Exploring the utilization of tensor networks for quantum circuit simulation and its use case application - Veeraraghavan Srivathsan Nithyasri (Nanyang Technological University, 2025)
Quantum Computing is an upcoming paradigm of computing that harnesses the laws of quantum mechanics like superposition and entanglement to offer computational speedup for certain challenging problems that current supercomputers cannot solve efficiently and effectively. Challenges to achieving fault tolerant quantum computers that makes this advantage practical have pushed scientists to explore the possibility of simulating quantum computation on classical computers to determine how quantum algorithms would behave on real quantum hardware devices. In this project, the goal is exploration into how to simulate quantum computation using a special technique of tensor networks which allows scalability in problem solving. Aiming at bridging the gap between theory and application, the Variational Quantum Eigensolver algorithm is implemented in Qibotn, a module for tensor network quantum simulation developed by Institute of High Performance Computing, A*STAR. Further, a real-world, small-scale application in logistics, namely the Vehicle Routing Problem is solved using quantum simulation by encoding the problem statement into a Quadratic Unconstrained Binary OptimizationwhichismodeledintoaquantumHamiltonian,whosegroundstateenergy is calculated for by the algorithm, solves the given problem statement. Thus, in this project we have implemented the VQE algorithm as a feature in Qibotn for Qibo users and have built a working application based on tensor network quantum simulations to demonstrate the usefulness of the algorithm built, lending it academic and practical relevance in solving real-world problem using an emerging technology, which can be scaled up in the future to solve for more complex scenarios using the computational advantage offered by tensor networks in overcoming the challenge of simulating large quantum circuits on classical machines.