Florence Paquette and colleagues at the University of Sherbrooke have created a quantum algorithm for pricing discretely monitored lookback options within the Black-Scholes framework. The algorithm reformulates the pricing problem as a quantum evolution process and uses the Variational Quantum Imaginary Time Evolution method to address challenges arising from jump conditions in these path-dependent options. The method offers a key step towards using quantum computers to price complex financial instruments with non-smooth dynamics, potentially providing advantages over classical Monte Carlo simulations. Quantum algorithms reduce qubit needs for complex option pricing A sequential quantum formulation, utilising dedicated jump Hamiltonians, reduced qubit requirements by 33% compared to classical Monte Carlo simulations for equivalent accuracy in pricing discretely monitored lookback options. This reduction in qubit count is particularly noteworthy given the limitations of current quantum hardware, where the number of available qubits is a critical constraint. The Black-Scholes model, a cornerstone of modern financial mathematics, typically relies on assumptions of continuous price movements. However, real-world markets often exhibit discrete jumps, sudden, significant price changes caused by events like earnings announcements or geopolitical shocks. Discretely monitored lookback options are path-dependent, meaning their payoff is determined not just by the final asset price, but by the entire trajectory of the underlying asset over a specified period, making them significantly more complex to price than standard European options. The 33% threshold is significant because it suggests the potential for solving complex financial problems on near-term, limited-qubit quantum hardware, something previously unattainable. The application of the Variational Quantum Imaginary Time Evolution method, or VarQITE, successfully prices options with ‘jump’ conditions, sudden changes in value, which are absent in standard option types and pose a strong challenge for traditional modelling. These jumps introduce discontinuities into the pricing partial differential equation (PDE), making it difficult to solve using conventional