Speaker
Description
Quantum computing harnesses the principles of quantum mechanics to tackle computational tasks that are infeasible for classical machines. Qubits, capable of existing in superposition and forming entangled states, enable quantum algorithms like Shor’s factoring method to surpass traditional techniques. This technology holds promise across numerous domains, including cryptography, pharmaceutical research, and artificial intelligence, addressing complex challenges beyond classical capabilities. Despite its potential, quantum computing is still in its infancy. Current devices in the Noisy Intermediate-Scale Quantum (NISQ) era suffer uncertainty due to limited qubit counts and high susceptibility to environmental disturbances and hardware flaws. While techniques such as surface codes can reduce errors, they also increase circuit complexity and computational demands, introducing additional error sources. Various strategies have been developed to model uncertainty in quantum systems, including worst-case fidelity analysis. However, these methods often rely on probabilistic assumptions about noise and tend to overlook critical output-level information such as observables. As a novel approach, quantum uncertainty shall be considered from the perspective of imprecise probabilities, where qubits are represented as ranges of possible states rather than fixed values. This view aligns well with possibility theory, which uses possibility and necessity measures to represent uncertainty without requiring precise probability distributions. This makes possibility theory particularly suitable for capturing qubit disturbances caused by noise and imprecision. To explore this perspective, a possible modeling framework for quantum algorithms is proposed and benchmarked against established methods. The framework is evaluated on various quantum algorithms, demonstrating its capacity to manage quantum noise and uncertainty effectively. It enables a detailed analysis of conditions under which quantum algorithms lose informational reliability, offering a unique way to reason about qubit behavior in uncertain environments. Additionally, the proposed framework supports the comparison of quantum algorithms regarding their robustness against noise. By considering both theoretical modeling and algorithmic performance, this approach highlights the potential of possibility theory as a tool for simplifying quantum system analysis, improving noise management, and advancing quantum computing reliability.
