Standard quantum annealing outperforms adiabatic reverse annealing with decoherence
arXiv:2201.11997 · doi:10.1103/PhysRevA.105.032431
Abstract
We study adiabatic reverse annealing (ARA) in an open system. In the closed system (unitary) setting, this annealing protocol allows avoidance of first-order quantum phase transitions of selected models, resulting in an exponential speedup compared with standard quantum annealing, provided that the initial state of the algorithm is close in Hamming distance to the target one. Here, we show that decoherence can significantly modify this conclusion: by resorting to the adiabatic master equation approach, we simulate the dynamics of the ferromagnetic -spin model with under independent and collective dephasing. For both models of decoherence, we show that the performance of open system ARA is far less sensitive to the choice of the initial state than its unitary counterpart, and, most significantly, that open system ARA by and large loses its time to solution advantage compared to standard quantum annealing. These results suggest that as a stand-alone strategy, ARA is unlikely to experimentally outperform standard "forward" quantum annealing, and that error mitigation strategies will likely be required in order to realize the benefits of ARA in realistic, noisy settings.
12 pages, 11 figures
References in corpus (14)
- Bounds for the adiabatic approximation with applications to quantum computation
- Adiabatic approximation in open quantum systems
- Decoherence in adiabatic quantum computation
- Energy gaps in quantum first-order mean-field-like transitions: The problems that quantum annealing cannot solve
- Exponential Enhancement of the Efficiency of Quantum Annealing by Non-Stochastic Hamiltonians
- Exponential Speedup of Quantum Annealing by Inhomogeneous Driving of the Transverse Field
- Accuracy vs run time in adiabatic quantum search
- General Adiabatic Evolution with a Gap Condition
- Reverse quantum annealing of the -spin model with relaxation
- Improving quantum annealing of the ferromagnetic -spin model through pausing
- Adiabatic Markovian Dynamics
- Why and when is pausing beneficial in quantum annealing?
- Optimally Stopped Optimization
- Achieving fair sampling in quantum annealing