Speaker
Description
Amidst the quantum revolution, promises of groundbreaking advancements abound, yet the current reality falls short, as quantum computers grapple with the limitation of insufficient qubits. We are now in the NISQ era - Noisy Intermediate-Scale Quantum - marked by imperfect qubits and error-prone quantum gates. To harness utility from these machines, we embrace Hybrid Quantum-Classical Computing, a strategy mitigating noise through the collaboration with classical processors.
In this presentation, I unveil my work on a novel hybrid quantum-classical algorithm tailored for the "Maximum Cut" problem. Beyond its relevance in machine learning, statistical physics, circuit design, and data clustering, the problem's NP-complete classification underscores its significance in tackling broader NP problems, like the knapsack problem and integer factorization. The latter plays a crucial role in internet security/secrecy through public key cryptography schemes, such as RSA encryption, entirely reliant on our classical computers' inability to factor large numbers. Motivated by these implications, my research delves into merging two existing variational quantum algorithms into a singular, optimized interpolation. The aim is to extract, and combine, the strengths of both algorithms, offering a promising avenue for addressing NP-complete problems efficiently.
