6.1. The Measurement Problem
The R-procedure is about the measurement, which concerns with observations. To understand R-procedure, we need a deeper understanding of wavefunction. The quantum theoretic experiments can be characterized in terms of Dirac bra-ket formalism (Dirac, 1930/1958). Let be a wavefunction. A state of is denoted by , which represents a vector in Hilbert space. This can be seen as a syntactic representation. The semantic meaning of a state is called the amplitude, which is characterized by the squared modulus of a complex number , write , which is also called the Born probability (density). As Dirac regards, the probability is the squared possibility. The wavefunction is single-valued and linear. The states of a wavefunction satisfy superposition operation.
The quantum theoretic experimentation can be characterized by the Dirac bra-ket formalism as follows: , where is a sample state, is a reasoning experiment, and is a set of reasoning tasks used in the experiment . When are used to keep stimulate reasoners one by one, the reasoners are required to provide answers one after one. Thus, becomes a function of , write , which supposes to be a wavefunction. As Feynman characterized, is the initial state while serves as the final state of the experiment (Feynman, Leighton, & Sands, 1971). However, as explained earlier, for the evaluation task used in reasoning experiments, such a wavefunction has two eigen values, yes or else no. In other words, it is two-valued, which is inconsistent with the single-value requirement. This is the long-standing measurement problem (or paradox, by Penrose) in quantum theory (Neuman, 1955/1983),
6.2. Stochastic Sampling
To solve the measurement problem of reasoning experiments, we propose a new approach called stochastic sampling (Yang, 2024). which is introduced in the following step by step.
Let H be the population of all potential reasoners. Let us consider an any given wavefunction (x), where x ranges over all the potential reasoners. 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 6.1. (the observation operator). 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 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 6.2. (the internal variable of a sample). 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 6.3. (sampling operator). We introduce a new operator , called the sample generator. , . Call the testing adjoint of .
Definition 6.4 (the 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 comparable 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 6.5 (the R-procedure). 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, who calls the final state and the initial state of a quantum theoretic experiment.
Definition 6.6 (the 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 6.7 (the 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 6.8 (Linearization). The linearization operator L is defined by ).
Definition 6.9 (
the sample Born probability). For any given testing sample
, which produces a yes-number
and a no-number
. The sample Born probability is defined by
Born probability is a kind of explanation, which serves as a semantics for the evolution of wavefunction. As Penrose points out (Penrose, 2004), the U-procedure and R-procedure must share the same semantics, i.e., the squared magnitude of two eigenvalues.
Theorem 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
This shows that Definition 6.8 is conformal with respect to the Born rule.
Part III. The Lagrangian and the “Man vs. Men” problem