Quantum Information & Computation
Volume 26
RESEARCH ARTICLE Open access

Hybrid Circuit–Spintronic Quantum Framework for Financial Risk Analysis with QCVaR Estimation Using Variational Quantum Algorithms and Maximum-Likelihood Amplitude Estimation

Gayathri S. S., Muthulakshmi P., R. Palanivel

Author affiliations
  • Gayathri S. S.1
  • Muthulakshmi P.2
  • R. Palanivel2
  1. Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu, Tamil Nadu, India
  2. Department of Computer Science, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India

pp. 20-37 · 10.2478/qic-2026-0002

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Abstract

Problem Statement

Accurate estimation of extreme financial risks, such as Conditional Value-at-Risk (CVaR) at high confidence levels (α ≥ 0.95), poses significant computational challenges for classical Monte Carlo methods, which require O(1/ ϵ2) samples and struggle to scale under rare-event scenarios.

Methodology

To address this, we propose a hybrid variational quantum–spintronic framework integrating Variational Quantum State Preparation (VQA), a threshold comparator oracle, and Maximum-Likelihood Amplitude Estimation (MLAE) to enable quantum-accelerated CVaR estimation suitable for NISQ devices.

Results

Using only 6 qubits and K = 6 amplification levels, our approach achieves CVaR^0.95=0.281±0.012{\widehat {{\rm{CVaR}}}_{0.95}} = 0.281 \pm 0.012 (error < 1.1% compared to classical benchmarks), with tail probability p^=0.0478±0.0009\hat p = 0.0478 \pm 0.0009 and state fidelity 0.967 on the ibm_brisbane backend. MLAE reduces circuit depth by 3.3 × relative to canonical QAE, and a conceptual spintronic implementation achieves ∼2.0 fJ/gate, corresponding to an 80% energy reduction versus CMOS.

Contributions

This work introduces the first NISQ-compatible, energy-efficient quantum–spintronic pipeline for regulatory-compliant financial risk analytics, providing a quadratic sampling speedup, accurate tail-risk estimation, and a pathway toward sustainable quantum finance.
Open accessCC BY-NC-ND 4.0

Keywords

  • quantum finance
  • Conditional Value-at-Risk (CVaR)
  • Maximum-Likelihood Amplitude Estimation (MLAE)
  • spintronic quantum computing
  • NISQ algorithms

Suggested citation

Gayathri S. S., Muthulakshmi P., R. Palanivel (2026). Hybrid Circuit–Spintronic Quantum Framework for Financial Risk Analysis with QCVaR Estimation Using Variational Quantum Algorithms and Maximum-Likelihood Amplitude Estimation. Quantum Information & Computation, 26(1), 20-37. https://doi.org/10.2478/qic-2026-0002

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