Abstract

Financial systems are entering a new computational paradigm defined by probabilistic engines, including advanced AI models and emerging quantum-assisted workflows. While these systems provide unprecedented modeling capabilities, they introduce a fundamental challenge: non-deterministic computation without verifiable guarantees.

This paper introduces a Trust Layer Architecture designed to transform probabilistic outputs into deterministic, cryptographically verifiable evidence, enabling auditability, regulatory compliance, and system-level trust without requiring re-execution.

The architecture is hardware-agnostic, compatible with AI-controlled and quantum-assisted systems, and aligns with emerging regulatory needs for traceability, reproducibility, and governance of autonomous decision-making systems.

1. Problem Statement

1.1 Structural Gap in Modern Financial Infrastructure

Emerging systems introduce three critical issues:

• -Non-deterministic outputs (AI inference, quantum sampling)

• -Opaque execution processes

• -Lack of reproducible audit trails

This creates a mismatch with regulatory requirements:

Requirement Current Systems

Auditability Limited

Reproducibility Not guaranteed

Explainability Partial

Trust without re-execution Not possible

2. Core Concept: Deterministic Evidence for Probabilistic Systems

We define:

Deterministic Evidence Layer (DEL)

A system that captures, seals, and verifies the full execution context of a probabilistic computation into an immutable and reproducible artifact.

3. Architecture Overview

3.1 Functional Layers

1. Execution Layer o AI inference engines / Quantum kernels / Ising optimizers

2. Telemetry Layer o Captures runtime conditions (noise, drift, parameters)

3. Evidence Construction Layer o Canonical serialization o Deterministic bundling o SBOM generation

4. Cryptographic Layer o Hashing (SHA-256) o Signing (Cosign-compatible) o Transparency logging

5. Governance Layer o Policy enforcement o Decision gating o Drift detection

6. Verification Layer o External reproducibility o Independent validation

4. System Diagram

Left → probabilistic systems (AI / Quantum)

Center → deterministic evidence transformation

Right → regulatory verification and audit

5. Key Innovation

5.1 Shift in Paradigm

From:

“Re-run to verify”

To: “Verify without re-execution”

5.2 Properties of the System

Property Description

Deterministic Evidence Identical hash for identical execution

Hardware Agnostic Works across QPU / GPU / hybrid systems

Cryptographic Integrity Tamper-proof verification

Regulatory Alignment Audit-ready outputs

Append-Only Provenance Immutable traceability

6. Financial Use Case

Use Case: AI + Quantum-Assisted Risk Evaluation

Scenario

A financial institution uses:

• AI models for credit risk scoring

• Quantum-assisted optimization for portfolio stress scenarios

Problem: Regulator asks:

“How do you prove this decision is correct and reproducible?”

Solution via QUVIO Trust Layer

Step-by-step:

1. Execution

o AI + quantum system generates probabilistic result

2. Capture

o Inputs, parameters, noise profile, hardware state

3. Bundle Creation

o Deterministic artifact generated

4. Cryptographic Sealing o Hash + signature + transparency log

5. Verification o Regulator validates without re-running system

Outcome

Capability Result

Audit Immediate

Reproducibility Guaranteed

Trust Cryptographically enforced

Compliance Strongly improved

7. Regulatory Relevance

Alignment with Emerging Frameworks

This architecture directly supports:

• AI Act (EU) → traceability, auditability

• DORA → operational resilience

• Basel frameworks → model risk management

• Future quantum governance → pre-standardization layer

8. Risk Considerations

Risk Mitigation

Misinterpretation of outputs Structured evidence schemas

Hardware variability Telemetry capture

Drift in AI systems Drift-guard layer

Security of evidence Cryptographic signing

9. Strategic Implication

This approach introduces a new infrastructure layer:

Trust as a Service for Probabilistic Computation

10. Conclusion

As financial systems evolve toward:

• AI-driven decision engines

• Quantum-assisted computation

• Autonomous execution pipelines

the fundamental requirement shifts:

From computation capability → to verifiability and trust

This architecture enables financial systems to trust probabilistic computation through deterministic, cryptographically verifiable evidence without requiring re-execution.

This contribution proposes a verifiable trust layer architecture for probabilistic computation in financial systems, enabling auditability and regulatory compliance for AI-driven and quantum-assisted infrastructures.