Which is an example of measurement error mitigation?
In this example we first looked at results for each of the definite basis states, and used these results to mitigate the effects of errors for a more general form of state. This is the basic principle behind measurement error mitigation. Now we just need to find a way to perform the mitigation algorithmically rather than manually.
How is measurement error mitigation done in Qiskit?
In Qiskit we mitigate for the noise by creating a measurement filter object. Then, taking the results from above, we use this to calculate a mitigated set of counts. Qiskit returns this as a dictionary, so that the user doesn’t need to use vectors themselves to get the result.
How to do error mitigation for two qubits?
As an example, let’s stick with doing error mitigation for a pair of qubits. For this we define a two qubit quantum register, and feed it into the function complete_meas_cal. This creates a set of circuits to take measurements for each of the four basis states for two qubits: |00⟩ | 00 ⟩, |01⟩ | 01 ⟩, |10⟩ | 10 ⟩ and |11⟩ | 11 ⟩.
What is the purpose of quantum error mitigation?
Quantum error mitigation refers to a series of techniques aimed at reducing (mitigating) the errors that occur in quantum computing algorithms. Unlike software bugs affecting code in usual computers, the errors which we attempt to reduce with mitigation are due to the hardware.
How are counting statistics related to error propagation?
– arise from inherent instrument limitation (e.g. electronic noise) and/or the inherent nature of the phenomena (e.g. biological variability, counting statistics)! – each measurement fluctuates independently of previous measurements, i.e. no constant offset or bias! – measurements with a low level of random error have a high precision.! 5
Why do random errors occur in counting statistics?
Random Errors! – arise from inherent instrument limitation (e.g. electronic noise) and/or the inherent nature of the phenomena (e.g. biological variability, counting statistics)! – each measurement fluctuates independently of previous measurements, i.e. no constant offset or bias! – measurements with a low level of random error have a high