quairkit.loss.measure¶
The source file of the class for the measurement.
-
class quairkit.loss.measure.Measure(measure_op=
None)¶ Compute the probability of the specified measurement result.
- Parameters:¶
- measure_op : str | List[str] | Tensor | None¶
Specify the basis of the measurement. Defaults to
'computational'.
Note
the allowable input for measure_op are: - None, i.e., a ‘computational’ basis - a string composed of ‘i’, ‘x’, ‘y’, and ‘z’ - a projection-valued measurement (PVM) in torch.Tensor
from quairkit.database import random_state, std_basis # Define measurement basis using a string (e.g., 'xy' denotes eigen-basis of X ⊗ Y) op = Measure("xy") # Define measurement basis using a list for multiple measurement setups (e.g., 'xy' and 'yz') op = Measure(["xy", "yz"]) # Define a custom measurement basis using a user-specified PVM tensor pvm_tensor = std_basis(2).density_matrix op = Measure(pvm_tensor) # Use default computational basis op = Measure() state = random_state(num_qubits=2) # Full measurement: probability distribution over all measurement outcomes. result = op(state) print("Probabilities for measuring all qubits:", result) # Measurement on a targeted subsystem (e.g., the first qubit) result = op(state, system_idx=0) print("Probabilities for measuring the first qubit:", result) # Compute probability for a specific full-system outcome ('10') result = op(state, desired_result='10') print("Probability for measuring all qubits with outcome 10:", result) # Compute probabilities for selected outcomes (e.g., outcomes corresponding to indices 0 and 3) result = op(state, desired_result=[0, 3]) print("Probabilities for measuring all qubits with outcomes 00 and 11:", result) # Retrieve both the probability and the post-measurement state. prob, post_state = op(state, keep_state=True) print("Post-measurement state:", post_state) # Batched measurement on multiple states. state_batch = random_state(num_systems=2, size=2) result = op(state_batch) print(f"Probabilities for measuring two states:\n{result}")Probabilities for measuring all qubits: tensor([0.1273, 0.0956, 0.3312, 0.4459]) Probabilities for measuring the first qubit: tensor([0.2229, 0.7771]) Probability for measuring all qubits with outcome 10: tensor([0.3312]) Probabilities for measuring all qubits with outcomes 00 and 11: tensor([0.1273, 0.4459]) Post-measurement state: ----------------------------------------------------- Backend: state_vector System dimension: [2, 2] System sequence: [0, 1] Batch size: [4] # 0: [1.+0.j 0.+0.j 0.+0.j 0.+0.j] # 1: [0.+0.j 1.+0.j 0.+0.j 0.+0.j] # 2: [0.+0.j 0.+0.j 1.+0.j 0.+0.j] # 3: [0.+0.j 0.+0.j 0.+0.j 1.+0.j] ----------------------------------------------------- Probabilities for measuring two states: tensor([[0.2846, 0.1826, 0.2665, 0.2663], [0.1513, 0.4921, 0.1676, 0.1891]])-
forward(state, system_idx=
'full', desired_result=None, keep_state=False)¶ Compute the probability of measurement to the input state.
- Parameters:¶
- state : State¶
The quantum state to be measured.
- system_idx : Iterable[int] | int | str | None¶
The index of the systems to be measured. Defaults to
'full'which means measure all the qubits.- desired_result : List[int | str] | int | str | None¶
Specify the results of the measurement to return. Defaults to
Nonewhich means return the probability of all the results.- keep_state : bool | None¶
Whether return the measured state. Defaults to
False.
- Returns:¶
The probability of the measurement.
- Return type:¶
Tensor | Tuple[Tensor, State] | Tuple[Dict, State]
- class quairkit.loss.measure.ExpecVal(hamiltonian)¶
The class of the loss function to compute the expectation value for the observable.
This interface can make you using the expectation value for the observable as the loss function.
- Parameters:¶
- hamiltonian : Hamiltonian¶
The input observable.
from quairkit.database import random_hamiltonian_generator, random_state observable = random_hamiltonian_generator(1) print(f'The input observable is:\n{observable}') expec_val = ExpecVal(observable) input_state = random_state(num_systems=1, rank=2) print('The expectation value of the observable:',expec_val(input_state, decompose=True)) input_state_batch = random_state(num_systems=1, size=2) print('The expectation value of the observable:',expec_val(input_state_batch, decompose=False))The input observable is: -0.28233465254251144 Z0 0.12440505341821817 X0 -0.2854054036807161 Y0 The expectation value of the observable: tensor([-0.1162, 0.0768, -0.0081]) The expectation value of the observable: tensor([0.1748, 0.0198])-
forward(state, decompose=
False)¶ Compute the expectation value of the observable with respect to the input state.
The value computed by this function can be used as a loss function to optimize.