Quantum Circuit-Based Adaptation for Credit Risk Analysis

Noisy and intermediate-scale quantum (NISQ) processors are sensitive to noise, prone to quantum decoherence, and are not yet capable of continuous quantum error correction for fault-tolerant quantum computation. Hence, quantum algorithms designed in the prefault-tolerant era cannot neglect the noisy nature of the hardware, and investigating the relationship between quantum hardware performance and the output of quantum algorithms is essential. In this work, we experimentally study how hardware-aware variational quantum circuits on a superconducting quantum processing unit can model distributions relevant to specific use-case applications for credit risk analysis, e.g., standard Gaussian distributions for latent factor loading in the Gaussian Conditional-Independence model. We use a transpilation technique tailored to the specific quantum hardware topology, which minimizes gate depth and connectivity violations, and we calibrate the gate rotations of the circuit to achieve an optimized output from quantum algorithms. Our results demonstrate the viability of quantum adaptation on a small-scale, proof-of-concept model inspired by financial applications and offer a good starting point for understanding the practical use of NISQ devices.