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 …
Noisy intermediate-scale quantum algorithms
A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …
integer factorization and unstructured database search requires millions of qubits with low …
From pulses to circuits and back again: A quantum optimal control perspective on variational quantum algorithms
The last decade has witnessed remarkable progress in the development of quantum
technologies. Although fault-tolerant devices likely remain years away, the noisy …
technologies. Although fault-tolerant devices likely remain years away, the noisy …
Quantum architecture search via deep reinforcement learning
Recent advances in quantum computing have drawn considerable attention to building
realistic application for and using quantum computers. However, designing a suitable …
realistic application for and using quantum computers. However, designing a suitable …
Reinforcement learning for many-body ground-state preparation inspired by counterdiabatic driving
The quantum alternating operator ansatz (QAOA) is a prominent example of variational
quantum algorithms. We propose a generalized QAOA called CD-QAOA, which is inspired …
quantum algorithms. We propose a generalized QAOA called CD-QAOA, which is inspired …
Hybrid quantum-classical algorithms for approximate graph coloring
We show how to apply the recursive quantum approximate optimization algorithm (RQAOA)
to MAX-$ k $-CUT, the problem of finding an approximate $ k $-vertex coloring of a graph …
to MAX-$ k $-CUT, the problem of finding an approximate $ k $-vertex coloring of a graph …
Using models to improve optimizers for variational quantum algorithms
Variational quantum algorithms are a leading candidate for early applications on noisy
intermediate-scale quantum computers. These algorithms depend on a classical …
intermediate-scale quantum computers. These algorithms depend on a classical …
Quantum optimization for the graph coloring problem with space-efficient embedding
Current quantum computing devices have different strengths and weaknesses depending
on their architectures. This means that flexible approaches to circuit design are necessary …
on their architectures. This means that flexible approaches to circuit design are necessary …
Self-correcting quantum many-body control using reinforcement learning with tensor networks
Quantum many-body control is a central milestone en route to harnessing quantum
technologies. However, the exponential growth of the Hilbert space dimension with the …
technologies. However, the exponential growth of the Hilbert space dimension with the …
A reinforcement learning approach to rare trajectory sampling
Very often when studying non-equilibrium systems one is interested in analysing dynamical
behaviour that occurs with very low probability, so called rare events. In practice, since rare …
behaviour that occurs with very low probability, so called rare events. In practice, since rare …