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A review on quantum approximate optimization algorithm and its variants
Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization problems that …
variational quantum algorithm that aims to solve combinatorial optimization problems that …
Challenges and opportunities in quantum optimization
Quantum computers have demonstrable ability to solve problems at a scale beyond brute-
force classical simulation. Interest in quantum algorithms has developed in many areas …
force classical simulation. Interest in quantum algorithms has developed in many areas …
[HTML][HTML] Short-depth QAOA circuits and quantum annealing on higher-order ising models
We present a direct comparison between QAOA (Quantum Alternating Operator Ansatz), and
QA (Quantum Annealing) on 127 qubit problem instances. QAOA with p= 1, 2 rounds is …
QA (Quantum Annealing) on 127 qubit problem instances. QAOA with p= 1, 2 rounds is …
Building spatial symmetries into parameterized quantum circuits for faster training
Practical success of quantum learning models hinges on having a suitable structure for the
parameterized quantum circuit. Such structure is defined both by the types of gates …
parameterized quantum circuit. Such structure is defined both by the types of gates …
Alignment between initial state and mixer improves QAOA performance for constrained optimization
Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic
algorithm, which it can approximate with sufficient depth. However, it is unclear to what …
algorithm, which it can approximate with sufficient depth. However, it is unclear to what …