Quantum annealing sampling with a bias field
arXiv:2205.15820 · doi:10.1103/PhysRevApplied.18.044036
Abstract
The presence of a bias field, encoding some information about the target state, can enhance the performance of quantum optimization methods. Here we investigate the effect of such a bias field on the outcome of quantum annealing sampling, at the example of the exact cover problem. The sampling is carried out on a D-Wave machine, and different bias configurations are benchmarked against the unbiased sampling procedure. It is found that the biased annealing algorithm works particularly well for larger problem sizes, where the Hamming distance between bias and target configuration becomes less important. This work motivates future research efforts for finding good bias configurations, either on the quantum machine itself, or in a hybrid fashion via classical algorithms.
References in corpus (23)
- Experimental implementation of an adiabatic quantum optimization algorithm
- Adiabatic tracking of quantum many-body dynamics
- Quantum annealing with antiferromagnetic fluctuations
- Simple Glass Models and their Quantum Annealing
- Exponential Speedup of Quantum Annealing by Inhomogeneous Driving of the Transverse Field
- Reverse quantum annealing of the -spin model with relaxation
- Experimental demonstration of perturbative anticrossing mitigation using non-uniform driver Hamiltonians
- Two-parameter counter-diabatic driving in quantum annealing
- Probing Entanglement in Adiabatic Quantum Optimization with Trapped Ions
- Genetic optimization of quantum annealing
- Quantum Approximate Optimization Algorithm with Adaptive Bias Fields
- Variational optimization of the quantum annealing schedule for the Lechner-Hauke-Zoller scheme
- Optimal quantum annealing: A variational shortcut to adiabaticity approach
- General bound on the performance of counter-diabatic driving acting on dissipative spin systems
- Generalized transitionless quantum driving for open quantum systems
- Search range in experimental quantum annealing
- Standard quantum annealing outperforms adiabatic reverse annealing with decoherence
- Polynomial scaling enhancement in ground-state preparation of Ising spin models via counter-diabatic driving
- Optimization by a quantum reinforcement algorithm
- Theoretical survey of unconventional quantum annealing methods applied to adifficult trial problem
- The quantum annealing gap and quench dynamics in the exact cover problem
- Hard instance learning for quantum adiabatic prime factorization
- Quantum walk in a reinforced free-energy landscape: Quantum annealing with reinforcement