A novel approach for quantum financial simulation and quantum state preparation

ArXiv ID: 2308.01844 “View on arXiv”

Authors: Unknown

Abstract

Quantum state preparation is vital in quantum computing and information processing. The ability to accurately and reliably prepare specific quantum states is essential for various applications. One of the promising applications of quantum computers is quantum simulation. This requires preparing a quantum state representing the system we are trying to simulate. This research introduces a novel simulation algorithm, the multi-Split-Steps Quantum Walk (multi-SSQW), designed to learn and load complicated probability distributions using parameterized quantum circuits (PQC) with a variational solver on classical simulators. The multi-SSQW algorithm is a modified version of the split-steps quantum walk, enhanced to incorporate a multi-agent decision-making process, rendering it suitable for modeling financial markets. The study provides theoretical descriptions and empirical investigations of the multi-SSQW algorithm to demonstrate its promising capabilities in probability distribution simulation and financial market modeling. Harnessing the advantages of quantum computation, the multi-SSQW models complex financial distributions and scenarios with high accuracy, providing valuable insights and mechanisms for financial analysis and decision-making. The multi-SSQW’s key benefits include its modeling flexibility, stable convergence, and instantaneous computation. These advantages underscore its rapid modeling and prediction potential in dynamic financial markets.

Keywords: Quantum Computing, Parameterized Quantum Circuits, Quantum Walks, Probabilistic Modeling, Variational Algorithms, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper is dense with advanced quantum mechanical formalism, unitary operators, and linear algebra, placing high on math complexity. However, it lacks empirical implementation details, code, backtests, or statistical performance metrics, focusing instead on theoretical proposals and simulation concepts.

  flowchart TD
    A["Research Goal: Simulate & Model<br>Complex Financial Distributions"] --> B["Key Method: Multi-Split-Steps<br>Quantum Walk Algorithm"]
    B --> C{"Data Input: Financial<br>Market Distributions"}
    C --> D["Computational Process:<br>Variational Quantum Circuit"]
    D --> E["Simulation Output:<br>Multi-Asset State Preparation"]
    E --> F["Key Findings:<br>High Accuracy & Stable Convergence"]
    F --> G["Outcome: Quantum-Enhanced<br>Financial Market Modeling"]