4.4. Stochastic sampling
Yang (2024) proposed a stochastic sampling method to generate Born Probability from the R-process. Let us consider an any given wavefunction (x), where x ranges over all of the space points. We assume that (x) is one-dimensional without loss of generality for multi-dimensions. Thus, the corresponding Hilbert space we are currently discussing is one-dimensional, denoted by H. Hence, we may treat all the vectors in H as space points also without loss of generality. Now, it introduces an observation operator Q, which is defined below.
Definition 4.1 For any given a, , . We call that is the observational conjugate of . Accordingly, we define | ranges over all possible observational . Call the observational dual space of .
The necessity of the distinction between the space points and the observational points is analytical to the distinction between of the intuitive natural numbers and the set-theoretic enumerers in Gödel’s work (Yang, 2022).
Consider the power set of , . Now, we start to select the elements from . Notice that this selection process is countable, but the cardinal number of is an uncountable infinity. We may reasonably assume this selection process is stochastic.
Definition 4.2 We introduce a new variable , . Of course, we also have , so we can introduce another variable , where the superscript j indicates the jth element stochastically selected from , the subscript i indicates that x ranges over only those space points within . It is easy to see that connects and . Accordingly, we have
Definition 4.3 We introduce a new operator , called the sample generator. , . Call the testing adjoint of .
Definition 4.4 Stochastic sampling: 1. For any given once a is stochastically selected, its adjoint becomes a testing sample. 2. For any , if it has not been selected, then its adjoint is not a testable sample yet.
Note, this definition is analytical to the expressibility in Gödel’s work [
6]. (Hint, the notion of expressibility is necessary to bridge the relations in Piano arithmetic and functions in the first order theory.) While here the definition of stochastic sampling process is necessary to bridge any
from sampling perspectives.
Definition 4.5 (R-process). Let stand for an any given sample , denote a YES/No type experiment, and q be a Yes/No type stimuli that can use to test . By Dirac bra-ket formalism, we can write this structure as . When gives the stimulus q to , each operational conjugate in returns a Yes/No type response. Thus, is a function of . This is called the R-procedure of the wavefunction. Note, this idea is from Feynman (1965), who calls the final state and the initial state of a quantum theoretic experiment.
Definition 4.6 (Sample space). The sample space for the Yes/No type measurement is two-valued, i.e., This means the E-projector has two and only two eigenstates, of which the eigenvalues are Yes and No.
Definition 4.7 (Sample phase). Consider projector E, for each proper sample of Yes/No type measurement, produces a pair of the yes-number c and the no-number d, which in turn produces a sample phase with respect to the exponential form of . All the possible sample phases form an group, write it G. From Definitions 1 to 4, it is easy to see that G is originally generated from the wavefunction , so we write G as .
Because symmetry, the stochastic sampling here satisfies the required conservativeness. It is worth mentioning that, in addition to the well-documented dynamic phase and Berry phase in the literature of dynamic analysis, the sample phase introduced here is the third kind of phase. This is one significant character of the R-procedure. For the U-procedure, we have the dynamic phase potential group, write it as .
Definition 4.8 (Linearization). The linearization operator L is defined by ).
Definition 4.9 (Sample Born probability). For any given testing sample , which produces a yes-number and a no-number . The sample Born probability is defined by
. (4.4)
Born probability is a kind of explanation, which serves as a semantics for the evolution of wavefunction. As Penrose pointes out [
1], the U-procedure and R-procedure must share the same semantics, i.e., the squared magnitude of two eigenvalues.
Theorem 4.1 (Born rule). The Born probability defined by Definition 9 obeys Born rule.
Proof. Let be a testing sample. eigenstates, Yes or else No. Assume the eigenvalue for Yes is c and the eigenvalue for No is d. Then, by Definition 8, we have ). Hence, by Definition 9, we have
( . (4.5)
This shows that Definition 8 is conformal with respect to the Born rule.