Experimental demonstration of perturbative anticrossing mitigation using non-uniform driver Hamiltonians

Abstract

Perturbative anticrossings have long been identified as a potential computational bottleneck for quantum annealing. This bottleneck can appear, for example, when a uniform transverse driver Hamiltonian is applied to each qubit. Previous theoretical research sought to alleviate such anticrossings by adjusting the transverse driver Hamiltonians on individual qubits according to a perturbative approximation. Here we apply this principle to a physical implementation of quantum annealing in a D-Wave 2000Q system. We use samples from the quantum annealing hardware and per-qubit anneal offsets to produce nonuniform driver Hamiltonians. On small instances with severe perturbative anticrossings, our algorithm yields an increase in minimum eigengaps, ground state success probabilities, and escape rates from metastable valleys. We also demonstrate that the same approach can mitigate biased sampling of degenerate ground states.

9 pages, 11 figures

References in corpus (19)

• A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
• Quantum annealing with more than one hundred qubits
• Quantum Computation by Adiabatic Evolution
• Experimental signature of programmable quantum annealing
• What is the Computational Value of Finite Range Tunneling?
• Entanglement in a quantum annealing processor
• Experimental Demonstration of a Robust and Scalable Flux Qubit
• Consistency Tests of Classical and Quantum Models for a Quantum Annealer
• First Order Quantum Phase Transition in Adiabatic Quantum Computation
• The performance of the quantum adiabatic algorithm on random instances of two optimization problems on regular hypergraphs
• A Compound Josephson Junction Coupler for Flux Qubits With Minimal Crosstalk
• Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the Ugly
• Probing Noise in Flux Qubits via Macroscopic Resonant Tunneling
• Exponentially-Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians
• Algorithmic approach to adiabatic quantum optimization
• Heavy tails in the distribution of time-to-solution for classical and quantum annealing
• Degeneracy, degree, and heavy tails in quantum annealing
• Advantages of Unfair Quantum Ground-State Sampling
• Simulations of the adiabatic quantum optimization for the Set Partition Problem

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