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 $p$-spin model with $p=3$ 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 ini...
We introduce and review briefly the phenomenon of quantum annealing and analog computation. The role...
We introduce and study the adiabatic dynamics of free-fermion models subject to a local Lindblad bat...
Quantum annealing (QA) is a promising method for solving combinatorial optimization problems whose s...
In this review, after providing the basic physical concept behind quantum annealing (or adiabatic qu...
We study quantum annealing for combinatorial optimization with Hamiltonian $H = z H_f + H_0$ where $...
The evaluation of the performance of adiabatic annealers is hindered by lack of efficient algorithms...
Reverse annealing is a variant of quantum annealing, in which the system is prepared in a classical ...
Adiabatic quantum computation and quantum annealing are powerful methods designed to solve optimizat...
Recent experiments with increasingly larger numbers of qubits have sparked renewed interest in adiab...
Quantum annealing aims at finding optimal solutions to complex optimization problems using a suitabl...
Quantum Error Mitigation (QEM) presents a promising near-term approach to reduce error when estimati...
We perform an in-depth comparison of quantum annealing with several classical optimisation technique...
We introduce and study the adiabatic dynamics of free-fermion models subject to a local Lindblad bat...
Quantum annealing (QA) is a promising approach for not only solving combinatorial optimization probl...
In adiabatic quantum annealing the required run-time to reach a given ground-state fidelity is dicta...
We introduce and review briefly the phenomenon of quantum annealing and analog computation. The role...
We introduce and study the adiabatic dynamics of free-fermion models subject to a local Lindblad bat...
Quantum annealing (QA) is a promising method for solving combinatorial optimization problems whose s...
In this review, after providing the basic physical concept behind quantum annealing (or adiabatic qu...
We study quantum annealing for combinatorial optimization with Hamiltonian $H = z H_f + H_0$ where $...
The evaluation of the performance of adiabatic annealers is hindered by lack of efficient algorithms...
Reverse annealing is a variant of quantum annealing, in which the system is prepared in a classical ...
Adiabatic quantum computation and quantum annealing are powerful methods designed to solve optimizat...
Recent experiments with increasingly larger numbers of qubits have sparked renewed interest in adiab...
Quantum annealing aims at finding optimal solutions to complex optimization problems using a suitabl...
Quantum Error Mitigation (QEM) presents a promising near-term approach to reduce error when estimati...
We perform an in-depth comparison of quantum annealing with several classical optimisation technique...
We introduce and study the adiabatic dynamics of free-fermion models subject to a local Lindblad bat...
Quantum annealing (QA) is a promising approach for not only solving combinatorial optimization probl...
In adiabatic quantum annealing the required run-time to reach a given ground-state fidelity is dicta...
We introduce and review briefly the phenomenon of quantum annealing and analog computation. The role...
We introduce and study the adiabatic dynamics of free-fermion models subject to a local Lindblad bat...
Quantum annealing (QA) is a promising method for solving combinatorial optimization problems whose s...