Junction X OP Pohjola: QSVT4CRA — silver ========================================== :Date: 2026-06-09 :Tags: quantum, hackathon, junction, op-pohjola, var, qsvt, npe, factor-copula, sbi :Original post: `LinkedIn `_ My first time in the quantum world — and we took **2nd place on Junction X OP Pohjola's challenge** with QSVT4CRA: a Quantum Singular Value Transformation pipeline for Value-at-Risk estimation that fuses amortised Bayesian inference with a quantum block-encoding of the learned factor-copula. This note preserves the LinkedIn write-up and adds a small amount of context. The team, in full: **Milla Kolehmainen, Antti Nurminen, Silja Heiskanen** — and huge credit to mentor **Juha Vesanto**. The LinkedIn post ----------------- .. raw:: html
I spent the weekend knee-deep in the quantum world, first time for me, and we won 2nd place on Junction X OP Pohjola's challenge! It couldn't have been possible without my team Milla Kolehmainen, Antti Nurminen, Silja H. and mentors such as the amazing Juha Vesanto. We approached the challenge from a real problem: banks like OP run ~10k Monte Carlo simulations daily to estimate tail-risk, VaR and CVaR for different portfolios and assets. Current classical methods: 1. Scale poorly to higher dimensions ``O(N·d·exp{c·d})`` 2. Have slow error decay ``O(sqrt{1/N})``. While advanced classical methods such as HMC and SVI offer benefits over the traditional MC we wanted to research if going quantum-native could offer a speedup. We researched Quantum Singular Value Transformations and Quantum Amplitude Estimation for scalable VaR estimation for capital planning and stress testing. We found: - Standard QAE requires ``k`` expensive ancilla qubits for ``k`` assets. - QSVT operates updates on single ancilla qubit but produces deeper circuits. - State loading eats up all of the quantum advantage if done poorly: ``k`` assets get encoded to ``2^k`` amplitudes. - Pre-calculated distributions (that would amortize state-prep) such as Gaussian Conditional Independence models scale poorly to Out-of-Distribution regimes (market shocks). Our pipeline: - Train NPE over different regimes to output posterior samples for factor-copula given some data. - Block-encode factor-copula params (output of single forward pass on trained NPE) to QSVT unitary. - Compile circuit, and run QAE to output VaR+CVaR. We were able to run our pipeline for a 12-asset portfolio on real IBM hardware (Boston) with ~10% error on VaR estimate. The error is due to decoherence and compounding gate error. Our work is fully open-source and documented in the project repo. .. raw:: html
The Heron r3 scaling study -------------------------- The chart below is the headline result — a **VaR-95 tail-estimate error sweep on IBM Heron r3** as the QSVT polynomial degree grows from 2 to 32, holding the shot budget fixed at 12 repetitions × 4096 shots per degree. .. figure:: ../_static/junction-quantumhack-qsvt4cra-chart.png :alt: Heron r3 posterior degree-vs-error :width: 100% Heron r3 VaR-95 tail-estimate error vs QSVT polynomial degree. Error rises with degree and circuit depth, peaking around ~11% at degree 20; the green dashed line is the ideal target (zero error). The right axis shows the transpiled circuit depth growing roughly linearly in degree. Honest read: the pipeline runs end-to-end on a real superconducting QPU, but VaR-95 error scales with circuit depth in the regime we tested. Improving the depth-error tradeoff — better state-loading, shallower polynomials, or error mitigation — is the obvious next step. What carried over from the rest of the work ------------------------------------------- The factor-copula + amortised-Bayes framing is the same trick that anchors `StimIQ `_ and `EASE Health `_: encode the structure you understand into a forward model, sample parameters from a posterior, and let the downstream algorithm consume those samples. Here the downstream algorithm is a QSVT unitary rather than a clinician-facing optimisation, but the epistemics are the same. The **formal** companion to this work is :doc:`the Lean 4 / Fable 5 proof <2026-06-15-fable5-lean4-copulas-quantum>` that any copula vertex lifts to a quantum circuit in O(d²) two-qubit gates. Together, the empirical pipeline (this note) and the formal bound close the copula-to-quantum loop. Links ----- - :doc:`Project page: QSVT4CRA <../projects>` - Repo: https://github.com/PlayerPlanet/QSVT4CRA - Original post: https://www.linkedin.com/feed/update/urn:li:activity:7470019622730248193/ - Track: Junction X OP Pohjola