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Governed Fault-Tolerant Quantum Computing on Commodity NISQ Hardware — companion notebook and audit datasets

Denis Ovseyenko, Denise Holt

Published Jun 14, 2026• 0 citations
Publisher

Abstract

This is the executable companion notebook to the paper Governed Fault-Tolerant Quantum Computing on Commodity NISQ Hardware — covering per-run audit datasets, structured commit records, and a sample of the transpiled OpenQASM 3.0 circuit corpus from the campaign. The notebook ships with all outputs (tables, figures, IBM circuit images) embedded.

DOI
10.5281/zenodo.20680916

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Abstract We demonstrate governed fault-tolerant quantum computing (GFTQC) applied to financial risk: every number is computed exactly, with a precision and reproducibility no sampling method can reach. Every risk number a financial institution must produce, a value-at-risk, an expected shortfall, a valuation adjustment, and the price and Greeks beneath them, is at bottom an expectation: a probability-weighted integral of a payoff over the distribution of future market paths. The industry evaluates these integrals by Monte-Carlo sampling, and sampling has two structural defects. Its statistical error falls only as 1/√N in the number of paths, so each additional digit costs a hundredfold more compute; and the estimate drifts from one run to the next, so it cannot deliver the auditable, reproducible numbers the FRTB P&L Attribution (PLA) test demands. The industry’s response has been to sample faster: NVIDIA’s audited STAC-A2 record [26], the benchmark for derivatives-risk computation, runs 561 option-valuations per second on an 8×H100 GPU server over 316 million paths, which is faster sampling and still stochastic. The quantum proposals do not close the gap either: the near-term methods (VQE, QAOA, QPE) only estimate, and the machines that would carry the field past sampling are a 2030+ prospect, not today. The field, in short, is caught between classical sampling that never becomes exact (Monte Carlo, quasi-Monte Carlo, GPU-accelerated) and quantum methods that either only estimate today (VQE, QPE, QAOA) or await a 2030+ roadmap. Governed fault-tolerant quantum computing (GFTQC) sits in neither camp: it commits the exact value on hardware that exists now. We compute the risk on quantum hardware that exists now, and we compute it exactly and deterministically. Under Seed IQ, a fault-tolerant quantum computation on the 156-qubit IBM Heron QPU encodes the risk-neutral density as a governed quantum register and computes its expectation at a self-certified eigenstate, rather than estimating it from repeated measurement. This is the same governed determinism our companion fault-tolerant quantum work established on the very same hardware—FCI-exact hydrogen-chain energies committed on IBM Boston (Heron r3), internally certified exact and reproducible to twelve decimal places [1, 2]; here that deterministic, on-hardware exactness is turned on the derivatives integral. The dissipative dynamics converge to the target eigenstate and the output is exact to machine precision, with zero variance and byte-identical on rerun—the 10−12 price no sampler reaches at any budget, delivered directly. Across structured products, factor-model risk, XVA and expected shortfall the story is the same: exact where Monte Carlo is noisy, reproducible where it drifts. That the same exact computation is also 35× faster than NVIDIA’s eight-GPU record on the audited STAC-A2 workload is a consequence of not sampling, not the headline; the headline is that the number is right to the last digit. The gap is not incremental. What separates this work from the standard quantum-finance roadmap is not a constant factor but the machine itself (Fig. 2): textbook fault-tolerant pricing needs a computer of 10^6–10^9 physical qubits with distillation factories, projected for the 2030s; GFTQC commits the same exact, fault-tolerant risk numbers today on a 156-qubit superconducting IBM Heron QPU, a ∼10^6× smaller physical footprint. Exact, reproducible, auditable financial risk is available now, not next decade.

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•Dec 26, 2025•0 citations
This is the executable companion notebook to the paper Governed Fault-Tolerant Quantum Computing on Commodity NISQ Hardware — covering per-run audit datasets, structured commit records, and a sample of the transpiled OpenQASM 3.0 circuit corpus from the campaign. The notebook ships with all outputs (tables, figures, IBM circuit images) embedded.
A low-level OpenQASM benchmark suite for NISQ evaluation and simulation. Please see our paper for details. n this repository you will find a low-level and light-weighted benchmark suite based on IBM OpenQASM language (see spec). It collects commonly seen quantum algorithms and routines from various domains including chemistry, simulation, linear algebra, searching, optimization, arithmetic, machine learning, fault tolerance, cryptography, etc. QASMBench trades-off between generality and usability, covering the number of qubits ranging from 2 to 60K, and the circuit depth from 4 to 12M
Abstract We demonstrate governed fault-tolerant quantum computing (GFTQC) applied to financial risk: every number is computed exactly, with a precision and reproducibility no sampling method can reach. Every risk number a financial institution must produce, a value-at-risk, an expected shortfall, a valuation adjustment, and the price and Greeks beneath them, is at bottom an expectation: a probability-weighted integral of a payoff over the distribution of future market paths. The industry evaluates these integrals by Monte-Carlo sampling, and sampling has two structural defects. Its statistical error falls only as 1/√N in the number of paths, so each additional digit costs a hundredfold more compute; and the estimate drifts from one run to the next, so it cannot deliver the auditable, reproducible numbers the FRTB P&L Attribution (PLA) test demands. The industry’s response has been to sample faster: NVIDIA’s audited STAC-A2 record [26], the benchmark for derivatives-risk computation, runs 561 option-valuations per second on an 8×H100 GPU server over 316 million paths, which is faster sampling and still stochastic. The quantum proposals do not close the gap either: the near-term methods (VQE, QAOA, QPE) only estimate, and the machines that would carry the field past sampling are a 2030+ prospect, not today. The field, in short, is caught between classical sampling that never becomes exact (Monte Carlo, quasi-Monte Carlo, GPU-accelerated) and quantum methods that either only estimate today (VQE, QPE, QAOA) or await a 2030+ roadmap. Governed fault-tolerant quantum computing (GFTQC) sits in neither camp: it commits the exact value on hardware that exists now. We compute the risk on quantum hardware that exists now, and we compute it exactly and deterministically. Under Seed IQ, a fault-tolerant quantum computation on the 156-qubit IBM Heron QPU encodes the risk-neutral density as a governed quantum register and computes its expectation at a self-certified eigenstate, rather than estimating it from repeated measurement. This is the same governed determinism our companion fault-tolerant quantum work established on the very same hardware—FCI-exact hydrogen-chain energies committed on IBM Boston (Heron r3), internally certified exact and reproducible to twelve decimal places [1, 2]; here that deterministic, on-hardware exactness is turned on the derivatives integral. The dissipative dynamics converge to the target eigenstate and the output is exact to machine precision, with zero variance and byte-identical on rerun—the 10−12 price no sampler reaches at any budget, delivered directly. Across structured products, factor-model risk, XVA and expected shortfall the story is the same: exact where Monte Carlo is noisy, reproducible where it drifts. That the same exact computation is also 35× faster than NVIDIA’s eight-GPU record on the audited STAC-A2 workload is a consequence of not sampling, not the headline; the headline is that the number is right to the last digit. The gap is not incremental. What separates this work from the standard quantum-finance roadmap is not a constant factor but the machine itself (Fig. 2): textbook fault-tolerant pricing needs a computer of 10^6–10^9 physical qubits with distillation factories, projected for the 2030s; GFTQC commits the same exact, fault-tolerant risk numbers today on a 156-qubit superconducting IBM Heron QPU, a ∼10^6× smaller physical footprint. Exact, reproducible, auditable financial risk is available now, not next decade.
Abstract This paper reports the first end-to-end fault-tolerant quantum computing stack to clear all four strict-FTQC requirements simultaneously on commodity superconducting NISQ silicon, rented through standard IBM Quantum cloud subscription with no purpose-built research machine, no custom decoder, and no magic-state distillation factory. The four requirements: (i) surface-code quantum error correction at distance d≥3 with logical-error rate below the unencoded baseline; (ii) a universal Clifford+T gate set executed via magic-state injection, with no arbitrary angle physical rotation carrying non-Clifford content on the wire (the algorithm-determined non-Clifford angle never fires on the silicon; the native RZ(θphys) on Heron fires only at Clifford-multiple angles required to compose H, S, T); (iii) heterogeneous primitive composition (teleportation, lattice-surgery CNOT, T-gate, logical memory) on a persistent encoded register; and (iv) runtime admissibility verification on every committed result. All five chips, five chemistry workloads spanning four molecules, and twenty-two governed runs in this paper were executed on the same primitive engine without architectural change. The architectural shift that makes this work on today’s hardware is the d=1 inversion: a 150-qubit governed encoded register held at substrate distance d=1 on the chip’s physical qubits, with logical fidelity preserved by Seed IQ execution governance over the encoded layer rather than by a larger code distance. Where the standard surface-code paradigm grows physical d quadratically to drive the logical-error rate down—and pays for it with magic-state fidelity that degrades as gate depth scales—Seed IQ governance acts as an operational substitute for code distance: it delivers the equivalent error-rate reduction in software while leaving the per-gate channel fidelity available for T-gate injection and Clifford operations intact. The result: the four strict-FTQC requirements hold simultaneously at approximately one physical qubit per logical qubit, against the ∼ 625 physical qubits per logical qubit required by the IBM 2029 roadmap target of d=25 surface code at comparable logical fidelity. Seed IQ is an Active Inference and Adaptive Multiagent Autonomous Control (AMAC) platform from AIX Global Innovations. Campaign summary. Eight stages, April through June 2026, across five IBM Heron processors (r2: IBM Fez, IBM Kingston, IBM Marrakesh, IBM Pittsburgh; r3: IBM Boston). April 9 — surface-code QEC: −88.5% LER reduction at d=3 on IBM Fez, −93.1% at d=5 where the MWPM+UF baseline collapsed, −60.9% at d=3 on 1.28°ø dirtier IBM Pittsburgh silicon. April 23–25 — all four universal FTQC primitives cleared the classical 2/3 entanglement bound at F≈0.99. April 25–26 — heterogeneous TELE→CNOT→T→CNOT→TELE°ø2 composition: 22,500 circuits at Fgoverned=1.0000, zero detected logical errors. May 7–9 — H2, 10 runs across 3 chips, 10/10 inside chem-acc, best ΔE=+0.0157mHa, two chips bit-identical to twelve decimals. May 23 —LiH, 3 runs across 3 chips, 3/3 inside chem-acc, two chips tied at ΔE=+0.056mHa. May 28–30 — H2O, 3 runs across 3 chips, 3/3 inside chem-acc at ΔE=+0.051mHa, all three bit-identical to twelve decimals. May 30–31 — BeH2 equilibrium, 3 runs across 2 chips, 3/3 inside chem-acc at ΔE=+0.000595mHa (2690.9°ø inside, the only sub-wavenumber QPU chemistry commit on record), all three bit-identical to twelve decimals. June 1 — BeH2 transition state (|cHF |2≈0.5 strong multireference, the canonical territory where single-reference CCSD breaks), 3 runs across two Heron processor generations at two Newton optimization depths on the rank-5 C2v trial-state subspace, 3/3 inside chem-acc on a 62-Pauli-term Hamiltonian; the two 2-D Newton runs (IBM Boston Heron r3, IBM Kingston Heron r2) commit bit-identical to twelve decimals at ΔE=+0.018189mHa, and the 4-D Newton run commits at the deeper fixed point at ΔE=+0.012643mHa (126.6°ø inside chem-acc, 1.6°ø inside the CCSD(T) gold band). Aggregate: twenty-two runs, 22/22 inside chemical accuracy. No prior NISQ-chemistry or partial-FTQC-chemistry result on commodity hardware has cleared this combination of accuracy and cross-chip reproducibility. Full per-stage detail in Åò4–Åò12.8. Compliance. All twenty-two committed chemistry runs and every primitive composition stage passed the four-requirement FTQC compliance check: closed Clifford+T compilation, magicstate-injected T-gates, encoded-register fidelity preservation, runtime admissibility verification. Fgoverned=1.0000 is the runtime admissibility pass-rate—every committed primitive cleared every admissibility check across the composition pipeline (22,500 composed circuits per run, replicated across two independent calibration windows for 45,000 circuits total) and the twenty-two chemistry runs, with zero detected logical errors, computed from per-shot stabilizer-syndrome statistics, entanglement-witness measurements, and the three-stage admissibility check under live IBM Heron hardware noise (Åò7, Åò13.13). The twelve-decimal cross-chip identity is the engineered signature of Seed IQ’s admissibility-and-projection contract: the three-stage envelope is designed so that noisy QPU measurement vectors from materially different chips identify the same Newton fixed point on the trial-state subspace; the committed energy is then the Hamiltonian sum E=Pi ci⟨Pi⟩ at that shared fixed point evaluated directly from each chip’s own QPU shot record by direct bitstring counting at the continuous coordinate—no binning, no precomputed energy curve, no offline lookup. IEEE 754 doubles are just the number format the energies are reported in; what makes the twelve decimal places physically meaningful is that materially different chips, with materially different noise, agree at all twelve. That agreement is the operational evidence the envelope is delivering what it was engineered to: an admissibility-and-projection contract that maps independent noisy QPU measurement vectors to the same fixed point on the trial-state subspace (Åò13.10, Åò14).