Submitted:
01 October 2024
Posted:
01 October 2024
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Abstract
Keywords:
1. Introduction
- QM Axiom 1 of 5 State Space: Every physical system is associated with a complex Hilbert space, and its state is represented by a ray (an equivalence class of vectors differing by a non-zero scalar multiple) in this space.
- QM Axiom 2 of 5 Observables: Physical observables correspond to Hermitian (self-adjoint) operators acting on the Hilbert space.
- QM Axiom 3 of 5 Dynamics: The time evolution of a quantum system is governed by the Schrödinger equation, where the Hamiltonian operator represents the system’s total energy.
- QM Axiom 4 of 5 Measurement: Measuring an observable projects the system into an eigenstate of the corresponding operator, yielding one of its eigenvalues as the measurement result.
- QM Axiom 1 of 5 Probability Interpretation: The probability of obtaining a specific measurement outcome is given by the squared magnitude of the projection of the state vector onto the relevant eigenstate (Born rule).
1.1. Statistical Mechanics
1.2. Quantum Mechanics
1.3. Fundamental Physics
1.4. Dimensional Obstructions
2. Results
2.1. Quantum Mechanics
- The entropy maximization procedure inherently normalizes the vectors with . This normalization links to a unit vector in Hilbert space. Furthermore, as physical states associate to the probability measure, and the probability is defined up to a phase, we conclude that physical states map to Rays within Hilbert space. This demonstrates 1.
-
In Z, an observable must satisfy:Since , then any self-adjoint operator satisfying the condition will equate the above equation, simply because . This demonstrates 1.
-
Upon transforming Equation (47) out of its eigenbasis through unitary operations, we find that the energy, , typically transforms in the manner of a Hamiltonian operator:The system’s dynamics emerge from differentiating the solution with respect to the Lagrange multiplier. This is manifested as:which is the Schrödinger equation. This demonstrates 1.
-
From Equation (47) it follows that the possible microstates of the system correspond to specific eigenvalues of . An observation can thus be conceptualized as sampling from , with the measured state being the occupied microstate i. Consequently, when a measurement occurs, the system invariably emerges in one of these microstates, which directly corresponds to an eigenstate of . Measured in the eigenbasis, the probability measure is:In scenarios where the probability measure is expressed in a basis other than its eigenbasis, the probability of obtaining the eigenvalue is given as a projection on a eigenstate:Here, signifies the squared magnitude of the amplitude of the state when projected onto the eigenstate . As this argument hold for any observables, this demonstrates d.
- Finally, since the probability measure (Equation (45)) replicates the Born rule, 1 is also demonstrated.
2.2. RQM in 2D
2.2.1. 1+1D Obstruction
2.3. RQM in 3+1D
2.3.1. Preliminaries
- is a positive scalar factor ensuring non-negativity.
- is a rotor representing Lorentz transformations (rotations and boosts in spacetime).
- is a complex phase factor, as and .
- The exponential map maps elements of the algebra to the connected component of the identity in the spin group , except at the zero vector, where the map is not injective.
- The wavefunction captures both the amplitude (through ) and the phase (through and ) of the quantum state.
- The double-copy product yields zero if and only if is the zero vector in the quotient space.
- All non-zero vectors in the quotient space have a strictly positive norm.
- The identification of null vectors with the zero vector removes the degeneracy caused by the indefinite metric signature.
2.3.2. RQM
2.3.3. Standard Model Gauge Symmetries
2.3.4. A Starting Point for a Theory of Quantum Gravity
2.4. Dimensional Obstructions
- :
- Let , then:which is valued in .
- :
- Let , then:which is valued in .
- :
-
Let , where , then:We note that , therefore:which is valued in .
3. Conclusions
Data Availability Statement
Conflicts of Interest
Appendix A. SM
Appendix B. RQM in 3+1D
Appendix C. SageMath program showing ⌊u ‡ u⌋ 3,4 u ‡ u=detM u
- from sage.algebras.clifford_algebra import CliffordAlgebra
- from sage.quadratic_forms.quadratic_form import QuadraticForm
- from sage.symbolic.ring import SR
- from sage.matrix.constructor import Matrix
- # Define the quadratic form for GA(3,1) over the Symbolic Ring
- Q = QuadraticForm(SR, 4, [-1, 0, 0, 0, 1, 0, 0, 1, 0, 1])
- # Initialize the GA(3,1) algebra over the Symbolic Ring
- algebra = CliffordAlgebra(Q)
- # Define the basis vectors
- e0, e1, e2, e3 = algebra.gens()
- # Define the scalar variables for each basis element
- a = var(’a’)
- t, x, y, z = var(’t x y z’)
- f01, f02, f03, f12, f23, f13 = var(’f01 f02 f03 f12 f23 f13’)
- v, w, q, p = var(’v w q p’)
- b = var(’b’)
- # Create a general multivector
- udegree0=a
- udegree1=t*e0+x*e1+y*e2+z*e3
- udegree2=f01*e0*e1+f02*e0*e2+f03*e0*e3+f12*e1*e2+f13*e1*e3+f23*e2*e3
- udegree3=v*e0*e1*e2+w*e0*e1*e3+q*e0*e2*e3+p*e1*e2*e3
- udegree4=b*e0*e1*e2*e3
- u=udegree0+udegree1+udegree2+udegree3+udegree4
- u2 = u.clifford_conjugate()*u
- u2degree0 = sum(x for x in u2.terms() if x.degree() == 0)
- u2degree1 = sum(x for x in u2.terms() if x.degree() == 1)
- u2degree2 = sum(x for x in u2.terms() if x.degree() == 2)
- u2degree3 = sum(x for x in u2.terms() if x.degree() == 3)
- u2degree4 = sum(x for x in u2.terms() if x.degree() == 4)
- u2conj34 = u2degree0+u2degree1+u2degree2-u2degree3-u2degree4
- I = Matrix(SR, [[1, 0, 0, 0],
- [0, 1, 0, 0],
- [0, 0, 1, 0],
- [0, 0, 0, 1]])
- #MAJORANA MATRICES
- y0 = Matrix(SR, [[0, 0, 0, 1],
- [0, 0, -1, 0],
- [0, 1, 0, 0],
- [-1, 0, 0, 0]])
- y1 = Matrix(SR, [[0, -1, 0, 0],
- [-1, 0, 0, 0],
- [0, 0, 0, -1],
- [0, 0, -1, 0]])
- y2 = Matrix(SR, [[0, 0, 0, 1],
- [0, 0, -1, 0],
- [0, -1, 0, 0],
- [1, 0, 0, 0]])
- y3 = Matrix(SR, [[-1, 0, 0, 0],
- [0, 1, 0, 0],
- [0, 0, -1, 0],
- [0, 0, 0, 1]])
- mdegree0 = a
- mdegree1 = t*y0+x*y1+y*y2+z*y3
- mdegree2 = f01*y0*y1+f02*y0*y2+f03*y0*y3+f12*y1*y2+f13*y1*y3+f23*y2*y3
- mdegree3 = v*y0*y1*y2+w*y0*y1*y3+q*y0*y2*y3+p*y1*y2*y3
- mdegree4 = b*y0*y1*y2*y3
- m=mdegree0+mdegree1+mdegree2+mdegree3+mdegree4
- print(u2conj34*u2 == m.det())
- True
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