# Depth-Efficient Quantum Topological Data Analysis for Regime-Specific Detection of Financial Stress

arXiv: https://arxiv.org/abs/2607.09906 · HTML: https://mlsanity.com/paper/2607.09906/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 10 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [A roadmap for the computation of persistent homology](https://mlsanity.com/paper/1506.08903/) (2015)
- [Ripser: efficient computation of Vietoris-Rips persistence barcodes](https://mlsanity.com/paper/1908.02518/) (2019)
- [Towards quantum advantage via topological data analysis](https://mlsanity.com/paper/2005.02607/) (2020)
- [Quantum Topological Data Analysis with Linear Depth and Exponential Speedup](https://mlsanity.com/paper/2108.02811/) (2021)
- [From Betti Numbers to Persistence Diagrams: A Hybrid Quantum Algorithm for Topological Data Analysis](https://mlsanity.com/paper/2512.02081/) (2025)

## Abstract (arXiv)

We present, to our knowledge, the first adaptation of Pauli Correlation Encoding (PCE) to quantum topological data analysis, reformulating Betti number estimation as a depth-efficient variational optimization over a compressed qubit register. From a Takens embedding and Vietoris--Rips filtration of S&P~500 returns, we extract combinatorial Laplacians and recast null-space counting as a continuous-PCE Rayleigh-quotient minimization with variational deflation, encoding nk simplex indices into O(nk1/kappa) qubits with shallow, ancilla-free circuits. Because the resulting loss is rational rather than bilinear in the correlators, the barren-plateau bound of~citeSciorilli25 does not transfer; empirically the gradient variance decays only polynomially, with no exponential barren plateau, over n=4--12 qubits. The classical stage matches ripser~citebauer2021ripser on all 190 sliding windows (2007-2009). On the real market Laplacians (β1=1--22), warm-starting from a classical null-space surrogate allows PCE-VQE to recover β1 exactly at every scale, placing the obstacle in the optimisation landscape rather than the encoding. Chronologically split classification gives in-regime ROC AUC 0.818, but out-of-distribution evaluation on the 2020 COVID shock and 2022 rate cycle (AUC 0.009, 0.515) shows the calibration does not generalize across crisis regimes.