Mapping State Transition Susceptibility in Quantum Annealing
Elijah Pelofske

TL;DR
This paper introduces a method to map the susceptibility of state transitions in quantum annealing, using reverse annealing and h-gain schedules on D-Wave devices to analyze how initial states evolve into solutions.
Contribution
The authors develop a novel approach to quantify state transition susceptibility in quantum annealing, providing detailed insights into reverse annealing dynamics on D-Wave hardware.
Findings
Mapping reveals detailed transition paths during reverse annealing
Susceptibility varies across different initial states
Experimental validation on three small Ising problems
Abstract
Quantum annealing is a novel type of analog computation that aims to use quantum mechanical fluctuations to search for optimal solutions of Ising problems. Quantum annealing in the transverse field Ising model, implemented on D-Wave devices, works by applying a time dependent transverse field, which puts all qubits into a uniform state of superposition, and then applying a Hamiltonian over time which describes a user programmed Ising problem. We present a method which utilizes two control features of D-Wave quantum annealers, reverse annealing and an h-gain schedule, to quantify the susceptibility, or the distance, between two classical states of an Ising problem. The starting state is encoded using reverse annealing, and the second state is encoded on the linear terms of problem Hamiltonian. An h-gain schedule is specified which incrementally increases the strength of the linear terms,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Advanced Memory and Neural Computing · Quantum and electron transport phenomena
