Fisher information from likelihood function for quantum circuits

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Johny Boy
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I want to confirm and discuss my idea of how to derive the Fisher information from the likelihood function of a state sent through a quantum circuit (such as the Mach-Zehnder).
In the context of a single phase estimation problem of a quantum photonics experiment. For example consider a 3-photon quantum circuit (such as the Mach-Zehnder which depends on some phase shift operator which encodes a parameter ##\theta##) with a photon counting measurement (two detectors) at the end of the circuit with measurement probabilities:

- P(0,2): 0 photons detected in Detector 1, 2 photons detected in Detector 2.
- P(1,1): 1 photon detected in each detector.
- P(2,0): 2 photons detected in Detector 1, and none in Detector 2.

Consider that at a given time we carry out ##M## total measurements. We will get some set of measurement outcomes {##m_{02},~m_{11},~m_{20}##}, where ##M = m_{02}+m_{11}+m_{20}##. Am I correct that we can define the corresponding likelihood function ##L(\theta)## (which we intend to use to evaluate the Fisher information) by:
$$L(\theta):= \frac{M!}{(m_{02}! m_{11}! m_{20}!)} P(0,2)^{m_{02}}P(1,1)^{m_{11}}P(2,0)^{m_{20}},$$
where the multinomial coefficients account for the different permutations.

Can anyone advise if this is the correct way to construct the Likelihood function for the described experimental scenario. Lastly, would I be correct that for the discrete case the Fisher information is given by $$I(\theta):= \sum_{x} \bigg(\frac{\partial }{\partial \theta} \log L(x;\theta)\bigg)^2L(x; \theta),$$
where the summation over ##x## refers to all the different permutations of outcomes {##m_{02},~m_{11},~m_{20}##} such that ##M = m_{02}+m_{11}+m_{20}## holds. Is this this the correct understanding of how to derive the Fisher information for this discrete phase estimation scheme? Many thanks for your time and assistance.
 

FAQ: Fisher information from likelihood function for quantum circuits

What is Fisher information in the context of quantum circuits?

Fisher information in the context of quantum circuits quantifies the amount of information that an observable random variable carries about an unknown parameter upon which the probability depends. It is a measure of the sensitivity of the likelihood function to changes in the parameter and is crucial for parameter estimation in quantum systems.

How is Fisher information derived from the likelihood function for quantum circuits?

Fisher information is derived from the likelihood function by calculating the second derivative of the log-likelihood function with respect to the parameter of interest. For quantum circuits, this involves determining the likelihood of measurement outcomes given a quantum state and then differentiating to find how sensitive these outcomes are to changes in the parameters of the quantum circuit.

Why is Fisher information important for quantum circuits?

Fisher information is important for quantum circuits because it provides a lower bound on the variance of unbiased estimators of a parameter, known as the Cramér-Rao bound. This is crucial for optimizing quantum algorithms and measurements, allowing for more precise and efficient parameter estimation in quantum computing and quantum information processing.

What are the challenges in computing Fisher information for quantum circuits?

Computing Fisher information for quantum circuits is challenging due to the complexity of quantum systems, including the need to handle high-dimensional Hilbert spaces and the probabilistic nature of quantum measurements. Additionally, quantum circuits often involve entanglement and other quantum phenomena that complicate the analytical and numerical computation of the Fisher information.

Can Fisher information be used to improve quantum algorithms?

Yes, Fisher information can be used to improve quantum algorithms by providing insights into the optimal settings of parameters and the design of measurement strategies. By maximizing Fisher information, one can enhance the precision of parameter estimation and thereby improve the performance and efficiency of quantum algorithms, particularly in tasks like quantum sensing, quantum tomography, and variational quantum algorithms.

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