Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage (Part 1)

Despite their success in "quantum supremacy" sampling tasks, the use of near-term quantum devices for solving high-value computational problems remains an open challenge. Proposals for achieving practical quantum advantage largely use parametrized quantum circuits (PQCs), but the existence of barren plateaus in the optimization landscape of these models makes training naively-initialized PQCs extremely difficult. We introduce a scalable means of leveraging classical resources to compute task-specific initializations for PQCs, which we show significantly boosts their performance on a variety of quantum simulation and machine learning problems, while demonstrably avoiding barren plateaus. Our method uses tensor network techniques to first identify a promising solution using available classical resources, before decomposing it into the parameters of a PQC. By utilizing a synergistic computing framework which blends quantum-inspired and fully-quantum models, our work opens new avenues for unlocking the full power of quantum devices for solving challenging real-world problems.

Part 1 introduces our synergistic optimization framework, while part 2 describes the high-performance decomposition procedure converting large matrix product states (MPS) into PQCs composed of two-qubit gates.