Quantum computing (QC) stands at the forefront of a technological revolution poised to disrupt the global banking industry. By harnessing superposition, entanglement, and quantum interference, QC offers exponential or quadratic speedups for complex financial computations that challenge classical systems. This study provides a comprehensive empirical analysis of QC’s dual impact—transformative opportunities in portfolio optimization, risk modeling, derivative pricing, fraud detection, and Monte Carlo simulations, alongside substantial risks including cryptographic vulnerabilities (e.g., Shor’s algorithm threatening RSA/ECC), high capital costs, talent shortages, and NISQ-era noise limitations.

Adopting a mixed-methods approach, the research integrates an extensive literature review, a survey of 120 banking and fintech professionals, expert insights, and hybrid quantum-classical simulations using frameworks like Qiskit and PennyLane on benchmark datasets (50–10,000 assets/scenarios). Findings indicate potential speedups of 20–30x in optimization and risk calculations, with 15–40% improvements in metrics such as Sharpe ratios and fraud detection accuracy. McKinsey projects $400–600 billion in annual value for finance by 2035, yet “harvest now, decrypt later” (HNDL) attacks pose systemic threats if post-quantum cryptography (PQC) migration lags.

The study highlights hybrid approaches as the pragmatic near-term path and proposes a quantum readiness framework tailored for banks, with emphasis on emerging markets. Strategic recommendations address governance, investment prioritization, talent development, and regulatory collaboration. This research contributes actionable insights for bank executives, policymakers, and researchers navigating the quantum transition.

Keywords: Quantum computing, banking disruption, portfolio optimization, risk management, post-quantum cryptography, hybrid quantum-classical systems, financial innovation, quantum readiness, empirical simulation