Where does the Xmon simulator from Googles cirq framework its entropy from?












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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?










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    5












    $begingroup$


    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?










    share|improve this question











    $endgroup$















      5












      5








      5





      $begingroup$


      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?










      share|improve this question











      $endgroup$




      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?







      measurement simulation cirq entropy nisq






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      edited Feb 2 at 21:18









      Blue

      6,26041355




      6,26041355










      asked Feb 2 at 20:58









      sycramoresycramore

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          Cirq uses numpy's pseudo random number generator to pick measurement results, e.g. here is code from XmonStepper.simulate_measurement:



              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.






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            1 Answer
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            $begingroup$

            Cirq uses numpy's pseudo random number generator to pick measurement results, e.g. here is code from XmonStepper.simulate_measurement:



                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.






            share|improve this answer









            $endgroup$


















              4












              $begingroup$

              Cirq uses numpy's pseudo random number generator to pick measurement results, e.g. here is code from XmonStepper.simulate_measurement:



                  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.






              share|improve this answer









              $endgroup$
















                4












                4








                4





                $begingroup$

                Cirq uses numpy's pseudo random number generator to pick measurement results, e.g. here is code from XmonStepper.simulate_measurement:



                    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.






                share|improve this answer









                $endgroup$



                Cirq uses numpy's pseudo random number generator to pick measurement results, e.g. here is code from XmonStepper.simulate_measurement:



                    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.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Feb 2 at 21:46









                Craig GidneyCraig Gidney

                4,471220




                4,471220






























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