867280 0.5783139 0.0905454 0.040 8.7069360 0.5835709 0.0949790 0.043 8.6267100 0.5889521 0.0994126 0.045 8.5498400 0.5942003 0.1036648 0.048 8.4735050 0.5995038 0.1078385 0.050 8.3983200 0.6048189 0.1119649 0.053 8.3261550 0.6100082 0.1158929 0.055 8.2536330 0.6153119 0.1198209 0.058 8.1842350 0.6204723 0.1235809 0.060 8.1165870 0.6255852 0.1271923 0.063 8.0486250 0.6308054 0.1308036 0.065 7.9838530 0.6358603 0.1342642 0.068 7.9199730 0.6409239 0.1376382 0.070 7.8558140 0.6460896 0.1410122 0.073 7.7943690 0.6511133 0.1442520 0.075 7.7343550 0.6560944 0.1473755 0.078 7.6740970 0.6611712 0.1504991 0.080 7.6148570 0.6662370 0.1535837 0.083 7.5582940 0.6711451 0.1565006 0.085 7.5015150 0.6761433 0.1594175 0.088 7.4445170 0.6812341 0.1623343 0.090 7.3906850 0.6861107 0.1651024 0.093 7.3380650 0.6909439 0.1677727 0.095 7.2852580 0.6958612 0.1704430 0.098 7.2322640 0.7008645 0.1731133 0.100 7.1815560 0.7057176 0.1756723 0.103 7.1313460 0.7105879 0.1781808 0.105 7.0809670 0.7155403 0.1806892 0.108 7.0304200 0.7205770 0.1831977 0.110 6.9816250 0.7255042 0.1856278 0.113 6.9338150 0.7303957 0.1879854 0.115 6.8858540 0.7353671 0.1903429 0.118 6.8377420 0.7404202 0.1927005 0.120 6.7902810 0.7454705 0.1950433 0.123 6.7457760 0.7502671 0.1972252 0.125 6.7011420 0.7551376 0.1994071 0.128 6.6563790 0.7600837 0.2015890 0.130 6.6114860 0.7651071 0.2037709 0.133 6.5674150 0.7701008 0.2059119 0.135 6.5248620 0.7749826 0.2079538 0.138 6.4821940 0.7799378 0.2099957 0.140 6.4394100 0.7849677 0.2120376 0.143 6.3965090 0.7900741 0.2140795 0.145 6.3544380 0.7951435 0.2160938 0.148 6.3148290 0.7999737 0.2179743 0.150 6.2751220 0.8048725 0.2198547 0.153 6.2353170 0.8098413 0.2217352 0.155 6.1954140 0.8148814 0.2236157 0.158 6.1554130 0.8199943 0.2254961 0.160 6.1165320 0.8250227 0.2273273 0.163 6.0780810 0.8300538 0.2291269 0.165 6.0395400 0.8351556 0.2309266 0.168 6.0009100 0.8403294 0.2327262 0.170 5.9621910 0.8455767 0.2345259 0.173 5.9233820 0.8508989 0.2363255 0.175 5.8870220 0.8559427 0.2380224 0.178 5.8509180 0.8610079 0.2396936 0.180 5.8147370 0.8661411 0.2413649 0.183 5.7784800 0.8713435 0.2430361 0.185 5.7421480 0.8766163 0.2447073 0.188 5.7057400 0.8819610 0.2463785 0.190 5.6703590 0.8872142 0.2480009 0.193 5.6356530 0.8924250 0.2495823 0.195 5.6008800 0.8977042 0.2511638 0.198 5.5660400 0.9030528 0.2527452 0.200 5.5311330 0.9084723 0.2543266 0.203 5.4961590 0.9139638 0.2559080 0.205 5.4611470 0.9195242 0.2574894 0.208 5.4273370 0.9249541 0.2590164 0.210 5.3935110 0.9304472 0.2605394 0.213 5.3596250 0.9360118 0.2620623 0.215 5.3256790 0.9416490 0.2635853 0.218 5.2916730 0.9473603 0.2651082 0.220 5.2576090 0.9531468 0.2666312 0.223 5.2236700 0.9589777 0.2681491 0.225 5.1912230 0.9646152 0.2695952 0.228 5.1587240 0.9703241 0.2710413 0.230 5.1261720 0.9761056 0.2724873 0.233 5.0935700 0.9819608 0.2739334 0.235 5.0609160 0.9878911 0.2753795 0.238 5.0282120 0.9938976 0.2768256 0.240 4.9954590 0.9999817 0.2782716 0.243 4.9637460 1.0059390 0.2796707 0.245 4.9321630 1.0119370 0.2810605 0.248 4.9005360 1.0180100 0.2824503 0.250 4.8688640 1.0241600 0.2838401 0.253 4.8371490 1.0303880 0.2852299 0.255 4.8053910 1.0366950 0.2866196 0.258 4.7735900 1.0430820 0.2880094 0.260 4.7420410 1.0494900 0.2893872 0.263 4.7110580 1.0558550 0.2907384 0.265 4.6800370 1.0622980 0.2920895 0.268 4.6489780 1.0688230 0.2934407 0.270 4.6178820 1.0754300 0.2947919 0.273 4.5867490 1.0821200 0.2961430 0.275 4.5555810 1.0888940 0.2974942 0.278 4.5243770 1.0957550 0.2988454 0.280 4.4935540 1.1026100 0.3001779 0.283 4.4630100 1.1094800 0.3014965 0.285 4.4324350 1.1164370 0.3028152 0.288 4.4018280 1.1234810 0.3041338 0.290 4.3711930 1.1306130 0.3054524 0.293 4.3405280 1.1378350 0.3067710 0.295 4.3098340 1.1451490 0.3080896 0.298 4.2791140 1.1525550 0.3094082 0.300 4.2486060 1.1599960 0.3107171 0.303 4.2182150 1.1674960 0.3120205 0.305 4.1877990 1.1750910 0.3133239 0.308 4.1573600 1.1827810 0.3146273 0.310 4.1268980 1.1905690 0.3159307 0.313 4.0964150 1.1984560 0.3172342 0.315 4.0659120 1.2064430 0.3185376 0.318 4.0353890 1.2145320 0.3198410 0.320 4.0049470 1.2226970 0.3211393 0.323 3.9745540 1.2309490 0.3224344 0.325 3.9441450 1.2393050 0.3237294 0.328 3.9137210 1.2477690 0.3250245 0.330 3.8832840 1.2563410 0.3263196 0.333 3.8528330 1.2650220 0.3276147 0.335 3.8223720 1.2738150 0.3289098 0.338 3.7918990 1.2827210 0.3302049 0.340 3.7613730 1.2917560 0.3315022 0.343 3.7307940 1.3009200 0.3328014 0.345 3.7002100 1.3102020 0.3341005 0.348 3.6696200 1.3196060 0.3353997 0.350 3.6390270 1.3291310 0.3366988 0.353 3.6084310 1.3387800 0.3379980 0.355 3.5778340 1.3485540 0.3392971 0.358 3.5472390 1.3584560 0.3405963 0.360 3.5165650 1.3685120 0.3418990 0.363 3.4857980 1.3787330 0.3432055 0.365 3.4550360 1.3890880 0.3445120 0.368 3.4242810 1.3995790 0.3458185 0.370 3.3935360 1.4102080 0.3471249 0.373 3.3628000 1.4209760 0.3484314 0.375 3.3320770 1.4318850 0.3497379 0.378 3.3013680 1.4429380 0.3510444 0.380 3.2704340 1.4542240 0.3523621 0.383 3.2392060 1.4657750 0.3536914 0.385 3.2079990 1.4774820 0.3550207 0.388 3.1768140 1.4893440 0.3563500 0.390 3.1456540 1.5013640 0.3576792 0.393 3.1145210 1.5135450 0.3590085 0.395 3.0834180 1.5258880 0.3603378 0.398 3.0523450 1.5383950 0.3616671 0.400 3.0209480 1.5512150 0.3630137 0.403 2.9893160 1.5643210 0.3643706 0.405 2.9577240 1.5776040 0.3657275 0.408 2.9261740 1.5910660 0.3670843 0.410 2.8946700 1.6047090 0.3684412 0.413 2.8632140 1.6185350 0.3697981 0.415 2.8318090 1.6325470 0.3711550 0.418 2.8004570 1.6467460 0.3725118 0.420 2.7684270 1.6614730 0.3739021 0.423 2.7363310 1.6764620 0.3752953 0.425 2.7043010 1.6916550 0.3766884 0.428 2.6723400 1.7070530 0.3780815 0.430 2.6404510 1.7226600 0.3794747 0.433 2.6086380 1.7384770 0.3808678 0.435 2.5769030 1.7545050 0.3822609 0.438 2.5449410 1.7709070 0.3836708 0.440 2.5124920 1.7878310 0.3851042 0.443 2.4801380 1.8049850 0.3865376 0.445 2.4478810 1.8223700 0.3879710 0.448 2.4157260 1.8399890 0.3894044 0.450 2.3836780 1.8578440 0.3908378 0.453 2.3517390 1.8759350 0.3922712 0.455 2.3198460 1.8943030 0.3937107 0.458 2.2869850 1.9135560 0.3951947 0.460 2.2542360 1.9330810 0.3966787 0.463 2.2216220 1.9528660 0.3981627 0.465 2.1891500 1.9729140 0.3996468 0.468 2.1568230 1.9932250 0.4011308 0.470 2.1246460 2.0138010 0.4026148 0.473 2.0925850 2.0346680 0.4041024 0.475 2.0597630 2.0564170 0.4056350 0.478 2.0270840 2.0784720 0.4071675 0.480 1.9945860 2.1008110 0.4087000 0.483 1.9622730 2.1234350 0.4102325 0.485 1.9301510 2.1463440 0.4117650 0.488 1.8982260 2.1695380 0.4132975 0.490 1.8663370 2.1931400 0.4148415 0.493 1.8338860 2.2176170 0.4164195 0.495 1.8016600 2.2423950 0.4179975 0.498 1.7696660 2.2674730 0.4195755 0.500 1.7379080 2.2928510 0.4211536 0.503 1.7063930 2.3185260 0.4227316 0.505 1.6751260 2.3444970 0.4243096 0.508 1.6435430 2.3712470 0.4259201 0.510 1.6117570 2.3987160 0.4275499 0.513 1.5802540 2.4264940 0.4291797 0.515 1.5490410 2.4545790 0.4308095 0.518 1.5181220 2.4829680 0.4324394 0.520 1.4875040 2.5116570 0.4340692 0.523 1.4570350 2.5407910 0.4357125 0.525 1.4258920 2.5711990 0.4374065 0.528 1.3950910 2.6019160 0.4391004 0.530 1.3646390 2.6329390 0.4407944 0.533 1.3345400 2.6642610 0.4424883 0.535 1.3048000 2.6958770 0.4441823 0.538 1.2754020 2.7278050 0.4458787 0.540 1.2453150 2.7612010 0.4476399 0.543 1.2155630 2.7949730 0.4494012 0.545 1.1862200 2.8290340 0.4511624 0.548 1.1572910 2.8633760 0.4529236 0.550 1.1287820 2.8979900 0.4546848 0.553 1.1006620 2.9329080 0.4564503 0.555 1.0723690 2.9688530 0.4582539 0.558 1.0445080 3.0050810 0.4600576 0.560 1.0171020 3.0415550 0.4618613 0.563 0.9901544 3.0782650 0.4636649 0.565 0.9636674 3.1152000 0.4654686 0.568 0.9374440 3.1526330 0.4672911 0.570 0.9109903 3.1913170 0.4691569 0.573 0.8850369 3.2302070 0.4710228 0.575 0.8595852 3.2692890 0.4728886 0.578 0.8346360 3.3085520 0.4747544 0.580 0.8101895 3.3479820 0.4766203 0.583 0.7857143 3.3884510 0.4785347 0.585 0.7614481 3.4296150 0.4804700 0.588 0.7377197 3.4709180 0.4824053 0.590 0.7145274 3.5123450 0.4843406 0.593 0.6918690 3.5538830 0.4862759 0.595 0.6696426 3.5957060 0.4882234 0.598 0.6475893 3.6383210 0.4901996 0.600 0.6260833 3.6810100 0.4921758 0.603 0.6051205 3.7237590 0.4941521 0.605 0.5846962 3.7665550 0.4961283 0.608 0.5648035 3.8093880 0.4981049 0.610 0.5448362 3.8535890 0.5001497 0.613 0.5253721 3.8979230 0.5021946 0.615 0.5064591 3.9422540 0.5042394 0.618 0.4880901 3.9865680 0.5062842 0.620 0.4702570 4.0308530 0.5083290 0.623 0.4525460 4.0761470 0.5104273 0.625 0.4352829 4.1215380 0.5125321 0.628 0.4185607 4.1668460 0.5146369 0.630 0.4023698 4.2120850 0.5167417 0.633 0.3867000 4.2571210 0.5188464 0.635 0.3712140 4.3029840 0.5210031 0.638 0.3561886 4.3489930 0.5231640 0.640 0.3416782 4.3949360 0.5253249 0.643 0.3276712 4.4407970 0.5274859 0.645 0.3141369 4.4865250 0.5296526 0.648 0.3007151 4.5334710 0.5318817 0.650 0.2877687 4.5803880 0.5341108 0.653 0.2753064 4.6271820 0.5363399 0.655 0.2633152 4.6738420 0.5385690 0.658 0.2517204 4.7206060 0.5408142 0.660 0.2404393 4.7677980 0.5430841 0.663 0.2296051 4.8148240 0.5453540 0.665 0.2192040 4.8616770 0.5476239 0.668 0.2092226 4.9083510 0.5498938 0.670 0.1994386 4.9558690 0.5522203 0.673 0.1899745 5.0036670 0.5545620 0.675 0.1809147 5.0512630 0.5569037 0.678 0.1722451 5.0986530 0.5592455 0.680 0.1639106 5.1460710 0.5616045 0.683 0.1558090 5.1940910 0.5640001 0.685 0.1480724 5.2418940 0.5663956 0.688 0.1406869 5.2894790 0.5687911 0.690 0.1336391 5.3368480 0.5711867 0.693 0.1268121 5.3847390 0.5736242 0.695 0.1202899 5.4325380 0.5760636 0.698 0.1140775 5.4801160 0.5785031 0.700 0.1081621 5.5274770 0.5809425 0.703 0.1024473 5.5753310 0.5834259 0.705 0.0969532 5.6235150 0.5859309 0.708 0.0917335 5.6714770 0.5884359 0.710 0.0867762 5.7192210 0.5909408 0.713 0.0820268 5.7671830 0.5934752 0.715 0.0774584 5.8156180 0.5960395 0.718 0.0731289 5.8638350 0.5986038 0.720 0.0690270 5.9118370 0.6011681 0.723 0.0651172 5.9599310 0.6037548 0.725 0.0613651 6.0085030 0.6063726 0.728 0.0578174 6.0568630 0.6089904 0.730 0.0544639 6.1050140 0.6116082 0.733 0.0512729 6.1533280 0.6142530 0.735 0.0482116 6.2022340 0.6169344 0.738 0.0453226 6.2509230 0.6196158 0.740 0.0425985 6.2994070 0.6222972 0.743 0.0400106 6.3480640 0.6250053 0.745 0.0375467 6.3970660 0.6277376 0.748 0.0352277 6.4458660 0.6304699 0.750 0.0330457 6.4944700 0.6332021 0.753 0.0309659 6.5435320 0.6359792 0.755 0.0289935 6.5928730 0.6387756 0.758 0.0271417 6.6420180 0.6415721 0.760 0.0254036 6.6909700 0.6443685 0.763 0.0237493 6.7404390 0.6472116 0.765 0.0221947 6.7898550 0.6500566 0.768 0.0207381 6.8390800 0.6529017 0.770 0.0193701 6.8882470 0.6557603 0.773 0.0180720 6.9379130 0.6586532 0.775 0.0168580 6.9873910 0.6615461 0.778 0.0157228 7.0366830 0.6644389 0.780 0.0146484 7.0864100 0.6673768 0.783 0.0136352 7.1364760 0.6703369 0.785 0.0126898 7.1863560 0.6732969 0.788 0.0118067 7.2361260 0.6762659 0.790 0.0109681 7.2866580 0.6792913 0.793 0.0101864 7.3370710 0.6823167 0.795 0.0094589 7.3872990 0.6853420 0.798 0.0087767 7.4377400 0.6883970 0.800 0.0081385 7.4883230 0.6914659 0.803 0.0075456 7.5387230 0.6945348 0.805 0.0069934 7.5890610 0.6976180 0.808 0.0064736 7.6399370 0.7007396 0.810 0.0059915 7.6906310 0.7038612 0.813 0.0055445 7.7411440 0.7069828 0.815 0.0051228 7.7923900 0.7101693 0.818 0.0047307 7.8437080 0.7133612 0.820 0.0043679 7.8948450 0.7165531 0.823 0.0040302 7.9461250 0.7197715 0.825 0.0037164 7.9975340 0.7230044 0.828 0.0034265 8.0487620 0.7262374 0.830 0.0031572 8.1000990 0.7294980 0.833 0.0029051 8.1520370 0.7327979 0.835 0.0026728 8.2037930 0.7360978 0.838 0.0024583 8.2554710 0.7394109 0.840 0.0022581 8.3076730 0.7427647 0.843 0.0020739 8.3597110 0.7461185 0.845 0.0019042 8.4116260 0.7494798 0.848 0.0017459 8.4641530 0.7528931 0.850 0.0016004 8.5165730 0.7563064 0.853 0.0014667 8.5688440 0.7597239 0.855 0.0013421 8.6217790 0.7632026 0.858 0.0012278 8.6746440 0.7666813 0.860 0.0011229 8.7273420 0.7701624 0.863 0.0010260 8.7803800 0.7736815 0.865 0.0009373 8.8333100 0.7772005 0.868 0.0008560 8.8860980 0.7807252 0.870 0.0007806 8.9395110 0.7843094 0.873 0.0007116 8.9928380 0.7878936 0.875 0.0006486 9.0460600 0.7914884 0.878 0.0005903 9.0999000 0.7951353 0.880 0.0005370 9.1536210 0.7987822 0.883 0.0004885 9.2072460 0.8024415 0.885 0.0004439 9.2611900 0.8061296 0.888 0.0004033 9.3149490 0.8098177 0.890 0.0003662 9.3688210 0.8135369 0.893 0.0003321 9.4231720 0.8172915 0.895 0.0003011 9.4773340 0.8210462 0.898 0.0002727 9.5317580 0.8248419 0.900 0.0002469 9.5864020 0.8286568 0.903 0.0002234 9.6408540 0.8324717 0.905 0.0002020 9.6957280 0.8363384 0.908 0.0001825 9.7505800 0.8402083 0.910 0.0001648 9.8053540 0.8440944 0.913 0.0001487 9.8607450 0.8480320 0.915 0.0001340 9.9159820 0.8519697 0.918 0.0001208 9.9712720 0.8559327 0.920 0.0001088 10.0266900 0.8599140 0.923 0.0000980 10.0819200 0.8638953 0.925 0.0000881 10.1377600 0.8679458 0.928 0.0000791 10.1936200 0.8719994 0.930 0.0000711 10.2494400 0.8760729 0.933 0.0000638 10.3056300 0.8801795 0.935 0.0000572 10.3616200 0.8842861 0.938 0.0000513 10.4178300 0.8884311 0.940 0.0000460 10.4740400 0.8925840 0.943 0.0000412 10.5301900 0.8967583 0.945 0.0000368 10.5869000 0.9009797 0.948 0.0000329 10.6434100 0.9052011 0.950 0.0000294 10.7003500 0.9094815 0.953 0.0000262 10.7574100 0.9137736 0.955 0.0000234 10.8144000 0.9180851 0.958 0.0000208 10.8716900 0.9224256 0.960 0.0000186 10.9287700 0.9267661 0.963 0.0000165 10.9862000 0.9311582 0.965 0.0000147 11.0435400 0.9355507 0.968 0.0000131 11.1010600 0.9399859 0.970 0.0000116 11.1588400 0.9444484 0.973 0.0000103 11.2165000 0.9489248 0.975 0.0000091 11.2745400 0.9534390 0.978 0.0000081 11.3324000 0.9579532 0.980 0.0000072 11.3907800 0.9625390 0.983 0.0000064 11.4491500 0.9671284 0.985 0.0000056 11.5075700 0.9717468 0.988 0.0000050 11.5660400 0.9763794 0.990 0.0000044 11.6245100 0.9810431 0.993 0.0000039 11.6834600 0.9857505 0.995 0.0000034 11.7422500 0.9904692 0.998 0.0000030 11.8014400 0.9952346 1.000 0.0000026 11.8604700 1.0000000 python/phase_diagram_data_collection.py 0 → 100644 +234 −0 Original line number Diff line number Diff line from dwave.cloud import Client from Embeddings import tilt_embedding, half_cell_embedding from Drawing import * from Drivers import * from Analysis import * import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib.ticker import FormatStrFormatter import seaborn as sns import pandas as pd import numpy as np import pickle import networkx as nx from itertools import product import os import warnings warnings.filterwarnings('ignore') from concurrent.futures import as_completed, ProcessPoolExecutor # Get the solver #ep = '44.230.13.66/sapi' ep = '54.201.1.180/sapi' endpoint = 'https://{}/'.format(ep) client = Client.from_config(endpoint=endpoint,permissive_ssl=True) client.session.headers.update({'host': 'cloud.dwavesys.com'}) solver = client.get_solver('DW_2000Q_5') client.close() # Build the embedding unpruned_embedding = half_cell_embedding(solver=solver, shift=0) emb = prune_shastry_sutherland(unpruned_embedding) ax, pos = draw_logical(emb, shift=0, reference='square') initial_state = FM_initial_state(emb,solver) init_state_name = 'FM' J1_mag = 1 factor_step = 0.2 h_step = 0.04 factors = np.round(np.arange(0,3 + factor_step,factor_step),5) h_targets = np.round(np.arange(-2,0 + h_step,h_step),5) mesh_points = list(product(factors,h_targets)) raw_inputs = [(J1_mag,J1_mag*factor,-4,h) for factor,h in mesh_points] chi = -0.03 inputs = [get_inputs_from_logical(J1, J2, J3, h, chi) for (J1,J2,J3,h) in raw_inputs] check_inputs_ranges(inputs) J_inputs = [] for (J1,J2,J3,h) in inputs: if (J1,J2,J3) in J_inputs: pass else: J_inputs.append((J1,J2,J3)) shift = 0 emb_name = 'halfcell' sampling_parameters = {'annealing_time': 500, 'num_reads': 500, 'readout_thermalization': 500, 'answer_mode': 'histogram', 'auto_scale': False } fbos={} t_pause=1998 def parameter_gen(s): return {'num_reads': 100, 'readout_thermalization': 100, 'answer_mode': 'raw', 'reinitialize_state' : False, 'anneal_schedule' : [[0,1],[1, s],[t_pause+1,s],[t_pause+2,1]], 'initial_state' : initial_state, 'auto_scale': False} s = 0.4 mode=2 field_steps = [*([0.04]*100), *([0.02]*100), *([0.01]*100)] h_mags = [inp[3] for inp in inputs] anneal_mode = 'quench' if mode==2 else 'ramp' init_state_name = 'FM' boundary_iters = 400 fbo_iters = 200 def run_save_fbo(J1,J2,J3): try: bias_df = pd.read_pickle('./fbo_data/Phase_Diagram/calibration_fbo_{J1}_{J2}_{J3}_{emb}_{shift}.p'.format(J1=J1, J2=J2, J3=J3,emb=emb_name, shift=shift)) mag_df = pd.read_pickle('./fbo_data/Phase_Diagram/calibration_mag_{J1}_{J2}_{J3}_{emb}_{shift}.p'.format(J1=J1, J2=J2, J3=J3,emb=emb_name, shift=shift)) fbo = bias_df.iloc[-1,:].values.tolist() del bias_df del mag_df return fbo except: pass with Client.from_config(endpoint=endpoint,connection_close=True,permissive_ssl=True,request_timeout=360) as client: client.session.headers.update({'host': 'cloud.dwavesys.com'}) solver = client.get_solver('DW_2000Q_5') bias_df, mag_df, used_qubits = calibrate_offsets(emb, solver, sampling_parameters, J1, J2, J3, iters=fbo_iters) bias_df.to_pickle('./fbo_data/Phase_Diagram/calibration_fbo_{J1}_{J2}_{J3}_{emb}_{shift}.p'.format(J1=J1, J2=J2, J3=J3,emb=emb_name, shift=shift)) mag_df.to_pickle('./fbo_data/Phase_Diagram/calibration_mag_{J1}_{J2}_{J3}_{emb}_{shift}.p'.format(J1=J1, J2=J2, J3=J3,emb=emb_name, shift=shift)) fbo = bias_df.iloc[-1,:].values.tolist() del bias_df del mag_df del used_qubits return fbo def run_boundary_shim(J1,J2,J3,h,fbos): savename = './RA_Data/Phase_Diagram/{J1}_{J2}_{J3}_{h}_{anneal_mode}_{s}_{init_state_name}_{emb_name}'.format(J1=J1,J2=J2, J3=J3, \ h=h, anneal_mode='quench',s=0.4, init_state_name='FM',\ emb_name='halfcell') directory_path = './RA_Data/Phase_Diagram/' basename = '{J1}_{J2}_{J3}_{h}_{anneal_mode}_{s}_{init_state_name}_{emb_name}.npz'.format(J1=J1,J2=J2, J3=J3, \ h=h, anneal_mode='quench',s=0.4, init_state_name='FM',\ emb_name='halfcell') savename = directory_path+basename print('({},{},{},{}) Boundary Shim Started'.format(J1,J2,J3,h)) if basename in os.listdir(directory_path): return (0,0) with Client.from_config(endpoint=endpoint,connection_close=True,permissive_ssl=True,request_timeout=360) as client: client.session.headers.update({'host': 'cloud.dwavesys.com'}) solver = client.get_solver('DW_2000Q_5') result = boundary_shim_driver(emb, solver, flux_biases=fbos, sampling_parameters=parameter_gen(s), \ J1_mag=J1, J2_mag=J2, J3_mag=J3, h_mag = h, max_iters=boundary_iters, \ field_steps=field_steps, burn_in=50, num_calls=10, num_final_calls=100, grad_tol=0.0005) np.savez(savename, fields=result[0].values, mags=result[1].values, qubits=result[2], solution_array=result[3]) del result #print('saved') return (0,0) if __name__ == '__main__': n_procs = len(J_inputs) if len(J_inputs)<10 else 10 with ProcessPoolExecutor(n_procs) as executor: results = [executor.submit(run_save_fbo,*x) for x in J_inputs] print('Start FBO Calibration') print('--------------------------------\n') for (x,result) in zip(J_inputs,results): result._x = x for r in as_completed(results): if r.exception: print(r.exception()) result = r.result() for h in h_mags: fbos[(*r._x,h)] = result print('{} FBO Calibration Completed'.format(r._x)) print('--------------------------------\n') print('End FBO Calibration\n') n_procs = len(inputs) if len(inputs)<15 else 15 with ProcessPoolExecutor(n_procs) as executor: results = [executor.submit(run_boundary_shim,*(*x,fbos[x])) for x in inputs] print('Start Boundary Shim') print('--------------------------------\n') for (x,result) in zip(inputs,results): result._x = x for r in as_completed(results): if r.exception(): print(r.exception()) print('Local Error') print('{} Boundary Shim Completed'.format(r._x)) print('--------------------------------\n') print('End Boundary Shim\n') # for (J1,J2,J3,h), fbo in zip(inputs, fbos): # client = Client.from_config(connection_close=True) # solver = client.get_solver('DW_2000Q_5') # savename = './RA_Data/full_calibration/{J1}_{J2}_{J3}_{h}_{anneal_mode}_{s}_{init_state_name}_{emb_name}'.format(J1=J1_mag_target,J2=J2_mag_target, J3=J3_mag_target, \ # h=h_mag_target, anneal_mode=anneal_mode,s=s, init_state_name=init_state_name,\ # emb_name=emb_name) # print(savename.split('/')[-1]+'.npz' in os.listdir('./RA_Data/Phase_Diagram/')) # if savename.split('/')[-1]+'.npz' in os.listdir('./RA_Data/Phase_Diagram/'): # print(savename) # client.close() # continue # field_df, mag_df, qubits, solution = boundary_shim_driver(emb, solver, flux_biases=fbo, sampling_parameters=ra_sampling_parameters, \ # J1_mag=J1_mag_target, J2_mag=J2_mag_target, J3_mag=J3_mag_target, h_mag = h_mag_target, max_iters=400, \ # field_steps=field_steps, burn_in=50, num_calls=10, num_final_calls=200, grad_tol=0.0005) # client.close() # np.savez(savename, fields=field_df.values, mags=mag_df.values, qubits=qubits, solution_array=solution) # Sqs = {} # for J1_mag_target, J2_mag_target, J3_mag_target, h_mag_target in sorted(inputs): # basename = '{J1}_{J2}_{J3}_{h}_{anneal_mode}_{s}_{init_state_name}_{emb_name}'.format(J1=J1_mag_target,J2=J2_mag_target, J3=J3_mag_target, \ # h=h_mag_target, anneal_mode=anneal_mode,s=s, init_state_name=init_state_name,\ # emb_name=emb_name) # if savename.split('/')[-1]+'.npz' in os.listdir('./RA_Data/full_calibration/'): # file = np.load('./RA_Data/Phase_Diagram/' + basename + '.npz') # else: # print(f'{basename} Not Found') # continue # if basename+'.npz' in os.listdir('./Sq_Data/RA/Phase_Diagram'): # Sqs[h_mag_target] = np.load('./Sq_Data/RA/Phase_Diagram/'+basename+'.npz')['Sq'] # print('done') # continue # else: # print(f'{basename} Sq not found') # file = np.load('./RA_Data/full_calibration/' + basename + '.npz') # qubits = file['qubits'] # field_df = pd.DataFrame(columns=qubits, data=file['fields']) # mag_df = pd.DataFrame(columns=qubits, data=file['mags']) # solution_array = file['solution_array'] # file.close() # q1, q2, sq = get_structure_factor(emb, solution_array, positions=pos, num_q_points=100, burn_in=50) # Sqs[h_mag_target] = sq # print(basename) # np.savez_compressed('./Sq_Data/RA/full_calibration/'+basename+'.npz', q1=q1, q2=q2, Sq=sq) Loading
python/DW_2000Q_5_annealing_schedule.csv 0 → 100644 +402 −0 Original line number Diff line number Diff line s A(s) (GHz) B(s) (GHz) C (normalized) 0.000 10.3214800 0.4927432 0.0000000 0.003 10.1947600 0.4988469 0.0078662 0.005 10.0735300 0.5048283 0.0152582 0.008 9.9566560 0.5107309 0.0223571 0.010 9.8419060 0.5166607 0.0292755 0.013 9.7299380 0.5225796 0.0359539 0.015 9.6234840 0.5283326 0.0423123 0.018 9.5186060 0.5341243 0.0485244 0.020 9.4187370 0.5397574 0.0543493 0.023 9.3202780 0.5454268 0.0600967 0.025 9.2256720 0.5509864 0.0655595 0.028 9.1328000 0.5565538 0.0709139 0.030 9.0429910 0.5620444 0.0760155 0.033 8.9550300 0.5675262 0.0810330 0.035 8.8707240 0.5728801 0.0857985 0.038 8.7867280 0.5783139 0.0905454 0.040 8.7069360 0.5835709 0.0949790 0.043 8.6267100 0.5889521 0.0994126 0.045 8.5498400 0.5942003 0.1036648 0.048 8.4735050 0.5995038 0.1078385 0.050 8.3983200 0.6048189 0.1119649 0.053 8.3261550 0.6100082 0.1158929 0.055 8.2536330 0.6153119 0.1198209 0.058 8.1842350 0.6204723 0.1235809 0.060 8.1165870 0.6255852 0.1271923 0.063 8.0486250 0.6308054 0.1308036 0.065 7.9838530 0.6358603 0.1342642 0.068 7.9199730 0.6409239 0.1376382 0.070 7.8558140 0.6460896 0.1410122 0.073 7.7943690 0.6511133 0.1442520 0.075 7.7343550 0.6560944 0.1473755 0.078 7.6740970 0.6611712 0.1504991 0.080 7.6148570 0.6662370 0.1535837 0.083 7.5582940 0.6711451 0.1565006 0.085 7.5015150 0.6761433 0.1594175 0.088 7.4445170 0.6812341 0.1623343 0.090 7.3906850 0.6861107 0.1651024 0.093 7.3380650 0.6909439 0.1677727 0.095 7.2852580 0.6958612 0.1704430 0.098 7.2322640 0.7008645 0.1731133 0.100 7.1815560 0.7057176 0.1756723 0.103 7.1313460 0.7105879 0.1781808 0.105 7.0809670 0.7155403 0.1806892 0.108 7.0304200 0.7205770 0.1831977 0.110 6.9816250 0.7255042 0.1856278 0.113 6.9338150 0.7303957 0.1879854 0.115 6.8858540 0.7353671 0.1903429 0.118 6.8377420 0.7404202 0.1927005 0.120 6.7902810 0.7454705 0.1950433 0.123 6.7457760 0.7502671 0.1972252 0.125 6.7011420 0.7551376 0.1994071 0.128 6.6563790 0.7600837 0.2015890 0.130 6.6114860 0.7651071 0.2037709 0.133 6.5674150 0.7701008 0.2059119 0.135 6.5248620 0.7749826 0.2079538 0.138 6.4821940 0.7799378 0.2099957 0.140 6.4394100 0.7849677 0.2120376 0.143 6.3965090 0.7900741 0.2140795 0.145 6.3544380 0.7951435 0.2160938 0.148 6.3148290 0.7999737 0.2179743 0.150 6.2751220 0.8048725 0.2198547 0.153 6.2353170 0.8098413 0.2217352 0.155 6.1954140 0.8148814 0.2236157 0.158 6.1554130 0.8199943 0.2254961 0.160 6.1165320 0.8250227 0.2273273 0.163 6.0780810 0.8300538 0.2291269 0.165 6.0395400 0.8351556 0.2309266 0.168 6.0009100 0.8403294 0.2327262 0.170 5.9621910 0.8455767 0.2345259 0.173 5.9233820 0.8508989 0.2363255 0.175 5.8870220 0.8559427 0.2380224 0.178 5.8509180 0.8610079 0.2396936 0.180 5.8147370 0.8661411 0.2413649 0.183 5.7784800 0.8713435 0.2430361 0.185 5.7421480 0.8766163 0.2447073 0.188 5.7057400 0.8819610 0.2463785 0.190 5.6703590 0.8872142 0.2480009 0.193 5.6356530 0.8924250 0.2495823 0.195 5.6008800 0.8977042 0.2511638 0.198 5.5660400 0.9030528 0.2527452 0.200 5.5311330 0.9084723 0.2543266 0.203 5.4961590 0.9139638 0.2559080 0.205 5.4611470 0.9195242 0.2574894 0.208 5.4273370 0.9249541 0.2590164 0.210 5.3935110 0.9304472 0.2605394 0.213 5.3596250 0.9360118 0.2620623 0.215 5.3256790 0.9416490 0.2635853 0.218 5.2916730 0.9473603 0.2651082 0.220 5.2576090 0.9531468 0.2666312 0.223 5.2236700 0.9589777 0.2681491 0.225 5.1912230 0.9646152 0.2695952 0.228 5.1587240 0.9703241 0.2710413 0.230 5.1261720 0.9761056 0.2724873 0.233 5.0935700 0.9819608 0.2739334 0.235 5.0609160 0.9878911 0.2753795 0.238 5.0282120 0.9938976 0.2768256 0.240 4.9954590 0.9999817 0.2782716 0.243 4.9637460 1.0059390 0.2796707 0.245 4.9321630 1.0119370 0.2810605 0.248 4.9005360 1.0180100 0.2824503 0.250 4.8688640 1.0241600 0.2838401 0.253 4.8371490 1.0303880 0.2852299 0.255 4.8053910 1.0366950 0.2866196 0.258 4.7735900 1.0430820 0.2880094 0.260 4.7420410 1.0494900 0.2893872 0.263 4.7110580 1.0558550 0.2907384 0.265 4.6800370 1.0622980 0.2920895 0.268 4.6489780 1.0688230 0.2934407 0.270 4.6178820 1.0754300 0.2947919 0.273 4.5867490 1.0821200 0.2961430 0.275 4.5555810 1.0888940 0.2974942 0.278 4.5243770 1.0957550 0.2988454 0.280 4.4935540 1.1026100 0.3001779 0.283 4.4630100 1.1094800 0.3014965 0.285 4.4324350 1.1164370 0.3028152 0.288 4.4018280 1.1234810 0.3041338 0.290 4.3711930 1.1306130 0.3054524 0.293 4.3405280 1.1378350 0.3067710 0.295 4.3098340 1.1451490 0.3080896 0.298 4.2791140 1.1525550 0.3094082 0.300 4.2486060 1.1599960 0.3107171 0.303 4.2182150 1.1674960 0.3120205 0.305 4.1877990 1.1750910 0.3133239 0.308 4.1573600 1.1827810 0.3146273 0.310 4.1268980 1.1905690 0.3159307 0.313 4.0964150 1.1984560 0.3172342 0.315 4.0659120 1.2064430 0.3185376 0.318 4.0353890 1.2145320 0.3198410 0.320 4.0049470 1.2226970 0.3211393 0.323 3.9745540 1.2309490 0.3224344 0.325 3.9441450 1.2393050 0.3237294 0.328 3.9137210 1.2477690 0.3250245 0.330 3.8832840 1.2563410 0.3263196 0.333 3.8528330 1.2650220 0.3276147 0.335 3.8223720 1.2738150 0.3289098 0.338 3.7918990 1.2827210 0.3302049 0.340 3.7613730 1.2917560 0.3315022 0.343 3.7307940 1.3009200 0.3328014 0.345 3.7002100 1.3102020 0.3341005 0.348 3.6696200 1.3196060 0.3353997 0.350 3.6390270 1.3291310 0.3366988 0.353 3.6084310 1.3387800 0.3379980 0.355 3.5778340 1.3485540 0.3392971 0.358 3.5472390 1.3584560 0.3405963 0.360 3.5165650 1.3685120 0.3418990 0.363 3.4857980 1.3787330 0.3432055 0.365 3.4550360 1.3890880 0.3445120 0.368 3.4242810 1.3995790 0.3458185 0.370 3.3935360 1.4102080 0.3471249 0.373 3.3628000 1.4209760 0.3484314 0.375 3.3320770 1.4318850 0.3497379 0.378 3.3013680 1.4429380 0.3510444 0.380 3.2704340 1.4542240 0.3523621 0.383 3.2392060 1.4657750 0.3536914 0.385 3.2079990 1.4774820 0.3550207 0.388 3.1768140 1.4893440 0.3563500 0.390 3.1456540 1.5013640 0.3576792 0.393 3.1145210 1.5135450 0.3590085 0.395 3.0834180 1.5258880 0.3603378 0.398 3.0523450 1.5383950 0.3616671 0.400 3.0209480 1.5512150 0.3630137 0.403 2.9893160 1.5643210 0.3643706 0.405 2.9577240 1.5776040 0.3657275 0.408 2.9261740 1.5910660 0.3670843 0.410 2.8946700 1.6047090 0.3684412 0.413 2.8632140 1.6185350 0.3697981 0.415 2.8318090 1.6325470 0.3711550 0.418 2.8004570 1.6467460 0.3725118 0.420 2.7684270 1.6614730 0.3739021 0.423 2.7363310 1.6764620 0.3752953 0.425 2.7043010 1.6916550 0.3766884 0.428 2.6723400 1.7070530 0.3780815 0.430 2.6404510 1.7226600 0.3794747 0.433 2.6086380 1.7384770 0.3808678 0.435 2.5769030 1.7545050 0.3822609 0.438 2.5449410 1.7709070 0.3836708 0.440 2.5124920 1.7878310 0.3851042 0.443 2.4801380 1.8049850 0.3865376 0.445 2.4478810 1.8223700 0.3879710 0.448 2.4157260 1.8399890 0.3894044 0.450 2.3836780 1.8578440 0.3908378 0.453 2.3517390 1.8759350 0.3922712 0.455 2.3198460 1.8943030 0.3937107 0.458 2.2869850 1.9135560 0.3951947 0.460 2.2542360 1.9330810 0.3966787 0.463 2.2216220 1.9528660 0.3981627 0.465 2.1891500 1.9729140 0.3996468 0.468 2.1568230 1.9932250 0.4011308 0.470 2.1246460 2.0138010 0.4026148 0.473 2.0925850 2.0346680 0.4041024 0.475 2.0597630 2.0564170 0.4056350 0.478 2.0270840 2.0784720 0.4071675 0.480 1.9945860 2.1008110 0.4087000 0.483 1.9622730 2.1234350 0.4102325 0.485 1.9301510 2.1463440 0.4117650 0.488 1.8982260 2.1695380 0.4132975 0.490 1.8663370 2.1931400 0.4148415 0.493 1.8338860 2.2176170 0.4164195 0.495 1.8016600 2.2423950 0.4179975 0.498 1.7696660 2.2674730 0.4195755 0.500 1.7379080 2.2928510 0.4211536 0.503 1.7063930 2.3185260 0.4227316 0.505 1.6751260 2.3444970 0.4243096 0.508 1.6435430 2.3712470 0.4259201 0.510 1.6117570 2.3987160 0.4275499 0.513 1.5802540 2.4264940 0.4291797 0.515 1.5490410 2.4545790 0.4308095 0.518 1.5181220 2.4829680 0.4324394 0.520 1.4875040 2.5116570 0.4340692 0.523 1.4570350 2.5407910 0.4357125 0.525 1.4258920 2.5711990 0.4374065 0.528 1.3950910 2.6019160 0.4391004 0.530 1.3646390 2.6329390 0.4407944 0.533 1.3345400 2.6642610 0.4424883 0.535 1.3048000 2.6958770 0.4441823 0.538 1.2754020 2.7278050 0.4458787 0.540 1.2453150 2.7612010 0.4476399 0.543 1.2155630 2.7949730 0.4494012 0.545 1.1862200 2.8290340 0.4511624 0.548 1.1572910 2.8633760 0.4529236 0.550 1.1287820 2.8979900 0.4546848 0.553 1.1006620 2.9329080 0.4564503 0.555 1.0723690 2.9688530 0.4582539 0.558 1.0445080 3.0050810 0.4600576 0.560 1.0171020 3.0415550 0.4618613 0.563 0.9901544 3.0782650 0.4636649 0.565 0.9636674 3.1152000 0.4654686 0.568 0.9374440 3.1526330 0.4672911 0.570 0.9109903 3.1913170 0.4691569 0.573 0.8850369 3.2302070 0.4710228 0.575 0.8595852 3.2692890 0.4728886 0.578 0.8346360 3.3085520 0.4747544 0.580 0.8101895 3.3479820 0.4766203 0.583 0.7857143 3.3884510 0.4785347 0.585 0.7614481 3.4296150 0.4804700 0.588 0.7377197 3.4709180 0.4824053 0.590 0.7145274 3.5123450 0.4843406 0.593 0.6918690 3.5538830 0.4862759 0.595 0.6696426 3.5957060 0.4882234 0.598 0.6475893 3.6383210 0.4901996 0.600 0.6260833 3.6810100 0.4921758 0.603 0.6051205 3.7237590 0.4941521 0.605 0.5846962 3.7665550 0.4961283 0.608 0.5648035 3.8093880 0.4981049 0.610 0.5448362 3.8535890 0.5001497 0.613 0.5253721 3.8979230 0.5021946 0.615 0.5064591 3.9422540 0.5042394 0.618 0.4880901 3.9865680 0.5062842 0.620 0.4702570 4.0308530 0.5083290 0.623 0.4525460 4.0761470 0.5104273 0.625 0.4352829 4.1215380 0.5125321 0.628 0.4185607 4.1668460 0.5146369 0.630 0.4023698 4.2120850 0.5167417 0.633 0.3867000 4.2571210 0.5188464 0.635 0.3712140 4.3029840 0.5210031 0.638 0.3561886 4.3489930 0.5231640 0.640 0.3416782 4.3949360 0.5253249 0.643 0.3276712 4.4407970 0.5274859 0.645 0.3141369 4.4865250 0.5296526 0.648 0.3007151 4.5334710 0.5318817 0.650 0.2877687 4.5803880 0.5341108 0.653 0.2753064 4.6271820 0.5363399 0.655 0.2633152 4.6738420 0.5385690 0.658 0.2517204 4.7206060 0.5408142 0.660 0.2404393 4.7677980 0.5430841 0.663 0.2296051 4.8148240 0.5453540 0.665 0.2192040 4.8616770 0.5476239 0.668 0.2092226 4.9083510 0.5498938 0.670 0.1994386 4.9558690 0.5522203 0.673 0.1899745 5.0036670 0.5545620 0.675 0.1809147 5.0512630 0.5569037 0.678 0.1722451 5.0986530 0.5592455 0.680 0.1639106 5.1460710 0.5616045 0.683 0.1558090 5.1940910 0.5640001 0.685 0.1480724 5.2418940 0.5663956 0.688 0.1406869 5.2894790 0.5687911 0.690 0.1336391 5.3368480 0.5711867 0.693 0.1268121 5.3847390 0.5736242 0.695 0.1202899 5.4325380 0.5760636 0.698 0.1140775 5.4801160 0.5785031 0.700 0.1081621 5.5274770 0.5809425 0.703 0.1024473 5.5753310 0.5834259 0.705 0.0969532 5.6235150 0.5859309 0.708 0.0917335 5.6714770 0.5884359 0.710 0.0867762 5.7192210 0.5909408 0.713 0.0820268 5.7671830 0.5934752 0.715 0.0774584 5.8156180 0.5960395 0.718 0.0731289 5.8638350 0.5986038 0.720 0.0690270 5.9118370 0.6011681 0.723 0.0651172 5.9599310 0.6037548 0.725 0.0613651 6.0085030 0.6063726 0.728 0.0578174 6.0568630 0.6089904 0.730 0.0544639 6.1050140 0.6116082 0.733 0.0512729 6.1533280 0.6142530 0.735 0.0482116 6.2022340 0.6169344 0.738 0.0453226 6.2509230 0.6196158 0.740 0.0425985 6.2994070 0.6222972 0.743 0.0400106 6.3480640 0.6250053 0.745 0.0375467 6.3970660 0.6277376 0.748 0.0352277 6.4458660 0.6304699 0.750 0.0330457 6.4944700 0.6332021 0.753 0.0309659 6.5435320 0.6359792 0.755 0.0289935 6.5928730 0.6387756 0.758 0.0271417 6.6420180 0.6415721 0.760 0.0254036 6.6909700 0.6443685 0.763 0.0237493 6.7404390 0.6472116 0.765 0.0221947 6.7898550 0.6500566 0.768 0.0207381 6.8390800 0.6529017 0.770 0.0193701 6.8882470 0.6557603 0.773 0.0180720 6.9379130 0.6586532 0.775 0.0168580 6.9873910 0.6615461 0.778 0.0157228 7.0366830 0.6644389 0.780 0.0146484 7.0864100 0.6673768 0.783 0.0136352 7.1364760 0.6703369 0.785 0.0126898 7.1863560 0.6732969 0.788 0.0118067 7.2361260 0.6762659 0.790 0.0109681 7.2866580 0.6792913 0.793 0.0101864 7.3370710 0.6823167 0.795 0.0094589 7.3872990 0.6853420 0.798 0.0087767 7.4377400 0.6883970 0.800 0.0081385 7.4883230 0.6914659 0.803 0.0075456 7.5387230 0.6945348 0.805 0.0069934 7.5890610 0.6976180 0.808 0.0064736 7.6399370 0.7007396 0.810 0.0059915 7.6906310 0.7038612 0.813 0.0055445 7.7411440 0.7069828 0.815 0.0051228 7.7923900 0.7101693 0.818 0.0047307 7.8437080 0.7133612 0.820 0.0043679 7.8948450 0.7165531 0.823 0.0040302 7.9461250 0.7197715 0.825 0.0037164 7.9975340 0.7230044 0.828 0.0034265 8.0487620 0.7262374 0.830 0.0031572 8.1000990 0.7294980 0.833 0.0029051 8.1520370 0.7327979 0.835 0.0026728 8.2037930 0.7360978 0.838 0.0024583 8.2554710 0.7394109 0.840 0.0022581 8.3076730 0.7427647 0.843 0.0020739 8.3597110 0.7461185 0.845 0.0019042 8.4116260 0.7494798 0.848 0.0017459 8.4641530 0.7528931 0.850 0.0016004 8.5165730 0.7563064 0.853 0.0014667 8.5688440 0.7597239 0.855 0.0013421 8.6217790 0.7632026 0.858 0.0012278 8.6746440 0.7666813 0.860 0.0011229 8.7273420 0.7701624 0.863 0.0010260 8.7803800 0.7736815 0.865 0.0009373 8.8333100 0.7772005 0.868 0.0008560 8.8860980 0.7807252 0.870 0.0007806 8.9395110 0.7843094 0.873 0.0007116 8.9928380 0.7878936 0.875 0.0006486 9.0460600 0.7914884 0.878 0.0005903 9.0999000 0.7951353 0.880 0.0005370 9.1536210 0.7987822 0.883 0.0004885 9.2072460 0.8024415 0.885 0.0004439 9.2611900 0.8061296 0.888 0.0004033 9.3149490 0.8098177 0.890 0.0003662 9.3688210 0.8135369 0.893 0.0003321 9.4231720 0.8172915 0.895 0.0003011 9.4773340 0.8210462 0.898 0.0002727 9.5317580 0.8248419 0.900 0.0002469 9.5864020 0.8286568 0.903 0.0002234 9.6408540 0.8324717 0.905 0.0002020 9.6957280 0.8363384 0.908 0.0001825 9.7505800 0.8402083 0.910 0.0001648 9.8053540 0.8440944 0.913 0.0001487 9.8607450 0.8480320 0.915 0.0001340 9.9159820 0.8519697 0.918 0.0001208 9.9712720 0.8559327 0.920 0.0001088 10.0266900 0.8599140 0.923 0.0000980 10.0819200 0.8638953 0.925 0.0000881 10.1377600 0.8679458 0.928 0.0000791 10.1936200 0.8719994 0.930 0.0000711 10.2494400 0.8760729 0.933 0.0000638 10.3056300 0.8801795 0.935 0.0000572 10.3616200 0.8842861 0.938 0.0000513 10.4178300 0.8884311 0.940 0.0000460 10.4740400 0.8925840 0.943 0.0000412 10.5301900 0.8967583 0.945 0.0000368 10.5869000 0.9009797 0.948 0.0000329 10.6434100 0.9052011 0.950 0.0000294 10.7003500 0.9094815 0.953 0.0000262 10.7574100 0.9137736 0.955 0.0000234 10.8144000 0.9180851 0.958 0.0000208 10.8716900 0.9224256 0.960 0.0000186 10.9287700 0.9267661 0.963 0.0000165 10.9862000 0.9311582 0.965 0.0000147 11.0435400 0.9355507 0.968 0.0000131 11.1010600 0.9399859 0.970 0.0000116 11.1588400 0.9444484 0.973 0.0000103 11.2165000 0.9489248 0.975 0.0000091 11.2745400 0.9534390 0.978 0.0000081 11.3324000 0.9579532 0.980 0.0000072 11.3907800 0.9625390 0.983 0.0000064 11.4491500 0.9671284 0.985 0.0000056 11.5075700 0.9717468 0.988 0.0000050 11.5660400 0.9763794 0.990 0.0000044 11.6245100 0.9810431 0.993 0.0000039 11.6834600 0.9857505 0.995 0.0000034 11.7422500 0.9904692 0.998 0.0000030 11.8014400 0.9952346 1.000 0.0000026 11.8604700 1.0000000
python/phase_diagram_data_collection.py 0 → 100644 +234 −0 Original line number Diff line number Diff line from dwave.cloud import Client from Embeddings import tilt_embedding, half_cell_embedding from Drawing import * from Drivers import * from Analysis import * import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib.ticker import FormatStrFormatter import seaborn as sns import pandas as pd import numpy as np import pickle import networkx as nx from itertools import product import os import warnings warnings.filterwarnings('ignore') from concurrent.futures import as_completed, ProcessPoolExecutor # Get the solver #ep = '44.230.13.66/sapi' ep = '54.201.1.180/sapi' endpoint = 'https://{}/'.format(ep) client = Client.from_config(endpoint=endpoint,permissive_ssl=True) client.session.headers.update({'host': 'cloud.dwavesys.com'}) solver = client.get_solver('DW_2000Q_5') client.close() # Build the embedding unpruned_embedding = half_cell_embedding(solver=solver, shift=0) emb = prune_shastry_sutherland(unpruned_embedding) ax, pos = draw_logical(emb, shift=0, reference='square') initial_state = FM_initial_state(emb,solver) init_state_name = 'FM' J1_mag = 1 factor_step = 0.2 h_step = 0.04 factors = np.round(np.arange(0,3 + factor_step,factor_step),5) h_targets = np.round(np.arange(-2,0 + h_step,h_step),5) mesh_points = list(product(factors,h_targets)) raw_inputs = [(J1_mag,J1_mag*factor,-4,h) for factor,h in mesh_points] chi = -0.03 inputs = [get_inputs_from_logical(J1, J2, J3, h, chi) for (J1,J2,J3,h) in raw_inputs] check_inputs_ranges(inputs) J_inputs = [] for (J1,J2,J3,h) in inputs: if (J1,J2,J3) in J_inputs: pass else: J_inputs.append((J1,J2,J3)) shift = 0 emb_name = 'halfcell' sampling_parameters = {'annealing_time': 500, 'num_reads': 500, 'readout_thermalization': 500, 'answer_mode': 'histogram', 'auto_scale': False } fbos={} t_pause=1998 def parameter_gen(s): return {'num_reads': 100, 'readout_thermalization': 100, 'answer_mode': 'raw', 'reinitialize_state' : False, 'anneal_schedule' : [[0,1],[1, s],[t_pause+1,s],[t_pause+2,1]], 'initial_state' : initial_state, 'auto_scale': False} s = 0.4 mode=2 field_steps = [*([0.04]*100), *([0.02]*100), *([0.01]*100)] h_mags = [inp[3] for inp in inputs] anneal_mode = 'quench' if mode==2 else 'ramp' init_state_name = 'FM' boundary_iters = 400 fbo_iters = 200 def run_save_fbo(J1,J2,J3): try: bias_df = pd.read_pickle('./fbo_data/Phase_Diagram/calibration_fbo_{J1}_{J2}_{J3}_{emb}_{shift}.p'.format(J1=J1, J2=J2, J3=J3,emb=emb_name, shift=shift)) mag_df = pd.read_pickle('./fbo_data/Phase_Diagram/calibration_mag_{J1}_{J2}_{J3}_{emb}_{shift}.p'.format(J1=J1, J2=J2, J3=J3,emb=emb_name, shift=shift)) fbo = bias_df.iloc[-1,:].values.tolist() del bias_df del mag_df return fbo except: pass with Client.from_config(endpoint=endpoint,connection_close=True,permissive_ssl=True,request_timeout=360) as client: client.session.headers.update({'host': 'cloud.dwavesys.com'}) solver = client.get_solver('DW_2000Q_5') bias_df, mag_df, used_qubits = calibrate_offsets(emb, solver, sampling_parameters, J1, J2, J3, iters=fbo_iters) bias_df.to_pickle('./fbo_data/Phase_Diagram/calibration_fbo_{J1}_{J2}_{J3}_{emb}_{shift}.p'.format(J1=J1, J2=J2, J3=J3,emb=emb_name, shift=shift)) mag_df.to_pickle('./fbo_data/Phase_Diagram/calibration_mag_{J1}_{J2}_{J3}_{emb}_{shift}.p'.format(J1=J1, J2=J2, J3=J3,emb=emb_name, shift=shift)) fbo = bias_df.iloc[-1,:].values.tolist() del bias_df del mag_df del used_qubits return fbo def run_boundary_shim(J1,J2,J3,h,fbos): savename = './RA_Data/Phase_Diagram/{J1}_{J2}_{J3}_{h}_{anneal_mode}_{s}_{init_state_name}_{emb_name}'.format(J1=J1,J2=J2, J3=J3, \ h=h, anneal_mode='quench',s=0.4, init_state_name='FM',\ emb_name='halfcell') directory_path = './RA_Data/Phase_Diagram/' basename = '{J1}_{J2}_{J3}_{h}_{anneal_mode}_{s}_{init_state_name}_{emb_name}.npz'.format(J1=J1,J2=J2, J3=J3, \ h=h, anneal_mode='quench',s=0.4, init_state_name='FM',\ emb_name='halfcell') savename = directory_path+basename print('({},{},{},{}) Boundary Shim Started'.format(J1,J2,J3,h)) if basename in os.listdir(directory_path): return (0,0) with Client.from_config(endpoint=endpoint,connection_close=True,permissive_ssl=True,request_timeout=360) as client: client.session.headers.update({'host': 'cloud.dwavesys.com'}) solver = client.get_solver('DW_2000Q_5') result = boundary_shim_driver(emb, solver, flux_biases=fbos, sampling_parameters=parameter_gen(s), \ J1_mag=J1, J2_mag=J2, J3_mag=J3, h_mag = h, max_iters=boundary_iters, \ field_steps=field_steps, burn_in=50, num_calls=10, num_final_calls=100, grad_tol=0.0005) np.savez(savename, fields=result[0].values, mags=result[1].values, qubits=result[2], solution_array=result[3]) del result #print('saved') return (0,0) if __name__ == '__main__': n_procs = len(J_inputs) if len(J_inputs)<10 else 10 with ProcessPoolExecutor(n_procs) as executor: results = [executor.submit(run_save_fbo,*x) for x in J_inputs] print('Start FBO Calibration') print('--------------------------------\n') for (x,result) in zip(J_inputs,results): result._x = x for r in as_completed(results): if r.exception: print(r.exception()) result = r.result() for h in h_mags: fbos[(*r._x,h)] = result print('{} FBO Calibration Completed'.format(r._x)) print('--------------------------------\n') print('End FBO Calibration\n') n_procs = len(inputs) if len(inputs)<15 else 15 with ProcessPoolExecutor(n_procs) as executor: results = [executor.submit(run_boundary_shim,*(*x,fbos[x])) for x in inputs] print('Start Boundary Shim') print('--------------------------------\n') for (x,result) in zip(inputs,results): result._x = x for r in as_completed(results): if r.exception(): print(r.exception()) print('Local Error') print('{} Boundary Shim Completed'.format(r._x)) print('--------------------------------\n') print('End Boundary Shim\n') # for (J1,J2,J3,h), fbo in zip(inputs, fbos): # client = Client.from_config(connection_close=True) # solver = client.get_solver('DW_2000Q_5') # savename = './RA_Data/full_calibration/{J1}_{J2}_{J3}_{h}_{anneal_mode}_{s}_{init_state_name}_{emb_name}'.format(J1=J1_mag_target,J2=J2_mag_target, J3=J3_mag_target, \ # h=h_mag_target, anneal_mode=anneal_mode,s=s, init_state_name=init_state_name,\ # emb_name=emb_name) # print(savename.split('/')[-1]+'.npz' in os.listdir('./RA_Data/Phase_Diagram/')) # if savename.split('/')[-1]+'.npz' in os.listdir('./RA_Data/Phase_Diagram/'): # print(savename) # client.close() # continue # field_df, mag_df, qubits, solution = boundary_shim_driver(emb, solver, flux_biases=fbo, sampling_parameters=ra_sampling_parameters, \ # J1_mag=J1_mag_target, J2_mag=J2_mag_target, J3_mag=J3_mag_target, h_mag = h_mag_target, max_iters=400, \ # field_steps=field_steps, burn_in=50, num_calls=10, num_final_calls=200, grad_tol=0.0005) # client.close() # np.savez(savename, fields=field_df.values, mags=mag_df.values, qubits=qubits, solution_array=solution) # Sqs = {} # for J1_mag_target, J2_mag_target, J3_mag_target, h_mag_target in sorted(inputs): # basename = '{J1}_{J2}_{J3}_{h}_{anneal_mode}_{s}_{init_state_name}_{emb_name}'.format(J1=J1_mag_target,J2=J2_mag_target, J3=J3_mag_target, \ # h=h_mag_target, anneal_mode=anneal_mode,s=s, init_state_name=init_state_name,\ # emb_name=emb_name) # if savename.split('/')[-1]+'.npz' in os.listdir('./RA_Data/full_calibration/'): # file = np.load('./RA_Data/Phase_Diagram/' + basename + '.npz') # else: # print(f'{basename} Not Found') # continue # if basename+'.npz' in os.listdir('./Sq_Data/RA/Phase_Diagram'): # Sqs[h_mag_target] = np.load('./Sq_Data/RA/Phase_Diagram/'+basename+'.npz')['Sq'] # print('done') # continue # else: # print(f'{basename} Sq not found') # file = np.load('./RA_Data/full_calibration/' + basename + '.npz') # qubits = file['qubits'] # field_df = pd.DataFrame(columns=qubits, data=file['fields']) # mag_df = pd.DataFrame(columns=qubits, data=file['mags']) # solution_array = file['solution_array'] # file.close() # q1, q2, sq = get_structure_factor(emb, solution_array, positions=pos, num_q_points=100, burn_in=50) # Sqs[h_mag_target] = sq # print(basename) # np.savez_compressed('./Sq_Data/RA/full_calibration/'+basename+'.npz', q1=q1, q2=q2, Sq=sq)
