The Quantum Zeno Monte Carlo algorithm bridges the gap between noisy intermediate-scale quantum and fault-tolerant quantum computing eras by offering polynomial computational complexity and resilience to both device noise and Trotter errors without requiring initial state overlap or variational parameters, as demonstrated on IBM’s NISQ devices with up to 12 qubits.
The research introduces lossy quantum dimension reduction for stochastic process sampling that compresses memory requirements beyond current quantum and classical approaches while maintaining high-fidelity approximations for both Markovian and non-Markovian processes, with applications across numerous scientific fields.
Quantum computing offers significant advantages for analyzing discrete stochastic processes by calculating characteristic functions through quantum superposition (“quantum brute force”), reducing variance without importance sampling, providing quadratic speedup through amplitude estimation, and demonstrating practical applications in finance and random walks.
D-Wave Systems has published a milestone study in collaboration with scientists at Google, demonstrating a computational performance advantage, increasing with both simulation size and problem hardness, to over 3 million times that of corresponding classical methods.
A team of researchers at University of the Basque Country, Santander Bank and IQM, has presented a digital quantum algorithm to solve Black-Scholes equation on a quantum computer for a wide range of relevant financial parameters by mapping it to the Schrödinger equation
Cambridge Quantum Computing announced a collaboration with CERN Openlab, named the QUATERNION project, in quantum computing.
Quantum Chemistry Breakthrough: DeepMind Uses Neural Networks to Tackle Schrödinger Equation.
Researchers from the Université Paris Diderot develop the first quantum algorithm for the constrained portfolio optimization problem.