Submitted:
30 April 2025
Posted:
07 May 2025
Read the latest preprint version here
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 5 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).
- t is the Lagrange multiplier enforcing the natural constraint
- p is the number of spatial dimensions
- w is an information density. As such, it adheres to all but one axiom of probability theory: it is non-negative, but it is not normalized to unity
- is a matrix or operator (its specific form depends on the dimension and will be given in the results section)
- the constraint is the continuum version of Axiom 1.
2. Results
2.1. Spinors + Dirac Equation
2.1.1. The Multivector Determinant
2.1.2. The Optimization Problem

- In 3+1D, we are interested in the case where the states are an element of the even sub-algebra of , whose determinant is non-negative:
- In the continuum such elements are transformed by a connection which is valued in :
- We also consider translations and . Hence, we define the covariant derivative as:
- The term will be added to contract with , leaving no free indices. But since it produces an odd-multivector in the process, the term is also added converting the result back into an even-multivector. This selects a preferred frame—the laboratory frame.
- is the normal gamma
- is the trace of the extrinsic curvature
- is the 3D spatial covariant derivative on the slice .
2.2. Dimensional Obstructions
- :
- Let , then:which is valued in .
- GA(0):
- Definition 1 requires 1 time parameter for the Lagrange multiplier, and at least 1 space parameter for the integration measure. This configuration has neither.
- GA(0, 1):
- Definition 1 requires 1 time parameter for the Lagrange multiplier, and at least 1 space parameter for the integration measure. This configuration has the time parameter, but lacks a space parameter.
- GA(1, 0):
- Definition 1 requires 1 time parameter for the Lagrange multiplier, and at least 1 space parameter for the integration measure. This configuration has the space parameter, but lacks a time parameter.
- GA(2, 0):
- Definition 1 requires 1 time parameter for the Lagrange multiplier, and at least 1 space parameter for the integration measurement. This configuration has two space parameters, but lacks the time parameter.
- GA(2, 2):
- Definition 1 requires 1 time parameter for the Lagrange multiplier, and at least 1 space parameter for the integration measurement. This configuration has two space parameters, but has more time parameters than Lagrange multipliers.
2.3. Gravity + Yang-Mills
2.3.1. Rationale
2.3.2. Notation
2.3.3. Geometry
2.3.4. Action
- is not a probability density—it lacks a conserved current () and is not normalized—but it is non-negative.
- Instead, is interpreted as an information density, encoding spacetime’s local information content.
- Conservation: The current is conserved (), making a conserved charge.
- Causal Propagation: Surprisal propagates at light speed, enforcing that bits of information cannot spread superluminally—a core tenet of relativity.
- Varying with respect to yields the EFE with the Einstein tensor from , and is sourced by the quantum action variation yielding the stress-energy tensor.
- Varying with respect to gives equations of motion that define the flow of information quantity in spacetime.
2.3.5. Yang-Mills
-
Probability Measure: The quartic form enforces rotor invariance , restricting transformations to those satisfying , for some rotor R of a geometric algebra of n dimensions:Solutions to are rotor transformations generated by bivectors in the Clifford algebra. For a -dimensional algebra, these generate , whose subgroups include .
- Internal Space: For the gauge transformation to represent a purely internal symmetry that does not mix spacetime components defined by the basis (specifically preserving the time direction ), the generators must commute with , i.e., . This ensures the transformation acts orthogonally to the spacetime structure.
- Spacetime: The origin of the multivector determinant from STA, parametrize the resulting internal space in spacetime.
- For Yang-Mills:
- For the Standard Model :
- : Generators of .
- : , , and gauge fields.
- : Higgs field (SU(2) doublet).
-
Leading Terms:
- (a)
- Cosmological constant: .
- (b)
- Einstein-Hilbert term: .
-
Yang-Mills and Higgs:
- (a)
- Gauge kinetic terms: .
- (b)
- Higgs kinetic and potential terms:
- Yukawa Couplings (from matter fields):
- Higher-Order Terms: Higher order field strength terms appear but are suppressed by , making them negligible at low energies.
- Uniqueness: The Standard Model is not uniquely selected by the optimization problem but resides within the landscape of allowed Yang-Mills theories.
2.3.6. Yang-Mills Axioms as Theorems
- Compact Gauge Group: The symmetry group is a compact Lie group G.
- Local Gauge Invariance: Fields transform under spacetime-dependent (local) group elements .
- Gauge Connections: Gauge fields are introduced as connections in the covariant derivative .
- Field Strength: The curvature defines the dynamics.
- Yang-Mills Action: The action depends on , e.g., .
- Constraint: implies invariance of arbitrary n-dimentional rotors: .
- Structure of Solutions: Rotor transformations in finite-dimensional Clifford algebras are generated by bivectors. These generate Spin() and its subgroups, which are compact Lie groups.
- Wavefunction Transformation: , where (exponentials of spacetime-dependent bivectors).
- Probability Measure: .
- Dirac Current: , since .
- Minimal Coupling: To preserve , the derivative must transform as , where .
- Gauge Field Definition: Let , then:
- Clifford Algebra Embedding: The are bivector fields in , ensuring (the Lie algebra of G)).
3. Discussion
3.1. Proposed Interpretation of QM
3.1.1. Demystifying the Measurement Problem
3.1.2. Dissolving the Measurement Problem
- We propose that a well-defined experiment begin with a measurement outcome , not an abstract quantum state .
-
Example: Preparing requires:
- (a)
- Measure systems to collapse to or .
- (b)
- Discard all systems in state .
- (c)
- Apply a Hadamard gate H to .
- (d)
- The preparation is complete.
Neglecting the initial measurement (a) implies that systems of unknown states are sent into the Hadamard gate—the resulting experiment is ill-defined.
-
Objection 1: Preparation Without Collapse
- (a)
- Issue: Traditional QM superficially appears to allow preparing without collapsing it (e.g., via unitary gates, cooling, etc.).
- (b)
- Response: In practice, all preparations are validated by measurement (or an equivalent).
- (c)
-
Example:
- i
- Cooling various qubits to is non-invertible (one cannot return to the initial because of dissipative effects). The end result is mathematically equivalent to a measurement or followed by a discard of .
- ii
- Creating requires assuming the initial , validated by prior conditions.
-
Objection 2: Loss of Quantum Coherence
- (a)
- Issue: If preparation starts with a measurement, how do we account for coherence (e.g., interference)?
- (b)
- Response: Coherence emerges operationally.
- (c)
-
Example:
- i
- Measure systems to collapse to or .
- ii
- Discard all systems in state .
- iii
- Apply H to many initial -verified states.
- iv
- Aggregate final measurements () show interference patterns, even though individual experiments start with collapsed states.
-
Objection 3: Entanglement and Nonlocality
- (a)
- Issue: Entangled states require joint preparation of superpositions.
- (b)
- Response: Entanglement is preparable from an initial measurement like any other state.
- (c)
-
Example:
- i
- Measure systems to collapse to , , , or .
- ii
- Discard all systems in state , , and .
- iii
- Apply a Hadamard gate to the first qubit:
- iv
- Apply a gate (with first qubit as control, second as target):
The final state is an entangled state—specifically, it’s one of the Bell states (sometimes denoted as ).
4. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. SM
Appendix B. SageMath Program Showing ⌊u ‡ u⌋ 3,4 u ‡ u=detϕ(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
Appendix C. SageMath Program Showing detϕ(u) is Positive-Definite for Even Multivectors of GA (3,1)
- reset()
- 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’)
- f01, f02, f03, f12, f23, f13 = var(’f01 f02 f03 f12 f23 f13’)
- b = var(’b’)
- udegree0=a
- udegree2=f01*e0*e1+f02*e0*e2+f03*e0*e3+f12*e1*e2+f13*e1*e3+f23*e2*e3
- udegree4=b*e0*e1*e2*e3
- M=udegree0+udegree2+udegree4
- print(M.clifford_conjugate()*e0*M)
- (a^2 + b^2 + f01^2 + f02^2 + f03^2 + f12^2 + f13^2 + f23^2)*e0
- + (−2*a*f01 + 2*f02*f12 + 2*f03*f13 − 2*b*f23)*e1
- + (−2*a*f02 − 2*f01*f12 + 2*b*f13 + 2*f03*f23)*e2
- + (−2*a*f03 − 2*b*f12 − 2*f01*f13 − 2*f02*f23)*e3
References
- Jaynes, E.T. Information theory and statistical mechanics. Physical review 1957, 106, 620. [Google Scholar] [CrossRef]
- Jaynes, E.T. Information theory and statistical mechanics. II. Physical review 1957, 108, 171. [Google Scholar] [CrossRef]
- Dirac, P.A.M. The principles of quantum mechanics; Number 27, Oxford university press, 1981.
- Von Neumann, J. Mathematical foundations of quantum mechanics: New edition; Vol. 53, Princeton university press, 2018.
- Lundholm, D. Geometric (Clifford) algebra and its applications. arXiv preprint math/0605280.
- Hestenes, D. Spacetime physics with geometric algebra (Page 6). American Journal of Physics 2003, 71, 691–714. [Google Scholar] [CrossRef]
- Acus, A.; Dargys, A. Inverse of multivector: Beyond p+ q= 5 threshold. arXiv preprint arXiv:1712.05204, arXiv:1712.05204 2017.
- Chamseddine, A.H.; Connes, A. The spectral action principle. Communications in Mathematical Physics 1997, 186, 731–750. [Google Scholar] [CrossRef]
| 1 | The removal of the determinant implies an additional term , where . Furthermore, since implies , it is simply a gauge choice. We thus choose . |
| 2 | The author suggests that observations, so defined, may constitute a broader conceptual category that could entail a richer landscape of effective theories beyond what experiments alone feasibly entail. Observations allow us to study parts of the universe whose complexity far exceeds our ability to precisely connect an initial preparation to a final measurement via unitary transformations in the laboratory. Accounting for this observed complexity suggests the development of effective theories across various domains, including biology, chemistry, complex systems theory, emergent phenomena, and cosmology. This extension of the optimization problem to observations, however, falls outside the scope of the current paper. |
| 3 | As statistical mechanics’ optimization problem does not reference an initial preparation, it could be argued, from these definitions, that it is based on observations and not on experiments. |
| 4 | This definition should not be taken as pejorative of observations. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).