HomeResearchCoursesEventsBlogDiscussions
QuantumSparkQuantumSpark

Research-grade quantum learning, papers, textbooks, mentorship, and project work for motivated students.

Platform

  • Research
  • Outputs
  • Courses

Resources

  • Blog
  • Fellowship
  • Events
  • Research Standards

Trust

  • Terms
  • Privacy
  • Cookies
  • Security
  • Research Policies

Updates

Occasional notes on new resources, workshops, and student research opportunities.

© 2026 QuantumSpark.

Built for careful learning, reproducible work, and early research fluency.

Research
CompletedTQFT, Tensor Networks

Skein-Theoretic Membrane Order for Non-Abelian SET Phases

In quantum physics, scientists use mathematical tools to identify hidden symmetries in complex materials known as topological phases. Previous methods relied on simple numbers, or scalars, to measure these symmetries, which often accidentally erased crucial details about how particles interact. The current research introduces a new, matrix-based measurement tool. By keeping the full mathematical matrix rather than compressing it into a single number, physicists can now detect subtle, previously invisible interactions and structural details in non-Abelian quantum systems.

1 contributorsUpdated 9/17/2026

Project record

In quantum physics, scientists use mathematical tools to identify hidden symmetries in complex materials known as topological phases. Previous methods relied on simple numbers, or scalars, to measure these symmetries, which often accidentally erased crucial details about how particles interact. The current research introduces a new, matrix-based measurement tool. By keeping the full mathematical matrix rather than compressing it into a single number, physicists can now detect subtle, previously invisible interactions and structural details in non-Abelian quantum systems.Manuscript SummaryThe manuscript establishes a matrix-valued membrane observable to diagnose symmetry fractionalization in non-Abelian symmetry-enriched topological (SET) phases, acting as a robust replacement for incomplete scalar order parameters. The observable evaluates the symmetry action over a disk-like region RRR intersected by a ribbon operator for an anyon aaa with fusion-space labels μ\muμ and ν\nuν. The unnormalized matrix elements are defined as:

Ma(s)(R)μν=⟨Ψa,μ∣Us(R)Wa(∂R)∣Ψa,ν⟩⟨Ψa,μ∣Ψa,μ⟩⟨Ψa,ν∣Ψa,ν⟩M_{a}^{(s)}(R)_{\mu\nu}=\frac{\langle\Psi_{a,\mu}\vert{}U_{s}(R)W_{a}(\partial R)\vert{}\Psi_{a,\nu}\rangle}{\sqrt{\langle\Psi_{a,\mu}\vert{}\Psi_{a,\mu}\rangle\langle\Psi_{a,\nu}\vert{}\Psi_{a,\nu}\rangle}}Ma(s)​(R)μν​=⟨Ψa,μ​∣Ψa,μ​⟩⟨Ψa,ν​∣Ψa,ν​⟩​⟨Ψa,μ​∣Us​(R)Wa​(∂R)∣Ψa,ν​⟩​

To account for nonuniversal perimeter laws, a vacuum-renormalized version is utilized, constructed by dividing the matrix by the vacuum element M1,1(s)(R)M_{1,1}^{(s)}(R)M1,1(s)​(R). A central convergence theorem proves that in the large region limit, this renormalized matrix converges to the categorical symmetry intertwiner, expressed precisely as:

M^b,a(s)(R)=δb,ρs(a)Bρs(a)−1Xa(s)Ba+O(e−dist(a,∂R)/ξ)\hat{M}_{b,a}^{(s)}(R)=\delta_{b,\rho_{s}(a)}B_{\rho_{s}(a)}^{-1}X_{a}(s)B_{a}+O(e^{-dist(a,\partial R)/\xi})M^b,a(s)​(R)=δb,ρs​(a)​Bρs​(a)−1​Xa​(s)Ba​+O(e−dist(a,∂R)/ξ)

Here, Xa(s)X_{a}(s)Xa​(s) represents the symmetry action on the fusion space, while the BBB matrices encode gauge choices such as fusion basis and tensor-network gauges. The text notes that raw matrix entries are gauge covariant rather than invariant. Therefore, physical invariants must be extracted via conjugacy classes, singular values for anyon permutations, or traces of closed skein words. The work ultimately proves that retaining the full matrix structure allows the separation of SET phases that possess identical topological entanglement entropy and modular data but differ in multiplicity-sensitive symmetry actions.

Contributors

Rihaan Shah

Founder/Researcher

Resources

Research paper and resources

Attached PDFs and links open inside QuantumSpark when the source permits embedding.

Open externally

This resource could not be rendered inline.

Open resource