The Quantum Future of AI, Finance, and Human Institutions

 When Myth Meets Reality: An In-Depth Study of Deep Research Agents' Real-World Performance and Quantum Field Theory Implications

Executive Summary

This report provides a deep analysis of the actual performance of the most advanced "Deep Research Agents" in financial research tasks, based on the latest empirical results from leading research teams, including the National University of Singapore (NUS). The study reveals that while Generative AI has made breakthrough progress in text generation and task automation, its overall success rate plummets to only around 20% when applied to rigorous, high-precision financial analysis scenarios.

This report not only explains the fundamental reasons for the gap between AI's performance and market expectations but also establishes a new analytical framework through Quantum Field Theory (QFT) to reinterpret the dynamics of financial markets. It proposes a next-generation hybrid financial analysis architecture combining "Classical AI × Quantum Inspiration × Human Experts."

1. Research Background and Methodology

1.1 Research Design

The study employed two core instruments:

FinDeepResearch Dataset

• Covers 8 major global financial markets

• Includes 64 publicly listed companies

• Data types: Financial reports, industry information, historical market conditions, and event-driven data

• Task setting: Simulates the entire process of a professional analyst, from "Data Collection → Analysis → Synthesis → Report Writing"

HisRubric (247-Indicator Assessment Framework)

• Assessment dimensions:

• Information Accuracy

• Industry and Financial Interpretation Capability

• Inference Quality

• Report Format and Structure

• Design philosophy: Fully replicates sell-side analyst scoring standards

1.2 Tested Subjects

The subjects were the most advanced AI research agents currently available on the market, capable of autonomously gathering information, synthesizing data, and drafting comprehensive reports.

2. Core Findings: Performance Gaps Exceed Expectations

2.1 Overall Success Rate: A Mere 20%

AI agents successfully completed only one-fifth of the tasks, and even these required significant manual correction.

2.2 Key Capability Assessment

Information Accuracy: 37.9 / 100

This is the most critical and concerning aspect. Major errors include:

• Citing incorrect or non-existent financial figures

• Mixing data from different companies or different years

• Fundamental errors in calculating financial ratios

• Misinterpretation of financial statement line items

Advanced Interpretation Capability: 80% Failure Rate

Common issues:

• Inverting cause-and-effect relationships

• Ignoring industry context and nuances

• Over-reliance on lexical similarity rather than logical inference

• Inability to identify and articulate risk factors

Format and Structure: Significantly Outperforms Humans

• Can follow instructions perfectly in terms of structure

• High consistency in formatting

• Reports have a professional and polished appearance

This is currently the only part approaching "commercial readiness."

3. Deep Analysis of Failure Causes

3.1 Superficial Understanding and Lack of Conceptual Models

• Inability to grasp the logic and structure among the three financial statements

• Lack of common business sense and judgment

• Failure to adjust analysis angles based on the "different dynamic mechanisms" of various industries

3.2 Insufficient Reasoning Ability

• Multi-step reasoning easily accumulates errors

• Cannot perform counterfactual reasoning

• Does not autonomously question suspicious data points

3.3 Knowledge Integration Difficulty

• Inability to synthesize financial, industry, event, and macroeconomic data

• Slow updating to market dynamics

• Lack of expert intuition and heuristics

4. Quantum Field Theory Perspective: A Deeper Explanation for AI's Limitations

This chapter represents the innovative core of this report: redefining the "essential structure" of financial markets from a Quantum Field Theory (QFT) perspective and explaining why conventional AI is fundamentally constrained in this domain.

4.1 Why is the QFT Framework Necessary?

The financial market is not merely a "collection of data tables," but a complex system characterized by non-linearity, field interactions, and superposition states.

Limitations of Conventional AI:

• Discrete data points

• Probabilities are static

• Pattern recognition is local, lacking a global field sense

Financial Market Characteristics Resemble a Quantum Field:

• States change continuously

• Non-local correlation (instantaneous cross-market reaction)

• Multiple possibilities coexisting simultaneously

• Emotions and information act as excitations of the field

4.2 Four Characteristics of Financial Markets under the QFT Framework

Characteristic 1: Vacuum Fluctuations ≈ Market Noise

• Market noise is not just error; it may contain information.

• AI struggles to distinguish "signal within the noise" from "noise within the signal."

Characteristic 2: Non-local Correlation ≈ Global Market Entanglement

• A shock in the US market instantaneously affects Asian market futures.

• Investor sentiment exhibits collective coupling.

• AI lacks the ability to capture cross-market, quantum-like correlations.

Characteristic 3: Observer Effect

• The analysis report itself changes the market.

• A company's financial report exists in a "superposition state" before its release.

• AI fails to understand that the analyst is also part of the market system.

Characteristic 4: Renormalization and Scale Dependence

• Short-term and long-term views belong to different "Effective Field Theories."

• Events like market crashes are Phase Transitions.

• AI lacks the ability to automatically select the appropriate scale for analysis.

4.3 Explaining AI's Three Fundamental Failures from a Field Theory Perspective

(1) Discretization Error (Lattice Error)

• Inability to handle the continuous dynamics of the market.

(2) Lack of Feynman Path Integral Perspective

• Cannot simultaneously consider multiple future paths, nor understand path dependence.

(3) Symmetry Breaking Blind Spot

• Inability to detect bubble formation, pre-crash indicators, or changes in order parameters.

5. Repositioning: AI's Correct Role in Financial Analysis

AI should not be the "analyst," but the "analyst augmentor."

5.1 The Centaur Model: The Optimal Strategy

AI → Responsible for data-intensive, standardized, and automatable tasks.

Humans → Handle judgment, inference, insight, and risk decision-making.

5.2 The Three-Layer Hybrid Architecture Inspired by QFT

Layer 1: Classic AI Layer (Current Technology)

• Data processing

• Report generation

• Pattern recognition (low-energy approximation of Effective Field Theory)

Layer 2: Quantum-Inspired Layer

• Quantum Annealing: Combinatorial optimization

• Quantum Sampling: Accelerating Monte Carlo simulations

• Handling non-linearity and high-dimensional coupling problems

Layer 3: Human Expert Layer

• Selecting the analysis scale (Renormalization Group)

• Identifying phase transitions

• Providing innovative insights

6. Financial Applications of Quantum Computing × Field Theory Thinking

6.1 Three Major Breakthrough Directions for Quantum Computing

(1) Portfolio Optimization

• Quantum Annealing to solve high-dimensional portfolio problems

• VQE to handle complex risk models

(2) Risk Assessment and Stress Testing

• Quantum Amplitude Estimation to accelerate Monte Carlo

• Better capability to capture tail risks

(3) Derivative Pricing

• Quantum PDE solvers

• Using Quantum Walk to simulate path dependence

6.2 New Tools Inspired by Field Theory

Market Field Theory (MFT)

• Treats price as field excitation

• Bubbles = Phase transitions

• Systemic risk = Increased field coupling strength

Renormalization Group (RG) Analysis for Markets

• Different time scales correspond to different effective theories

• Identifying market critical exponents

Feynman Diagram Market Analysis

• Using graph theory to map market interactions

• Calculating the contribution of each path to the market state

6.3 Technology Maturity and Timeline

2025–2028: Hybrid Classical-Quantum Era

• Achieving 2–10x acceleration

• Preliminary adoption of quantum finance engineering

2028–2033: Fault-Tolerant Quantum Computing Era

• Real-time pricing of complex derivatives

• Genuine multi-market coupled simulation

• Risk warning systems enter a new phase

7. Future Outlook

7.1 Short-Term Development (1–3 years)

Over the next three years, the development of AI and quantum-related technologies in financial analysis will primarily focus on tool optimization and process integration:

• AI efficiency in data processing, summarization, and report formatting will continue to improve.

• Human-computer collaboration interfaces will mature, enhancing the integration of analysis, risk control, and investment workflows.

• The precision of specialized models for specific tasks (e.g., corporate scoring, financial report analysis, industry comparison, factor modeling) is expected to breakthrough.

• Quantum-Inspired Algorithms will begin pilot applications in portfolio optimization and solution space exploration.

7.2 Mid- to Long-Term Challenges and Opportunities (3–10+ years)

For AI to truly become a reliable analytical tool in finance, current limitations must be overcome across three major layers: technology, theory, and the ecosystem.

(I) Technology Layer

• Deep Reasoning Capability: Accuracy in cross-paragraph, multi-step logical inference must be urgently improved.

• Common Sense Integration: AI must embed commercial common sense, basic logic, and real-world constraints.

• Contextual Understanding: Models must adjust based on different market environments, regulatory backgrounds, and country factors.

• Explainability: Practitioners must understand the AI's chain of reasoning to trust model decisions.

• Quantum Hardware Maturity: Fault-tolerant quantum computing needs to truly enter the large-scale operational stage.

(II) Theoretical Layer

• Establishing a complete "Financial Field Theory" mathematical framework.

• Developing fundamental theories of market dynamics analogous to the physical "Standard Model."

• Understanding the market's "vacuum structure," phase transition mechanisms, and unstable states.

• Exploring whether economic phenomena have valid quantum analogues (superposition, interference, field excitation, etc.).

(III) Ecosystem Layer

• Training and building a talent pool of Quantum Finance Engineers across disciplines.

• Strengthening collaboration mechanisms between industry, academia, and research institutions.

• Updating the regulatory framework for AI/Quantum finance.

• Launching forward-looking research on ethics, social impact, and risk governance.

7.3 The Potential for a Paradigm Shift

When quantum computing and field theory thinking truly integrate into financial analysis, it could lead to a third paradigm shift in financial research and market behavior.

(I) From "Prediction" to "Computation"

• Traditional: Relying on historical data to extrapolate the future.

• New Paradigm: Calculating the distribution of "all possible paths," similar to quantum amplitudes, to gain a superposition-based predictive view.

(II) From "Risk Management" to "Field Modulation"

• Traditional: Passively quantifying and hedging risk.

• New Paradigm: Actively modulating market structure and reducing the risk of Systemic Phase Transitions.

(III) From "Efficient Market" to "Quantum Market"

• Traditional EMH: Price reflects all information.

• New Paradigm: Price is in a "superposition state" of information; trading behavior changes the market's quantum state.

8. Conclusion and Recommendations

Core Conclusions

• Significant Reality Gap: AI's current capabilities are far below the promotion and expectations within the financial industry.

• AI as Augmentor: AI will not replace analysts but will serve as a tool to enhance human capabilities.

• Human Centrality: The most critical analysis and judgment must remain in human hands.

• New Framework: Quantum Field Theory offers a novel framework for understanding AI's limitations and charting breakthrough directions.

• Paradigm Shift Brewing: Within 10–15 years, field theory and quantum computing may fundamentally reshape financial analysis methods.

Action Recommendations

• Prudent Adoption: Introduce AI but with strict validation and verification mechanisms.

• Continuous Learning: Practitioners should acquire foundational knowledge in AI, quantum computing, and field theory.

• Realistic Expectations: Avoid blind faith in AI, but maintain technological sensitivity.

• Regulatory Focus: Establish appropriate norms for AI financial assistance services.

• Interdisciplinary Collaboration: Combine finance × physics × computer science expertise.

• Investment in Foundational Research: Support the theoretical construction of quantum finance and financial field theory.

References

Empirical Research Sources

• Leading research institutions including the National University of Singapore

• Assessment frameworks: FinDeepResearch Dataset, HisRubric System

• Coverage: 8 markets, 64 companies, 247 indicators

Theoretical Frameworks

• Quantum Field Theory (QFT)

• Renormalization Group (RG)

• Quantum Computing, Quantum Algorithms

• Complex Systems and Phase Transition Theory

Further Reading

• Feynman, QED

• Steven Weinberg, Quantum Field Theory

• Mandelbrot, The (Mis)Behavior of Markets

• Nielsen & Chuang, Quantum Computation and Quantum Information

Reflection Points

• To what extent would you trust the analysis results from an AI?

• What capabilities is AI still missing to become a reliable analyst?

• How should financial professionals adjust their skill sets?

• Will quantum computing bring practical impact during your career?

• What insights can the analogy between the "Financial Field" and the "Quantum Field" offer?

• If the market possesses quantum properties, does the EMH still hold?