UNLOCK THE FUTURE OF
FINANCE WITH QUANTUM

Advancing Innovation in Quantum-AI Hybrid Systems
Presented by: Dr. Aaron Loeb

CLICK HERE AND UNLOCK THE FUTURE OF FINANCE WITH QUANTUM COMPUTING

Table of Contents

No prior coding or quantum experience needed

✅ Real-world financial use cases explained clearly

✅ Learn at your own pace, from anywhere

Research
Background

Q-FinPredict: Hybrid Quantum-Classical
AI for High-Frequency Trading
Field: Quantum Machine Learning

Research Problem Why This Study?
✔ Quantum algorithms for next-generation
financial optimization.”
“Harnessing quantum power to solve complex
financial problems.”
“Quantum computing’s impact on future financial
innovation.”
“Accelerating finance with quantum-driven
solutions.”

✔ “To understand quantum computing’s future role
in finance.
”

“To reveal faster, smarter financial solutions
through quantum tech.
”

“To examine quantum technology’s impact on
financial innovation.
”

“To bridge quantum computing and real-world
financial challenges.

Objectives &
Hypothesis

1.Identify quantum solutions for financial problems.
2.Explore quantum algorithms for finance.
3.Assess benefits of quantum speed and accuracy.
4.Evaluate quantum impact on risk and trading.
5.

Quantum-enhanced AI will outperform classical models
by 30% in high-frequency trading
Primary Objectives

Hypothesis

Literature Review

Quantum computing offers exponential speedups.
Financial models struggle with complex datasets.
Quantum algorithms improve optimization tasks.
Researchers link quantum tech to portfolio
management.
Studies show potential in risk analysis and
cryptography.
Early experiments reveal faster trading simulations.
Literature highlights challenges in practical
implementation

Methodology

✔ Wardiere Inc. quantum cloud + Timmerman

Industries trading sim

Test Environment

Technical
Development

✔ 90% faster backpropagation
✔ Error-corrected quantum memory

✔ Qubit decoherence during market opens
✔ Mitigation: Dynamic circuit recompilation

Innovations

Challenges

Expected Outcomes
Faster financial computations.
Improved optimization and risk analysis.
Enhanced trading and forecasting accuracy.
Clearer understanding of quantum–finance
integration.
Identification of practical quantum use cases

Phase Duration Milestone

q4-q4-2024 Design & Simulation Architecture approved

Q3-Q4 2024 Prototype Dev First live trade executed

Q1 2025 Optimization Latency <100μs achieved

Q2 2025 Commercial Prep Warner & Spencer pilot
Project Timeline

Category Cost

Quantum Compute $1.2M

AI Training $750K

Security Audit $300K
Budget

Research Team

✔ Dr. Itsuki Takahashi (PhD Quantum Comp, Borcelle)

Principal Investigator

Where quantum
meets Wall Street

1M+

THANK YOU FOR YOUR

ATTENTION!

Advancing Innovation in Quantum-AI Hybrid Systems
Presented by: Dr. Aaron Loeb