microsoft/qdk
Publicmirrored from https://github.com/microsoft/qdkAvailable
source/pip/qsharp/estimator/_estimator.py
1082lines · modecode
| 1 | # Copyright (c) Microsoft Corporation. |
| 2 | # Licensed under the MIT License. |
| 3 | import re |
| 4 | from typing import Any, Dict, List, Optional, Union |
| 5 | from dataclasses import dataclass, field |
| 6 | from .._native import physical_estimates |
| 7 | |
| 8 | import json |
| 9 | |
| 10 | try: |
| 11 | # Both markdown and mdx_math (from python-markdown-math) must be present for our markdown |
| 12 | # rendering logic to work. If either is missing, we'll fall back to plain text. |
| 13 | import markdown |
| 14 | import mdx_math |
| 15 | |
| 16 | has_markdown = True |
| 17 | except ImportError: |
| 18 | has_markdown = False |
| 19 | |
| 20 | |
| 21 | class EstimatorError(BaseException): |
| 22 | """ |
| 23 | An error returned from the resource estimation. |
| 24 | """ |
| 25 | |
| 26 | def __init__(self, code: str, message: str): |
| 27 | self.message = f"Error estimating resources ({code}):\n{message}" |
| 28 | self.code = code |
| 29 | |
| 30 | def __str__(self): |
| 31 | return self.message |
| 32 | |
| 33 | |
| 34 | @dataclass |
| 35 | class AutoValidatingParams: |
| 36 | """ |
| 37 | A helper class for target parameters. |
| 38 | |
| 39 | It has a function as_dict that automatically extracts a dictionary from |
| 40 | the class' fields. They are added to the result dictionary if their value |
| 41 | is not None, the key is automatically transformed from Python snake case |
| 42 | to camel case, and if validate is True and if the field has a validation |
| 43 | function, the field is validated beforehand. |
| 44 | """ |
| 45 | |
| 46 | def as_dict(self, validate=True): |
| 47 | result = {} |
| 48 | |
| 49 | for name, field in self.__dataclass_fields__.items(): |
| 50 | field_value = self.__getattribute__(name) |
| 51 | if field_value is not None: |
| 52 | # validate field? |
| 53 | if validate and "validate" in field.metadata: |
| 54 | func = field.metadata["validate"] |
| 55 | # check for indirect call (like in @staticmethod) |
| 56 | if hasattr(func, "__func__"): |
| 57 | func = func.__func__ |
| 58 | func(name, field_value) |
| 59 | |
| 60 | # translate field name to camel case |
| 61 | s = re.sub(r"(_|-)+", " ", name).title().replace(" ", "") |
| 62 | attribute = "".join([s[0].lower(), s[1:]]) |
| 63 | result[attribute] = field_value |
| 64 | |
| 65 | if validate: |
| 66 | self.post_validation(result) |
| 67 | |
| 68 | return result |
| 69 | |
| 70 | def post_validation(self, result): |
| 71 | """ |
| 72 | A function that is called after all individual fields have been |
| 73 | validated, but before the result is returned. |
| 74 | |
| 75 | Here result is the current dictionary. |
| 76 | """ |
| 77 | pass |
| 78 | |
| 79 | |
| 80 | def validating_field(validation_func, default=None): |
| 81 | """ |
| 82 | A helper method to declare field for an AutoValidatingParams data class. |
| 83 | """ |
| 84 | return field(default=default, metadata={"validate": validation_func}) |
| 85 | |
| 86 | |
| 87 | class QubitParams: |
| 88 | GATE_US_E3 = "qubit_gate_us_e3" |
| 89 | GATE_US_E4 = "qubit_gate_us_e4" |
| 90 | GATE_NS_E3 = "qubit_gate_ns_e3" |
| 91 | GATE_NS_E4 = "qubit_gate_ns_e4" |
| 92 | MAJ_NS_E4 = "qubit_maj_ns_e4" |
| 93 | MAJ_NS_E6 = "qubit_maj_ns_e6" |
| 94 | |
| 95 | |
| 96 | class QECScheme: |
| 97 | SURFACE_CODE = "surface_code" |
| 98 | FLOQUET_CODE = "floquet_code" |
| 99 | |
| 100 | |
| 101 | def _check_error_rate(name, value): |
| 102 | if value <= 0.0 or value >= 1.0: |
| 103 | raise ValueError(f"{name} must be between 0 and 1") |
| 104 | |
| 105 | |
| 106 | def _check_error_rate_or_process_and_readout(name, value): |
| 107 | if value is None: |
| 108 | return |
| 109 | |
| 110 | if isinstance(value, float): |
| 111 | _check_error_rate(name, value) |
| 112 | return |
| 113 | |
| 114 | if not isinstance(value, MeasurementErrorRate): |
| 115 | raise ValueError( |
| 116 | f"{name} must be either a float or " |
| 117 | "MeasurementErrorRate with two fields: 'process' and 'readout'" |
| 118 | ) |
| 119 | |
| 120 | |
| 121 | def check_time(name, value): |
| 122 | pat = r"^(\+?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?)\s*(s|ms|μs|µs|us|ns)$" |
| 123 | if re.match(pat, value) is None: |
| 124 | raise ValueError( |
| 125 | f"{name} is not a valid time string; use a " "suffix s, ms, us, or ns" |
| 126 | ) |
| 127 | |
| 128 | |
| 129 | @dataclass |
| 130 | class MeasurementErrorRate(AutoValidatingParams): |
| 131 | process: float = field(metadata={"validate": _check_error_rate}) |
| 132 | readout: float = field(metadata={"validate": _check_error_rate}) |
| 133 | |
| 134 | |
| 135 | @dataclass |
| 136 | class EstimatorQubitParams(AutoValidatingParams): |
| 137 | @staticmethod |
| 138 | def check_instruction_set(name, value): |
| 139 | if value not in [ |
| 140 | "gate-based", |
| 141 | "gate_based", |
| 142 | "GateBased", |
| 143 | "gateBased", |
| 144 | "Majorana", |
| 145 | "majorana", |
| 146 | ]: |
| 147 | raise ValueError(f"{name} must be GateBased or Majorana") |
| 148 | |
| 149 | name: Optional[str] = None |
| 150 | instruction_set: Optional[str] = validating_field(check_instruction_set) |
| 151 | one_qubit_measurement_time: Optional[str] = validating_field(check_time) |
| 152 | two_qubit_joint_measurement_time: Optional[str] = validating_field(check_time) |
| 153 | one_qubit_gate_time: Optional[str] = validating_field(check_time) |
| 154 | two_qubit_gate_time: Optional[str] = validating_field(check_time) |
| 155 | t_gate_time: Optional[str] = validating_field(check_time) |
| 156 | one_qubit_measurement_error_rate: Union[None, float, MeasurementErrorRate] = ( |
| 157 | validating_field(_check_error_rate_or_process_and_readout) |
| 158 | ) |
| 159 | two_qubit_joint_measurement_error_rate: Union[None, float, MeasurementErrorRate] = ( |
| 160 | validating_field(_check_error_rate_or_process_and_readout) |
| 161 | ) |
| 162 | one_qubit_gate_error_rate: Optional[float] = validating_field(_check_error_rate) |
| 163 | two_qubit_gate_error_rate: Optional[float] = validating_field(_check_error_rate) |
| 164 | t_gate_error_rate: Optional[float] = validating_field(_check_error_rate) |
| 165 | idle_error_rate: Optional[float] = validating_field(_check_error_rate) |
| 166 | |
| 167 | _default_models = [ |
| 168 | QubitParams.GATE_US_E3, |
| 169 | QubitParams.GATE_US_E4, |
| 170 | QubitParams.GATE_NS_E3, |
| 171 | QubitParams.GATE_NS_E4, |
| 172 | QubitParams.MAJ_NS_E4, |
| 173 | QubitParams.MAJ_NS_E6, |
| 174 | ] |
| 175 | _gate_based = ["gate-based", "gate_based", "GateBased", "gateBased"] |
| 176 | _maj_based = ["Majorana", "majorana"] |
| 177 | |
| 178 | def post_validation(self, result): |
| 179 | # check whether all fields have been specified in case a custom qubit |
| 180 | # model is specified |
| 181 | custom = result != {} and ( |
| 182 | self.name is None or self.name not in self._default_models |
| 183 | ) |
| 184 | |
| 185 | # no further validation needed for non-custom models |
| 186 | if not custom: |
| 187 | return |
| 188 | |
| 189 | # instruction set must be set |
| 190 | if self.instruction_set is None: |
| 191 | raise LookupError( |
| 192 | "instruction_set must be set for custom qubit " "parameters" |
| 193 | ) |
| 194 | |
| 195 | # NOTE at this point, we know that instruction set must have valid |
| 196 | # value |
| 197 | if self.one_qubit_measurement_time is None: |
| 198 | raise LookupError("one_qubit_measurement_time must be set") |
| 199 | if self.one_qubit_measurement_error_rate is None: |
| 200 | raise LookupError("one_qubit_measurement_error_rate must be set") |
| 201 | |
| 202 | # this only needs to be checked for gate based qubits |
| 203 | if self.instruction_set in self._gate_based: |
| 204 | if self.one_qubit_gate_time is None: |
| 205 | raise LookupError("one_qubit_gate_time must be set") |
| 206 | |
| 207 | def as_dict(self, validate=True) -> Dict[str, Any]: |
| 208 | qubit_params = super().as_dict(validate) |
| 209 | if len(qubit_params) != 0: |
| 210 | if isinstance(self.one_qubit_measurement_error_rate, MeasurementErrorRate): |
| 211 | qubit_params["oneQubitMeasurementErrorRate"] = ( |
| 212 | self.one_qubit_measurement_error_rate.as_dict(validate) |
| 213 | ) |
| 214 | |
| 215 | if isinstance( |
| 216 | self.two_qubit_joint_measurement_error_rate, MeasurementErrorRate |
| 217 | ): |
| 218 | qubit_params["twoQubitJointMeasurementErrorRate"] = ( |
| 219 | self.two_qubit_joint_measurement_error_rate.as_dict(validate) |
| 220 | ) |
| 221 | |
| 222 | return qubit_params |
| 223 | |
| 224 | |
| 225 | @dataclass |
| 226 | class EstimatorQecScheme(AutoValidatingParams): |
| 227 | name: Optional[str] = None |
| 228 | error_correction_threshold: Optional[float] = validating_field(_check_error_rate) |
| 229 | crossing_prefactor: Optional[float] = None |
| 230 | distance_coefficient_power: Optional[int] = None |
| 231 | logical_cycle_time: Optional[str] = None |
| 232 | physical_qubits_per_logical_qubit: Optional[str] = None |
| 233 | max_code_distance: Optional[int] = None |
| 234 | |
| 235 | |
| 236 | @dataclass |
| 237 | class ProtocolSpecificDistillationUnitSpecification(AutoValidatingParams): |
| 238 | num_unit_qubits: Optional[int] = None |
| 239 | duration_in_qubit_cycle_time: Optional[int] = None |
| 240 | |
| 241 | def post_validation(self, result): |
| 242 | if self.num_unit_qubits is None: |
| 243 | raise LookupError("num_unit_qubits must be set") |
| 244 | |
| 245 | if self.duration_in_qubit_cycle_time is None: |
| 246 | raise LookupError("duration_in_qubit_cycle_time must be set") |
| 247 | |
| 248 | |
| 249 | @dataclass |
| 250 | class DistillationUnitSpecification(AutoValidatingParams): |
| 251 | name: Optional[str] = None |
| 252 | display_name: Optional[str] = None |
| 253 | num_input_ts: Optional[int] = None |
| 254 | num_output_ts: Optional[int] = None |
| 255 | failure_probability_formula: Optional[str] = None |
| 256 | output_error_rate_formula: Optional[str] = None |
| 257 | physical_qubit_specification: Optional[ |
| 258 | ProtocolSpecificDistillationUnitSpecification |
| 259 | ] = None |
| 260 | logical_qubit_specification: Optional[ |
| 261 | ProtocolSpecificDistillationUnitSpecification |
| 262 | ] = None |
| 263 | logical_qubit_specification_first_round_override: Optional[ |
| 264 | ProtocolSpecificDistillationUnitSpecification |
| 265 | ] = None |
| 266 | |
| 267 | def has_custom_specification(self): |
| 268 | return ( |
| 269 | self.display_name is not None |
| 270 | or self.num_input_ts is not None |
| 271 | or self.num_output_ts is not None |
| 272 | or self.failure_probability_formula is not None |
| 273 | or self.output_error_rate_formula is not None |
| 274 | or self.physical_qubit_specification is not None |
| 275 | or self.logical_qubit_specification is not None |
| 276 | or self.logical_qubit_specification_first_round_override is not None |
| 277 | ) |
| 278 | |
| 279 | def has_predefined_name(self): |
| 280 | return self.name is not None |
| 281 | |
| 282 | def post_validation(self, result): |
| 283 | if not self.has_custom_specification() and not self.has_predefined_name(): |
| 284 | raise LookupError( |
| 285 | "name must be set or custom specification must be provided" |
| 286 | ) |
| 287 | |
| 288 | if self.has_custom_specification() and self.has_predefined_name(): |
| 289 | raise LookupError( |
| 290 | "If predefined name is provided, " |
| 291 | "custom specification is not allowed. " |
| 292 | "Either remove name or remove all other " |
| 293 | "specification of the distillation unit" |
| 294 | ) |
| 295 | |
| 296 | if self.has_predefined_name(): |
| 297 | return # all other validation is on the server side |
| 298 | |
| 299 | if self.num_input_ts is None: |
| 300 | raise LookupError("num_input_ts must be set") |
| 301 | |
| 302 | if self.num_output_ts is None: |
| 303 | raise LookupError("num_output_ts must be set") |
| 304 | |
| 305 | if self.failure_probability_formula is None: |
| 306 | raise LookupError("failure_probability_formula must be set") |
| 307 | |
| 308 | if self.output_error_rate_formula is None: |
| 309 | raise LookupError("output_error_rate_formula must be set") |
| 310 | |
| 311 | if self.physical_qubit_specification is not None: |
| 312 | self.physical_qubit_specification.post_validation(result) |
| 313 | |
| 314 | if self.logical_qubit_specification is not None: |
| 315 | self.logical_qubit_specification.post_validation(result) |
| 316 | |
| 317 | if self.logical_qubit_specification_first_round_override is not None: |
| 318 | self.logical_qubit_specification_first_round_override.post_validation( |
| 319 | result |
| 320 | ) |
| 321 | |
| 322 | def as_dict(self, validate=True) -> Dict[str, Any]: |
| 323 | specification_dict = super().as_dict(validate) |
| 324 | if len(specification_dict) != 0: |
| 325 | if self.physical_qubit_specification is not None: |
| 326 | physical_qubit_specification_dict = ( |
| 327 | self.physical_qubit_specification.as_dict(validate) |
| 328 | ) |
| 329 | if len(physical_qubit_specification_dict) != 0: |
| 330 | specification_dict["physicalQubitSpecification"] = ( |
| 331 | physical_qubit_specification_dict |
| 332 | ) |
| 333 | |
| 334 | if self.logical_qubit_specification is not None: |
| 335 | logical_qubit_specification_dict = ( |
| 336 | self.logical_qubit_specification.as_dict(validate) |
| 337 | ) |
| 338 | if len(logical_qubit_specification_dict) != 0: |
| 339 | specification_dict["logicalQubitSpecification"] = ( |
| 340 | logical_qubit_specification_dict |
| 341 | ) |
| 342 | |
| 343 | if self.logical_qubit_specification_first_round_override is not None: |
| 344 | logical_qubit_specification_first_round_override_dict = ( |
| 345 | self.logical_qubit_specification_first_round_override.as_dict( |
| 346 | validate |
| 347 | ) |
| 348 | ) |
| 349 | if len(logical_qubit_specification_first_round_override_dict) != 0: |
| 350 | specification_dict[ |
| 351 | "logicalQubitSpecificationFirstRoundOverride" |
| 352 | ] = logical_qubit_specification_first_round_override_dict |
| 353 | |
| 354 | return specification_dict |
| 355 | |
| 356 | |
| 357 | @dataclass |
| 358 | class ErrorBudgetPartition(AutoValidatingParams): |
| 359 | logical: float = 0.001 / 3 |
| 360 | t_states: float = 0.001 / 3 |
| 361 | rotations: float = 0.001 / 3 |
| 362 | |
| 363 | |
| 364 | @dataclass |
| 365 | class EstimatorConstraints(AutoValidatingParams): |
| 366 | @staticmethod |
| 367 | def at_least_one(name, value): |
| 368 | if value < 1: |
| 369 | raise ValueError(f"{name} must be at least 1") |
| 370 | |
| 371 | logical_depth_factor: Optional[float] = validating_field(at_least_one) |
| 372 | max_t_factories: Optional[int] = validating_field(at_least_one) |
| 373 | max_duration: Optional[int] = validating_field(check_time) |
| 374 | max_physical_qubits: Optional[int] = validating_field(at_least_one) |
| 375 | |
| 376 | def post_validation(self, result): |
| 377 | if self.max_duration is not None and self.max_physical_qubits is not None: |
| 378 | raise LookupError( |
| 379 | "Both duration and number of physical qubits constraints are provided, but only one is allowe at a time." |
| 380 | ) |
| 381 | |
| 382 | |
| 383 | class EstimatorInputParamsItem: |
| 384 | """ |
| 385 | Input params for microsoft.estimator target |
| 386 | |
| 387 | :ivar error_budget Total error budget for execution of the algorithm |
| 388 | """ |
| 389 | |
| 390 | def __init__(self): |
| 391 | super().__init__() |
| 392 | |
| 393 | self.qubit_params: EstimatorQubitParams = EstimatorQubitParams() |
| 394 | self.qec_scheme: EstimatorQecScheme = EstimatorQecScheme() |
| 395 | self.distillation_unit_specifications = ( |
| 396 | [] |
| 397 | ) # type: List[DistillationUnitSpecification] |
| 398 | self.constraints: EstimatorConstraints = EstimatorConstraints() |
| 399 | self.error_budget: Optional[Union[float, ErrorBudgetPartition]] = None |
| 400 | self.estimate_type: Optional[str] = None |
| 401 | |
| 402 | def as_dict(self, validate=True, additional_params=None) -> Dict[str, Any]: |
| 403 | result = {} |
| 404 | |
| 405 | qubit_params = self.qubit_params.as_dict(validate) |
| 406 | if len(qubit_params) != 0: |
| 407 | result["qubitParams"] = qubit_params |
| 408 | elif hasattr(additional_params, "qubit_params"): |
| 409 | qubit_params = additional_params.qubit_params.as_dict(validate) |
| 410 | if len(qubit_params) != 0: |
| 411 | result["qubitParams"] = qubit_params |
| 412 | |
| 413 | qec_scheme = self.qec_scheme.as_dict(validate) |
| 414 | if len(qec_scheme) != 0: |
| 415 | result["qecScheme"] = qec_scheme |
| 416 | elif hasattr(additional_params, "qec_scheme"): |
| 417 | qec_scheme = additional_params.qec_scheme.as_dict(validate) |
| 418 | if len(qec_scheme) != 0: |
| 419 | result["qecScheme"] = qec_scheme |
| 420 | |
| 421 | for specification in self.distillation_unit_specifications: |
| 422 | specification_dict = specification.as_dict(validate) |
| 423 | if len(specification_dict) != 0: |
| 424 | if result.get("distillationUnitSpecifications") is None: |
| 425 | result["distillationUnitSpecifications"] = [] |
| 426 | |
| 427 | result["distillationUnitSpecifications"].append(specification_dict) |
| 428 | if result.get("distillationUnitSpecifications") is not None and hasattr( |
| 429 | additional_params, "distillation_unit_specifications" |
| 430 | ): |
| 431 | for specification in additional_params.distillation_unit_specifications: |
| 432 | specification_dict = specification.as_dict(validate) |
| 433 | if len(specification_dict) != 0: |
| 434 | if result.get("distillationUnitSpecifications") is None: |
| 435 | result["distillationUnitSpecifications"] = [] |
| 436 | |
| 437 | result["distillationUnitSpecifications"].append(specification_dict) |
| 438 | |
| 439 | constraints = self.constraints.as_dict(validate) |
| 440 | if len(constraints) != 0: |
| 441 | result["constraints"] = constraints |
| 442 | elif hasattr(additional_params, "constraints"): |
| 443 | constraints = additional_params.constraints.as_dict(validate) |
| 444 | if len(constraints) != 0: |
| 445 | result["constraints"] = constraints |
| 446 | |
| 447 | if self.error_budget is not None: |
| 448 | if isinstance(self.error_budget, float) or isinstance( |
| 449 | self.error_budget, int |
| 450 | ): |
| 451 | if validate and (self.error_budget <= 0 or self.error_budget >= 1): |
| 452 | message = "error_budget must be value between 0 and 1" |
| 453 | raise ValueError(message) |
| 454 | result["errorBudget"] = self.error_budget |
| 455 | elif isinstance(self.error_budget, ErrorBudgetPartition): |
| 456 | result["errorBudget"] = self.error_budget.as_dict(validate) |
| 457 | elif hasattr(additional_params, "error_budget"): |
| 458 | if isinstance(additional_params.error_budget, float) or isinstance( |
| 459 | additional_params.error_budget, int |
| 460 | ): |
| 461 | if validate and ( |
| 462 | additional_params.error_budget <= 0 |
| 463 | or additional_params.error_budget >= 1 |
| 464 | ): |
| 465 | message = "error_budget must be value between 0 and 1" |
| 466 | raise ValueError(message) |
| 467 | result["errorBudget"] = additional_params.error_budget |
| 468 | elif isinstance(additional_params.error_budget, ErrorBudgetPartition): |
| 469 | result["errorBudget"] = additional_params.error_budget.as_dict(validate) |
| 470 | |
| 471 | if self.estimate_type is not None: |
| 472 | if self.estimate_type not in ["frontier", "singlePoint"]: |
| 473 | raise ValueError( |
| 474 | "estimate_type must be either 'frontier' or 'singlePoint'" |
| 475 | ) |
| 476 | result["estimateType"] = self.estimate_type |
| 477 | |
| 478 | return result |
| 479 | |
| 480 | |
| 481 | class EstimatorParams(EstimatorInputParamsItem): |
| 482 | MAX_NUM_ITEMS: int = 1000 |
| 483 | |
| 484 | def __init__(self, num_items: Optional[int] = None): |
| 485 | EstimatorInputParamsItem.__init__(self) |
| 486 | |
| 487 | if num_items is not None: |
| 488 | self.has_items = True |
| 489 | if num_items <= 0 or num_items > self.MAX_NUM_ITEMS: |
| 490 | raise ValueError( |
| 491 | "num_items must be a positive value less or equal to " |
| 492 | f"{self.MAX_NUM_ITEMS}" |
| 493 | ) |
| 494 | self._items = [EstimatorInputParamsItem() for _ in range(num_items)] |
| 495 | else: |
| 496 | self.has_items = False |
| 497 | |
| 498 | @property |
| 499 | def items(self) -> List: |
| 500 | if self.has_items: |
| 501 | return self._items |
| 502 | else: |
| 503 | raise Exception( |
| 504 | "Cannot access items in a non-batching job, call " |
| 505 | "make_params with num_items parameter" |
| 506 | ) |
| 507 | |
| 508 | def as_dict(self, validate=True) -> Dict[str, Any]: |
| 509 | """ |
| 510 | Constructs a dictionary from the input params. |
| 511 | |
| 512 | For batching jobs, top-level entries are merged into item entries. |
| 513 | Item entries have priority in case they are specified. |
| 514 | """ |
| 515 | |
| 516 | # initialize result and set type hint |
| 517 | result: Dict[str, Any] = EstimatorInputParamsItem.as_dict(self, validate) |
| 518 | |
| 519 | if self.has_items: |
| 520 | result["items"] = [item.as_dict(validate, self) for item in self._items] |
| 521 | # In case of batching, no need to stop if failing an item |
| 522 | result["resumeAfterFailedItem"] = True |
| 523 | |
| 524 | return result |
| 525 | |
| 526 | |
| 527 | class HTMLWrapper: |
| 528 | """ |
| 529 | Simple HTML wrapper to expose _repr_html_ for Jupyter clients. |
| 530 | """ |
| 531 | |
| 532 | def __init__(self, content: str): |
| 533 | self.content = content |
| 534 | |
| 535 | def _repr_html_(self): |
| 536 | return self.content |
| 537 | |
| 538 | |
| 539 | class EstimatorResult(dict): |
| 540 | """ |
| 541 | Microsoft Resource Estimator result. |
| 542 | |
| 543 | The class represents simple resource estimation results as well as batching |
| 544 | resource estimation results. The latter can be indexed by an integer index to |
| 545 | access an individual result from the batching result. |
| 546 | """ |
| 547 | |
| 548 | MAX_DEFAULT_ITEMS_IN_TABLE = 5 |
| 549 | |
| 550 | def __init__(self, data: Union[Dict, List]): |
| 551 | self._error = None |
| 552 | |
| 553 | if isinstance(data, list) and len(data) == 1: |
| 554 | data = data[0] |
| 555 | if not EstimatorResult._is_succeeded(data): |
| 556 | raise EstimatorError(data["code"], data["message"]) |
| 557 | |
| 558 | if isinstance(data, dict): |
| 559 | self._data = data |
| 560 | super().__init__(data) |
| 561 | |
| 562 | self._is_simple = True |
| 563 | if EstimatorResult._is_succeeded(self): |
| 564 | self._repr = self._item_result_table() |
| 565 | self.summary = HTMLWrapper(self._item_result_summary_table()) |
| 566 | self.diagram = EstimatorResultDiagram(self.data().copy()) |
| 567 | else: |
| 568 | self._error = EstimatorError(data["code"], data["message"]) |
| 569 | |
| 570 | elif isinstance(data, list): |
| 571 | super().__init__( |
| 572 | {idx: EstimatorResult(item_data) for idx, item_data in enumerate(data)} |
| 573 | ) |
| 574 | |
| 575 | self._data = data |
| 576 | self._is_simple = False |
| 577 | num_items = len(data) |
| 578 | self._repr = "" |
| 579 | if num_items > self.MAX_DEFAULT_ITEMS_IN_TABLE: |
| 580 | self._repr += ( |
| 581 | "<p><b>Info:</b> <i>The overview table is " |
| 582 | "cut off after " |
| 583 | f"{self.MAX_DEFAULT_ITEMS_IN_TABLE} items. If " |
| 584 | "you want to see all items, suffix the result " |
| 585 | "variable with <code>[:]</code></i></p>" |
| 586 | ) |
| 587 | num_items = self.MAX_DEFAULT_ITEMS_IN_TABLE |
| 588 | self._repr += self._batch_result_table(range(num_items)) |
| 589 | |
| 590 | # Add plot function for batching jobs |
| 591 | self.plot = self._plot |
| 592 | self.summary_data_frame = self._summary_data_frame |
| 593 | |
| 594 | def _is_succeeded(self): |
| 595 | return "status" in self and self["status"] == "success" |
| 596 | |
| 597 | def data(self, idx: Optional[int] = None) -> Any: |
| 598 | """ |
| 599 | Returns raw data of the result object. |
| 600 | |
| 601 | In case of a batching job, you can pass an index to access a specific |
| 602 | item. |
| 603 | """ |
| 604 | if idx is None: |
| 605 | return self._data |
| 606 | elif not self._is_simple: |
| 607 | return self._data[idx] |
| 608 | else: |
| 609 | msg = "Cannot pass parameter 'idx' to 'data' for non-batching job" |
| 610 | raise ValueError(msg) |
| 611 | |
| 612 | @property |
| 613 | def error(self) -> Optional[EstimatorError]: |
| 614 | """ |
| 615 | Returns the error object if the result is an error. |
| 616 | """ |
| 617 | return self._error |
| 618 | |
| 619 | @property |
| 620 | def logical_counts(self): |
| 621 | """ |
| 622 | Returns the logical counts of the result. |
| 623 | """ |
| 624 | if self._is_simple: |
| 625 | return LogicalCounts(self.data()["logicalCounts"]) |
| 626 | else: |
| 627 | return LogicalCounts(self.data(0)["logicalCounts"]) |
| 628 | |
| 629 | def _repr_html_(self): |
| 630 | """ |
| 631 | HTML table representation of the result. |
| 632 | """ |
| 633 | if self._error: |
| 634 | raise self._error |
| 635 | return self._repr |
| 636 | |
| 637 | def __getitem__(self, key): |
| 638 | """ |
| 639 | If the result represents a batching job and key is a slice, a |
| 640 | side-by-side table comparison is shown for the indexes represented by |
| 641 | the slice. |
| 642 | |
| 643 | Otherwise, the key is used to access the raw data directly. |
| 644 | """ |
| 645 | if isinstance(key, slice): |
| 646 | if self._is_simple: |
| 647 | msg = "Cannot pass slice to '__getitem__' for non-batching job" |
| 648 | raise ValueError(msg) |
| 649 | return HTMLWrapper(self._batch_result_table(range(len(self))[key])) |
| 650 | else: |
| 651 | if super().__contains__(key): |
| 652 | return super().__getitem__(key) |
| 653 | elif super().__contains__("frontierEntries"): |
| 654 | return super().__getitem__("frontierEntries")[0].__getitem__(key) |
| 655 | else: |
| 656 | raise KeyError(key) |
| 657 | |
| 658 | def _plot(self, **kwargs): |
| 659 | """ |
| 660 | Plots all result items in a space time plot, where the x-axis shows |
| 661 | total runtime, and the y-axis shows total number of physical qubits. |
| 662 | Both axes are in log-scale. |
| 663 | Attributes: |
| 664 | labels (list): List of labels for the legend. |
| 665 | """ |
| 666 | try: |
| 667 | import matplotlib.pyplot as plt |
| 668 | except ImportError: |
| 669 | raise ImportError( |
| 670 | "Missing optional 'matplotlib' dependency. To install run: " |
| 671 | "pip install matplotlib" |
| 672 | ) |
| 673 | |
| 674 | labels = kwargs.pop("labels", []) |
| 675 | |
| 676 | [xs, ys] = zip( |
| 677 | *[ |
| 678 | ( |
| 679 | self.data(i)["physicalCounts"]["runtime"], |
| 680 | self.data(i)["physicalCounts"]["physicalQubits"], |
| 681 | ) |
| 682 | for i in range(len(self)) |
| 683 | ] |
| 684 | ) |
| 685 | |
| 686 | _ = plt.figure(figsize=(15, 8)) |
| 687 | |
| 688 | plt.ylabel("Physical qubits") |
| 689 | plt.xlabel("Runtime") |
| 690 | plt.loglog() |
| 691 | for i, (x, y) in enumerate(zip(xs, ys)): |
| 692 | if isinstance(labels, list) and i < len(labels): |
| 693 | label = labels[i] |
| 694 | else: |
| 695 | label = str(i) |
| 696 | plt.scatter(x=[x], y=[y], label=label, marker="os+x"[i % 4]) |
| 697 | |
| 698 | nsec = 1 |
| 699 | usec = 1e3 * nsec |
| 700 | msec = 1e3 * usec |
| 701 | sec = 1e3 * msec |
| 702 | min = 60 * sec |
| 703 | hour = 60 * min |
| 704 | day = 24 * hour |
| 705 | week = 7 * day |
| 706 | month = 31 * day |
| 707 | year = 365 * month |
| 708 | decade = 10 * year |
| 709 | century = 10 * decade |
| 710 | |
| 711 | time_units = [ |
| 712 | nsec, |
| 713 | usec, |
| 714 | msec, |
| 715 | sec, |
| 716 | min, |
| 717 | hour, |
| 718 | day, |
| 719 | week, |
| 720 | month, |
| 721 | year, |
| 722 | decade, |
| 723 | century, |
| 724 | ] |
| 725 | time_labels = [ |
| 726 | "1 ns", |
| 727 | "1 µs", |
| 728 | "1 ms", |
| 729 | "1 s", |
| 730 | "1 min", |
| 731 | "1 hour", |
| 732 | "1 day", |
| 733 | "1 week", |
| 734 | "1 month", |
| 735 | "1 year", |
| 736 | "1 decade", |
| 737 | "1 century", |
| 738 | ] |
| 739 | |
| 740 | cutoff = ( |
| 741 | next( |
| 742 | (i for i, x in enumerate(time_units) if x > max(xs)), |
| 743 | len(time_units) - 1, |
| 744 | ) |
| 745 | + 1 |
| 746 | ) |
| 747 | |
| 748 | plt.xticks(time_units[0:cutoff], time_labels[0:cutoff], rotation=90) |
| 749 | plt.legend(loc="upper left") |
| 750 | plt.show() |
| 751 | |
| 752 | @property |
| 753 | def json(self): |
| 754 | """ |
| 755 | Returns a JSON representation of the resource estimation result data. |
| 756 | """ |
| 757 | if not hasattr(self, "_json"): |
| 758 | import json |
| 759 | |
| 760 | self._json = json.dumps(self._data) |
| 761 | |
| 762 | return self._json |
| 763 | |
| 764 | def _summary_data_frame(self, **kwargs): |
| 765 | try: |
| 766 | import pandas as pd |
| 767 | except ImportError: |
| 768 | raise ImportError( |
| 769 | "Missing optional 'pandas' dependency. To install run: " |
| 770 | "pip install pandas" |
| 771 | ) |
| 772 | |
| 773 | # get labels or use default value, then extend with missing elements, |
| 774 | # and truncate extra elements |
| 775 | labels = kwargs.pop("labels", []) |
| 776 | labels.extend(range(len(labels), len(self))) |
| 777 | labels = labels[: len(self)] |
| 778 | |
| 779 | def get_row(result): |
| 780 | if EstimatorResult._is_succeeded(result): |
| 781 | formatted = result["physicalCountsFormatted"] |
| 782 | |
| 783 | return ( |
| 784 | formatted["algorithmicLogicalQubits"], |
| 785 | formatted["logicalDepth"], |
| 786 | formatted["numTstates"], |
| 787 | result["logicalQubit"]["codeDistance"], |
| 788 | formatted["numTfactories"], |
| 789 | formatted["physicalQubitsForTfactoriesPercentage"], |
| 790 | formatted["physicalQubits"], |
| 791 | formatted["rqops"], |
| 792 | formatted["runtime"], |
| 793 | ) |
| 794 | else: |
| 795 | return ["No solution found"] * 9 |
| 796 | |
| 797 | data = [get_row(self.data(index)) for index in range(len(self))] |
| 798 | columns = [ |
| 799 | "Logical qubits", |
| 800 | "Logical depth", |
| 801 | "T states", |
| 802 | "Code distance", |
| 803 | "T factories", |
| 804 | "T factory fraction", |
| 805 | "Physical qubits", |
| 806 | "rQOPS", |
| 807 | "Physical runtime", |
| 808 | ] |
| 809 | return pd.DataFrame(data, columns=columns, index=labels) |
| 810 | |
| 811 | def _item_result_table(self): |
| 812 | html = "" |
| 813 | |
| 814 | if has_markdown: |
| 815 | md = markdown.Markdown(extensions=["mdx_math"]) |
| 816 | for group in self["reportData"]["groups"]: |
| 817 | html += f""" |
| 818 | <details {"open" if group['alwaysVisible'] else ""}> |
| 819 | <summary style="display:list-item"> |
| 820 | <strong>{group['title']}</strong> |
| 821 | </summary> |
| 822 | <table>""" |
| 823 | for entry in group["entries"]: |
| 824 | val = self |
| 825 | for key in entry["path"].split("/"): |
| 826 | if key not in val and "frontierEntries" in val: |
| 827 | val = val["frontierEntries"][0] |
| 828 | val = val[key] |
| 829 | if has_markdown: |
| 830 | explanation = md.convert(entry["explanation"]) |
| 831 | else: |
| 832 | explanation = entry["explanation"] |
| 833 | html += f""" |
| 834 | <tr> |
| 835 | <td style="font-weight: bold; vertical-align: top; white-space: nowrap">{entry['label']}</td> |
| 836 | <td style="vertical-align: top; white-space: nowrap">{val}</td> |
| 837 | <td style="text-align: left"> |
| 838 | <strong>{entry["description"]}</strong> |
| 839 | <hr style="margin-top: 2px; margin-bottom: 0px; border-top: solid 1px black" /> |
| 840 | {explanation} |
| 841 | </td> |
| 842 | </tr> |
| 843 | """ |
| 844 | html += "</table></details>" |
| 845 | |
| 846 | html += f'<details><summary style="display:list-item"><strong>Assumptions</strong></summary><ul>' |
| 847 | if has_markdown: |
| 848 | for assumption in self["reportData"]["assumptions"]: |
| 849 | html += f"<li>{md.convert(assumption)}</li>" |
| 850 | html += "</ul></details>" |
| 851 | |
| 852 | return html |
| 853 | |
| 854 | def _item_result_summary_table(self): |
| 855 | html = """ |
| 856 | <style> |
| 857 | .aqre-tooltip { |
| 858 | position: relative; |
| 859 | border-bottom: 1px dotted black; |
| 860 | } |
| 861 | |
| 862 | .aqre-tooltip .aqre-tooltiptext { |
| 863 | font-weight: normal; |
| 864 | visibility: hidden; |
| 865 | width: 600px; |
| 866 | background-color: #e0e0e0; |
| 867 | color: black; |
| 868 | text-align: center; |
| 869 | border-radius: 6px; |
| 870 | padding: 5px 5px; |
| 871 | position: absolute; |
| 872 | z-index: 1; |
| 873 | top: 150%; |
| 874 | left: 50%; |
| 875 | margin-left: -200px; |
| 876 | border: solid 1px black; |
| 877 | } |
| 878 | |
| 879 | .aqre-tooltip .aqre-tooltiptext::after { |
| 880 | content: ""; |
| 881 | position: absolute; |
| 882 | bottom: 100%; |
| 883 | left: 50%; |
| 884 | margin-left: -5px; |
| 885 | border-width: 5px; |
| 886 | border-style: solid; |
| 887 | border-color: transparent transparent black transparent; |
| 888 | } |
| 889 | |
| 890 | .aqre-tooltip:hover .aqre-tooltiptext { |
| 891 | visibility: visible; |
| 892 | } |
| 893 | </style>""" |
| 894 | |
| 895 | if has_markdown: |
| 896 | md = markdown.Markdown(extensions=["mdx_math"]) |
| 897 | for group in self["reportData"]["groups"]: |
| 898 | html += f""" |
| 899 | <details {"open" if group['alwaysVisible'] else ""}> |
| 900 | <summary style="display:list-item"> |
| 901 | <strong>{group['title']}</strong> |
| 902 | </summary> |
| 903 | <table>""" |
| 904 | for entry in group["entries"]: |
| 905 | val = self |
| 906 | for key in entry["path"].split("/"): |
| 907 | val = val[key] |
| 908 | if has_markdown: |
| 909 | explanation = md.convert(entry["explanation"]) |
| 910 | else: |
| 911 | explanation = entry["explanation"] |
| 912 | html += f""" |
| 913 | <tr class="aqre-tooltip"> |
| 914 | <td style="font-weight: bold"><span class="aqre-tooltiptext">{explanation}</span>{entry['label']}</td> |
| 915 | <td>{val}</td> |
| 916 | <td style="text-align: left">{entry["description"]}</td> |
| 917 | </tr> |
| 918 | """ |
| 919 | html += "</table></details>" |
| 920 | |
| 921 | html += f"<details><summary style='display:list-item'><strong>Assumptions</strong></summary><ul>" |
| 922 | if has_markdown: |
| 923 | for assumption in self["reportData"]["assumptions"]: |
| 924 | html += f"<li>{md.convert(assumption)}</li>" |
| 925 | html += "</ul></details>" |
| 926 | |
| 927 | return html |
| 928 | |
| 929 | def _batch_result_table(self, indices): |
| 930 | succeeded_item_indices = [ |
| 931 | i for i in indices if EstimatorResult._is_succeeded(self[i]) |
| 932 | ] |
| 933 | if len(succeeded_item_indices) == 0: |
| 934 | print("None of the jobs succeeded") |
| 935 | return "" |
| 936 | |
| 937 | first_succeeded_item_index = succeeded_item_indices[0] |
| 938 | |
| 939 | html = "" |
| 940 | |
| 941 | if has_markdown: |
| 942 | md = markdown.Markdown(extensions=["mdx_math"]) |
| 943 | |
| 944 | item_headers = "".join(f"<th>{i}</th>" for i in indices) |
| 945 | |
| 946 | for group_index, group in enumerate( |
| 947 | self[first_succeeded_item_index]["reportData"]["groups"] |
| 948 | ): |
| 949 | html += f""" |
| 950 | <details {"open" if group['alwaysVisible'] else ""}> |
| 951 | <summary style="display:list-item"> |
| 952 | <strong>{group['title']}</strong> |
| 953 | </summary> |
| 954 | <table> |
| 955 | <thead><tr><th>Item</th>{item_headers}</tr></thead>""" |
| 956 | |
| 957 | visited_entries = set() |
| 958 | |
| 959 | for entry in [ |
| 960 | entry |
| 961 | for index in succeeded_item_indices |
| 962 | for entry in self[index]["reportData"]["groups"][group_index]["entries"] |
| 963 | ]: |
| 964 | label = entry["label"] |
| 965 | if label in visited_entries: |
| 966 | continue |
| 967 | visited_entries.add(label) |
| 968 | |
| 969 | html += f""" |
| 970 | <tr> |
| 971 | <td style="font-weight: bold; vertical-align: top; white-space: nowrap">{label}</td> |
| 972 | """ |
| 973 | |
| 974 | for index in indices: |
| 975 | val = self[index] |
| 976 | if index in succeeded_item_indices: |
| 977 | for key in entry["path"].split("/"): |
| 978 | if key in val: |
| 979 | val = val[key] |
| 980 | else: |
| 981 | val = "N/A" |
| 982 | break |
| 983 | else: |
| 984 | val = "N/A" |
| 985 | html += f""" |
| 986 | <td style="vertical-align: top; white-space: nowrap">{val}</td> |
| 987 | """ |
| 988 | |
| 989 | html += """ |
| 990 | </tr> |
| 991 | """ |
| 992 | html += "</table></details>" |
| 993 | |
| 994 | html += f'<details><summary style="display:list-item"><strong>Assumptions</strong></summary><ul>' |
| 995 | if has_markdown: |
| 996 | for assumption in self[0]["reportData"]["assumptions"]: |
| 997 | html += f"<li>{md.convert(assumption)}</li>" |
| 998 | html += "</ul></details>" |
| 999 | |
| 1000 | return html |
| 1001 | |
| 1002 | @staticmethod |
| 1003 | def _is_succeeded(obj): |
| 1004 | return "status" in obj and obj["status"] == "success" |
| 1005 | |
| 1006 | |
| 1007 | class EstimatorResultDiagram: |
| 1008 | def __init__(self, data): |
| 1009 | data.pop("reportData") |
| 1010 | self.data_json = json.dumps(data).replace(" ", "") |
| 1011 | self.vis_lib = "https://cdn-aquavisualization-prod.azureedge.net/resource-estimation/index.js" |
| 1012 | self.space = HTMLWrapper(self._space_diagram()) |
| 1013 | self.time = HTMLWrapper(self._time_diagram()) |
| 1014 | |
| 1015 | def _space_diagram(self): |
| 1016 | html = f""" |
| 1017 | <script src={self.vis_lib}></script> |
| 1018 | <re-space-diagram data={self.data_json}></re-space-diagram>""" |
| 1019 | return html |
| 1020 | |
| 1021 | def _time_diagram(self): |
| 1022 | html = f""" |
| 1023 | <script src={self.vis_lib}></script> |
| 1024 | <re-time-diagram data={self.data_json}></re-time-diagram>""" |
| 1025 | return html |
| 1026 | |
| 1027 | |
| 1028 | class LogicalCounts(dict): |
| 1029 | """ |
| 1030 | Microsoft Resource Estimator Logical Counts. |
| 1031 | |
| 1032 | The class represents logical counts that can be used as input to physical estimation of resources |
| 1033 | in the Microsoft Resource Estimator. |
| 1034 | """ |
| 1035 | |
| 1036 | def __init__(self, data: Dict): |
| 1037 | self._data = {} |
| 1038 | self._data["numQubits"] = data.get("numQubits", 0) |
| 1039 | self._data["tCount"] = data.get("tCount", 0) |
| 1040 | self._data["rotationCount"] = data.get("rotationCount", 0) |
| 1041 | self._data["rotationDepth"] = data.get("rotationDepth", 0) |
| 1042 | self._data["cczCount"] = data.get("cczCount", 0) |
| 1043 | self._data["ccixCount"] = data.get("ccixCount", 0) |
| 1044 | self._data["measurementCount"] = data.get("measurementCount", 0) |
| 1045 | super().__init__(self._data) |
| 1046 | |
| 1047 | @property |
| 1048 | def json(self): |
| 1049 | """ |
| 1050 | Returns a JSON representation of the logical counts. |
| 1051 | """ |
| 1052 | if not hasattr(self, "_json"): |
| 1053 | import json |
| 1054 | |
| 1055 | self._json = json.dumps(self._data) |
| 1056 | |
| 1057 | return self._json |
| 1058 | |
| 1059 | def estimate( |
| 1060 | self, params: Union[dict, List, EstimatorParams] = None |
| 1061 | ) -> EstimatorResult: |
| 1062 | """ |
| 1063 | Estimates resources for the current logical counts, using the |
| 1064 | Parallel Synthesis Sequential Pauli Computation (PSSPC) layout method. |
| 1065 | |
| 1066 | :param logical_counts: The logical counts. |
| 1067 | :param params: The parameters to configure physical estimation. |
| 1068 | |
| 1069 | :returns resources: The estimated resources. |
| 1070 | """ |
| 1071 | if params is None: |
| 1072 | params = [{}] |
| 1073 | elif isinstance(params, EstimatorParams): |
| 1074 | if params.has_items: |
| 1075 | params = params.as_dict()["items"] |
| 1076 | else: |
| 1077 | params = [params.as_dict()] |
| 1078 | elif isinstance(params, dict): |
| 1079 | params = [params] |
| 1080 | return EstimatorResult( |
| 1081 | json.loads(physical_estimates(self.json, json.dumps(params))) |
| 1082 | ) |
| 1083 | |