Measurements create entropy as we all know. But computers themselves are deterministic machines. Most devices use processor heat as a source for random number generation as far as I know - which has lead to problems in the past. Any cryptographic key is only as good as the entropy source from which its content originate. When I try to collect binary entropy as results from a quantum measurement it is still a simulation - yet for huge numbers it converges well to the distribution I should obtain. So how does the simulator collect the entropy for the measurement outcomes?
Cirq uses numpy's pseudo random number generator to pick measurement results, e.g. here is code from
def simulate_measurement(self, index: int) -> bool: [...] prob_one = np.sum(self._pool.map(_one_prob_per_shard, args)) result = bool(np.random.random() <= prob_one) [...]
Cirq simulations are not intended to be a source of cryptographically secure entropy.