August 30, 2022

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

  • Jacob Miller
  • Jing Chen
  • Alejandro Perdomo-Ortiz

Atithi Acharya, Manuel Rudolph


While recent breakthroughs have proven the ability of noisy intermediate-scale quantum (NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the use of these devices for solving more practically relevant computational problems remains a challenge. Proposals for attaining practical quantum advantage typically involve parametrized quantum circuits (PQCs), whose parameters can be optimized to find solutions to diverse problems throughout quantum simulation and machine learning. However, training PQCs for real-world problems remains a significant practical challenge, largely due to the phenomenon of barren plateaus in the optimization landscapes of randomly-initialized quantum circuits. In this work, we introduce a scalable procedure for harnessing classical computing resources to determine task-specific initializations of PQCs, which we show significantly improves the trainability and performance of PQCs on a variety of problems. Given a specific optimization task, this method first utilizes tensor network (TN) simulations to identify a promising quantum state, which is then converted into gate parameters of a PQC by means of a high performance decomposition procedure. We show that this task-specific initialization avoids barren plateaus, and effectively translates increases in classical resources to enhanced performance and speed in training quantum circuits. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing, and opens up new avenues to harness the power of modern quantum hardware for realizing practical quantum advantage.

Jacob Miller
Zapata Author

Jacob Miller , Ph.D.

Quantum Research Scientist
Jing Chen
Zapata Author

Jing Chen , Ph.D.

Senior Quantum Scientist, Research
Alejandro Perdomo-Ortiz
Zapata Author

Alejandro Perdomo-Ortiz , Ph.D.

Research Director, Quantum AI/ML