Initial State Encoding via Reverse Quantum Annealing and H-Gain Features

Journal Article · Sun Jan 01 00:00:00 EST 2023 · IEEE Transactions on Quantum Engineering

DOI:https://doi.org/10.1109/TQE.2023.3319586· OSTI ID:2204894

Pelofske, Elijah  ^[1]; Hahn, Georg  ^[2]; Djidjev, Hristo  ^[1]

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1. CCS-3 Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA
2. Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA

Quantum annealing is a specialized type of quantum computation that aims to use quantum fluctuations in order to obtain global minimum solutions of combinatorial optimization problems. Programmable D-Wave quantum annealers are available as cloud computing resources, which allow users low-level access to quantum annealing control features. In this article, we are interested in improving the quality of the solutions returned by a quantum annealer by encoding an initial state into the annealing process. We explore two D-Wave features that allow one to encode such an initial state: the reverse annealing (RA) and the h-gain (HG) features. RA aims to refine a known solution following an anneal path starting with a classical state representing a good solution, going backward to a point where a transverse field is present, and then finishing the annealing process with a forward anneal. The HG feature allows one to put a time-dependent weighting scheme on linear ( $$h$$ ) biases of the Hamiltonian, and we demonstrate that this feature likewise can be used to bias the annealing to start from an initial state. We also consider a hybrid method consisting of a backward phase resembling RA and a forward phase using the HG initial state encoding. Importantly, we investigate the idea of iteratively applying RA and HG to a problem, with the goal of monotonically improving on an initial state that is not optimal. The HG encoding technique is evaluated on a variety of input problems including the edge-weighted maximum cut problem and the vertex-weighted maximum clique problem, demonstrating that the HG technique is a viable alternative to RA for some problems. We also investigate how the iterative procedures perform for both RA and HG initial state encodings on random whole-chip spin glasses with the native hardware connectivity of the D-Wave Chimera and Pegasus chips.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: 20220656ER; 89233218CNA000001
• OSTI ID: 2204894
• Alternate ID(s): OSTI ID: 2204897; OSTI ID: 2305314
• Report Number(s): LA-UR-23-22902; 10265106
• Journal Information: IEEE Transactions on Quantum Engineering, Journal Name: IEEE Transactions on Quantum Engineering Vol. 4; ISSN 2689-1808
• Publisher: Institute of Electrical and Electronics Engineers
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
quantum annealing
reverse quantum annealing
h-gain
Ising model
QEMC
iterated reverse annealing
anneal schedule
Bayesian optimization
D-Wave
maximum cut
QUBO