The neuron's output state is transmitted as a signal strength to whatever neurons its axon synapses on.
The output state of the circuit is then measured in the computational basis, or in any other arbitrary orthonormal basis.
As the combined output state can be used to uniquely identify the combined input state, the operation is logically reversible.
There are three components to determine the digital output state of the PSoC.
In order to discuss the convenience of the problem, the output state will not be considered in the evaluation approach.
The output state vector of the model is used to monitor the trajectory of moving objects and the moving surfaces.
Note that if (lambda=0), i.e. no entanglement at the input, then the output state is not entangled either.
Since flip-flops are sequential logic elements, the present inputs along with the previous output determine the present output state.
Under the right conditions, neurons can switch between bursting and single-spiking modes, therefore providing a conditional output state.
It then computes a new activation state based on its total input and current activation state, and a new output state based on its new activation value.
Measuring this output state yields \(\lvert 0\rangle\) or \(\lvert 1\rangle\) with 50/50 probability, which can be used to simulate a fair coin toss.
