A Quantum Approximate Optimization Algorithm with Metalearning for MaxCut Problem and Its Simulation via TensorFlow Quantum

<div>Parameterized quantum circuit, with unitaries <span class="nowrap"><svg height="19.6048pt" id="M97" style="vertical-align:-5.528899pt" version="1.1" viewbox="-0.0498162 -14.0759 132.482 19.6048" width="132.482pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M715 650H476L470 622C550 619 562 610 562 570C562 536 553 481 535 384L523 321C485 121 408 37 283 37C178 37 99 109 134 297L178 532C193 612 199 616 278 622L284 650H29L23 622C105 616 109 609 94 532L48 291C23 160 51 83 98 40C143 -1 202 -16 260 -16C321 -16 387 3 439 49C509 111 542 204 563 312L577 384C595 479 608 533 619 569C629 603 637 620 709 622L715 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,8.606,3.132)"><path d="M400 606C400 634 383 656 353 656C316 656 294 620 294 593C294 564 317 545 343 545C375 545 400 573 400 606ZM366 351C379 413 381 451 356 451C323 451 251 408 183 341L199 313C223 335 267 365 277 365C285 365 284 354 277 312C245 132 222 27 193 -100C182 -148 160 -188 131 -188C113 -188 90 -178 75 -170C64 -164 55 -168 48 -175C38 -185 24 -203 24 -222S48 -257 71 -257C89 -257 131 -241 186 -192C243 -141 286 -46 310 74L366 351Z"></path></g><g transform="matrix(.013,0,0,-0.013,13.032,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path></g><g transform="matrix(.013,0,0,-0.013,17.53,0)"><path d="M471 401C471 442 455 448 440 448C421 448 389 439 366 424C314 390 233 331 165 242H163L187 345C193 372 197 393 197 409C197 435 189 448 174 448C146 448 80 408 23 349L40 327C64 351 97 373 107 373C115 373 118 366 111 337L29 -4L36 -12C56 -4 83 3 110 9C123 69 136 126 150 172C227 283 336 381 377 381C392 381 396 370 379 298L326 74C290 -77 273 -172 273 -197C273 -219 316 -261 346 -261L353 -246C348 -227 352 -174 374 -66L457 310C467 352 471 381 471 401Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,23.627,3.132)"><path d="M400 606C400 634 383 656 353 656C316 656 294 620 294 593C294 564 317 545 343 545C375 545 400 573 400 606ZM366 351C379 413 381 451 356 451C323 451 251 408 183 341L199 313C223 335 267 365 277 365C285 365 284 354 277 312C245 132 222 27 193 -100C182 -148 160 -188 131 -188C113 -188 90 -178 75 -170C64 -164 55 -168 48 -175C38 -185 24 -203 24 -222S48 -257 71 -257C89 -257 131 -241 186 -192C243 -141 286 -46 310 74L366 351Z"></path></g><g transform="matrix(.013,0,0,-0.013,28.053,0)"><path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z"></path></g><g transform="matrix(.013,0,0,-0.013,33.196,0)"><path d="M478 372C478 418 458 448 431 448C409 448 389 431 389 410C389 404 391 400 394 395C398 388 406 371 406 348C406 253 308 122 251 51H249C254 122 249 257 231 336C212 421 189 448 159 448C126 448 75 412 23 327L48 306C83 354 103 371 115 371C125 371 134 360 144 334C185 224 192 64 183 -19C146 -100 116 -202 110 -244L125 -261C154 -259 208 -234 222 -220C222 -194 225 -84 235 -23C247 -3 273 36 308 79C379 165 478 288 478 372Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,38.864,3.132)"><path d="M400 606C400 634 383 656 353 656C316 656 294 620 294 593C294 564 317 545 343 545C375 545 400 573 400 606ZM366 351C379 413 381 451 356 451C323 451 251 408 183 341L199 313C223 335 267 365 277 365C285 365 284 354 277 312C245 132 222 27 193 -100C182 -148 160 -188 131 -188C113 -188 90 -178 75 -170C64 -164 55 -168 48 -175C38 -185 24 -203 24 -222S48 -257 71 -257C89 -257 131 -241 186 -192C243 -141 286 -46 310 74L366 351Z"></path></g><g transform="matrix(.013,0,0,-0.013,43.29,0)"><path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path></g><g transform="matrix(.013,0,0,-0.013,51.419,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"></path></g><g transform="matrix(.013,0,0,-0.013,62.682,0)"><path d="M391 364C391 409 353 448 295 448C249 448 198 426 152 393C65 331 23 225 23 139C23 14 96 -12 146 -12C198 -12 280 9 367 101L351 124C300 78 242 48 194 48C129 48 109 107 109 162V191C208 213 391 266 391 364ZM313 350C313 305 268 261 113 223C132 334 187 381 217 398C227 404 244 405 261 405C290 405 313 385 313 350Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,68.064,-5.741)"><path d="M556 236V289H56V236H556Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,73.624,-5.741)"><path d="M250 606C250 634 233 656 203 656C168 656 146 618 146 593C146 564 169 545 192 545C227 545 250 573 250 606ZM227 95L212 119C187 98 152 71 135 71C129 71 128 78 134 102L207 373C219 418 217 451 194 451C165 451 92 411 30 351L44 326C77 353 106 371 114 371C124 371 121 357 117 341L55 97C32 5 46 -12 70 -12C108 -12 191 51 227 95Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,76.173,-5.741)"><path d="M478 400C478 445 461 451 447 451C425 451 399 442 369 425C316 395 241 339 172 252H170L191 340C198 371 203 395 203 412C203 439 194 451 178 451C147 451 81 411 24 351L43 324C68 349 101 371 111 371C117 371 122 363 115 334L35 -4L42 -11C61 -4 87 3 117 9C130 72 144 130 158 181C241 296 342 379 383 379C398 379 404 370 384 285L330 60C302 -58 282 -164 282 -190S323 -257 354 -257L361 -243C357 -223 359 -177 382 -73L464 303C474 347 478 378 478 400Z"></path></g><g transform="matrix(.0065,0,0,-0.0065,80.513,-3.641)"><path d="M405 606C405 634 387 657 355 657C316 657 294 621 294 593C294 563 319 543 346 543C377 543 405 573 405 606ZM371 354C383 412 387 454 360 454C324 454 253 412 182 345L199 312C221 333 269 364 278 364C288 364 287 354 279 310L195 -97C185 -139 166 -179 137 -179C119 -179 94 -169 79 -164C68 -160 59 -160 52 -168C42 -177 25 -197 25 -216C25 -238 50 -253 74 -253C91 -253 124 -242 189 -187C255 -131 292 -40 316 81L371 354Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,85.185,-7.714)"><path d="M686 564C571 601 458 635 356 679H331C229 635 115 600 0 564L11 533L343 603L675 533L686 564Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,83.856,-5.741)"><path d="M870 650H600L593 618C681 609 684 605 669 516L641 367H261L286 516C302 603 313 610 391 618L398 650H132L123 618C214 610 218 606 200 516L127 130C113 46 107 40 25 32L18 0H290L298 32C204 40 200 42 214 130L250 321H631L596 131C580 47 573 39 484 32L475 0H753L762 32C673 39 668 46 685 130L757 516C772 606 778 610 861 618L870 650Z"></path></g><g transform="matrix(.0065,0,0,-0.0065,90.972,-3.641)"><path d="M1014 650H832L496 153L362 650H172L164 615C268 606 273 598 247 505L192 305C160 190 138 130 122 98C103 60 79 40 17 35L13 0H260L268 35C182 44 180 54 187 98C193 133 204 189 228 276L303 544H305L438 9H467L816 548H818L760 143C746 43 743 41 658 35L649 0H928L932 35C843 41 834 44 846 145L893 534C901 601 909 609 1005 615L1014 650Z"></path></g><g transform="matrix(.013,0,0,-0.013,98.592,0)"><path d="M391 364C391 409 353 448 295 448C249 448 198 426 152 393C65 331 23 225 23 139C23 14 96 -12 146 -12C198 -12 280 9 367 101L351 124C300 78 242 48 194 48C129 48 109 107 109 162V191C208 213 391 266 391 364ZM313 350C313 305 268 261 113 223C132 334 187 381 217 398C227 404 244 405 261 405C290 405 313 385 313 350Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,103.974,-5.741)"><path d="M556 236V289H56V236H556Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,109.534,-5.741)"><path d="M250 606C250 634 233 656 203 656C168 656 146 618 146 593C146 564 169 545 192 545C227 545 250 573 250 606ZM227 95L212 119C187 98 152 71 135 71C129 71 128 78 134 102L207 373C219 418 217 451 194 451C165 451 92 411 30 351L44 326C77 353 106 371 114 371C124 371 121 357 117 341L55 97C32 5 46 -12 70 -12C108 -12 191 51 227 95Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,112.083,-5.741)"><path d="M493 373C493 420 472 451 445 451C421 451 400 435 400 410C400 404 402 400 406 394S417 371 417 350C417 259 317 128 256 55H254C260 126 256 262 238 340C219 424 194 451 163 451C128 451 78 413 24 329L52 302C89 353 106 370 121 370C129 370 137 363 147 336C186 231 196 73 184 -16C152 -88 120 -192 112 -238L130 -257C157 -253 213 -227 231 -213C231 -187 234 -77 240 -23C251 -6 277 30 309 69C424 208 493 299 493 373Z"></path></g><g transform="matrix(.0065,0,0,-0.0065,116.196,-3.641)"><path d="M405 606C405 634 387 657 355 657C316 657 294 621 294 593C294 563 319 543 346 543C377 543 405 573 405 606ZM371 354C383 412 387 454 360 454C324 454 253 412 182 345L199 312C221 333 269 364 278 364C288 364 287 354 279 310L195 -97C185 -139 166 -179 137 -179C119 -179 94 -169 79 -164C68 -160 59 -160 52 -168C42 -177 25 -197 25 -216C25 -238 50 -253 74 -253C91 -253 124 -242 189 -187C255 -131 292 -40 316 81L371 354Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,120.868,-7.714)"><path d="M686 564C571 601 458 635 356 679H331C229 635 115 600 0 564L11 533L343 603L675 533L686 564Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,119.539,-5.741)"><path d="M870 650H600L593 618C681 609 684 605 669 516L641 367H261L286 516C302 603 313 610 391 618L398 650H132L123 618C214 610 218 606 200 516L127 130C113 46 107 40 25 32L18 0H290L298 32C204 40 200 42 214 130L250 321H631L596 131C580 47 573 39 484 32L475 0H753L762 32C673 39 668 46 685 130L757 516C772 606 778 610 861 618L870 650Z"></path></g><g transform="matrix(.0065,0,0,-0.0065,126.655,-3.641)"><path d="M698 629C658 643 581 667 489 667C226 667 25 529 25 293C25 92 179 -17 373 -17C455 -17 544 -1 580 10C604 32 651 130 669 174L635 195C569 88 505 25 390 25C233 25 134 137 134 299C134 468 260 621 466 621C589 621 647 583 654 458L694 462L698 629Z"></path></g></svg>,</span> input state <span class="nowrap"><svg height="11.439pt" id="M98" style="vertical-align:-2.15067pt" version="1.1" viewbox="-0.0498162 -9.28833 15.184 11.439" width="15.184pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M162 -163V703H101V-163H162Z"></path></g><g transform="matrix(.013,0,0,-0.013,3.419,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path></g><g transform="matrix(.013,0,0,-0.013,9.659,0)"><path d="M307 267V273L115 703H53L246 270L53 -163H115L307 267Z"></path></g></svg>,</span> and classical register <span class="nowrap"><svg height="6.1673pt" id="M99" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -5.96091 5.39742 6.1673" width="5.39742pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M383 397C383 424 344 448 299 448C244 448 172 409 132 375C66 319 23 227 23 146C23 42 74 -12 146 -12C208 -12 298 30 359 103L343 124C315 95 248 48 192 48C145 48 111 85 111 163C111 228 129 294 151 330C171 363 201 401 241 401C275 401 302 384 325 356C332 347 339 344 348 348C373 360 383 381 383 397Z"></path></g></svg>.</span></div>

Mathematical Problems in Engineering

fig3

Figure 3

Figure 3: A Quantum Approximate Optimization Algorithm with Metalearning for MaxCut Problem and Its Simulation via TensorFlow Quantum 