Quantum Risk Rating Circuits for Probabilistic Credit Assessment
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Solution Overview
Problem
Existing credit rating methods are limited by their inability to efficiently process and incorporate large quantities of financial data in a timely manner, leading to deterministic and static risk assessments that do not account for the inherent volatility and complexity of financial instruments.
Innovation Solution
A quantum computing-based framework that generates stochastic quantum risk values from deterministic inputs, utilizing N-qubit quantum circuits to process exponentially large data sets and incorporate volatility information, followed by classical parameter modification to produce a final quantum risk rating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deterministic calculations are used for credit risk rating, then the calculation process is simple and straightforward, but the method fails to capture the dynamic and probabilistic nature of financial instrument risks
Solution Approach 1:
The patent replaces the classical deterministic calculation system with a quantum computing system. Quantum computers use quantum bits (qubits) that can exist in superposition states, allowing them to process multiple risk scenarios simultaneously. This substitution enables the system to capture the probabilistic nature of financial risks while maintaining computational efficiency through quantum parallelism.
Solution Approach 2:
The patent transforms the calculation paradigm from deterministic fixed values to probabilistic quantum states. By representing risk factors as quantum superpositions of multiple possible values, the system can model the dynamic and uncertain nature of financial instruments. The quantum computer evolves these states according to quantum mechanical principles, naturally incorporating volatility and uncertainty into the risk assessment.
2Productivity
If classical computation methods are used, then the processing approach is straightforward, but the ability to process exponentially large data sets efficiently is limited
Solution Approach 1:
The patent merges quantum computing capabilities with classical credit risk assessment frameworks. The quantum computer processes the exponentially large space of possible risk outcomes through quantum parallelism, while the classical system provides the structured methodology for evaluating credit risk. This hybrid approach leverages the strengths of both computational paradigms to achieve efficient processing of complex risk data sets.
Solution Approach 2:
The patent transitions from classical binary computation to quantum computation, adding the dimension of quantum superposition and entanglement. This dimensional change allows the system to represent and process exponentially more states simultaneously. N qubits can represent 2^N classical states at once, enabling efficient processing of exponentially large data sets that would be intractable for classical computers.
Data Source
AI summary
A computer-implemented method for calculating risk is provided. The method comprises receiving, at a first time, respective deterministic values for a number of factors. Over an evolution time interval starting at the first time, respective stochastic quantum values are generated for each factor from the deterministic values. Each factor is represented by a respective N-qubit quantum circuit, wherein each qubit represents a risk level. The quantum values for the factors are combined into an intermediate quantum risk value at a second time at the end of the evolution time interval. The intermediate quantum risk value according is then modified to a number of classical parameters, and a final stochastic quantum risk value at the second time is generated from the modified intermediate quantum risk value.