Journal
QUANTUM SCIENCE AND TECHNOLOGY
Volume 6, Issue 3, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/2058-9565/abdca4
Keywords
quantum computing; variational quantum eigensolver; quantum computational chemistry
Funding
- Canada 150 Research Chairs Program
- Google, Inc.
- U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Quantum Algorithms Teams Program
- U.S. Department of Energy [DE-SC0019374]
- U.S. Department of Energy (DOE) [DE-SC0019374] Funding Source: U.S. Department of Energy (DOE)
Ask authors/readers for more resources
This work presents a method for constructing reduced-size entangler pools leveraging classical algorithms, which ranks and screens entanglers based on mutual information between qubits in classically approximated ground state. Numerical experiments demonstrate that a reduced entangler pool can achieve the same numerical accuracy as the original pool, paving a new way for adaptive construction of ansatz circuits in variational quantum algorithms.
Adaptive construction of ansatz circuits offers a promising route towards applicable variational quantum eigensolvers on near-term quantum hardware. Those algorithms aim to build up optimal circuits for a certain problem and ansatz circuits are adaptively constructed by selecting and adding entanglers from a predefined pool. In this work, we propose a way to construct entangler pools with reduced size by leveraging classical algorithms. Our method uses mutual information between the qubits in classically approximated ground state to rank and screen the entanglers. The density matrix renormalization group method is employed for classical precomputation in this work. We corroborate our method numerically on small molecules. Our numerical experiments show that a reduced entangler pool with a small portion of the original entangler pool can achieve same numerical accuracy. We believe that our method paves a new way for adaptive construction of ansatz circuits for variational quantum algorithms.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available