Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm
for solving optimization problems on quantum computers. However, the potential of QAOA to …
for solving optimization problems on quantum computers. However, the potential of QAOA to …
A review of barren plateaus in variational quantum computing
Variational quantum computing offers a flexible computational paradigm with applications in
diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) …
diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) …
[HTML][HTML] Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer
Realizing the potential of near-term quantum computers to solve industry-relevant
constrained-optimization problems is a promising path to quantum advantage. In this work …
constrained-optimization problems is a promising path to quantum advantage. In this work …
Large-scale quantum approximate optimization on nonplanar graphs with machine learning noise mitigation
Quantum computers are increasing in size and quality but are still very noisy. Error
mitigation extends the size of the quantum circuits that noisy devices can meaningfully …
mitigation extends the size of the quantum circuits that noisy devices can meaningfully …
A perspective on protein structure prediction using quantum computers
Despite the recent advancements by deep learning methods such as AlphaFold2, in silico
protein structure prediction remains a challenging problem in biomedical research. With the …
protein structure prediction remains a challenging problem in biomedical research. With the …
Equivariant quantum circuits for learning on weighted graphs
Variational quantum algorithms are the leading candidate for advantage on near-term
quantum hardware. When training a parametrized quantum circuit in this setting to solve a …
quantum hardware. When training a parametrized quantum circuit in this setting to solve a …
[HTML][HTML] Scaling whole-chip QAOA for higher-order Ising spin glass models on heavy-hex graphs
We show that the quantum approximate optimization algorithm (QAOA) for higher-order,
random coefficient, heavy-hex compatible spin glass Ising models has strong parameter …
random coefficient, heavy-hex compatible spin glass Ising models has strong parameter …
Parameter setting in quantum approximate optimization of weighted problems
Abstract Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate
algorithm for solving combinatorial optimization problems on quantum computers. However …
algorithm for solving combinatorial optimization problems on quantum computers. However …
Constrained optimization via quantum zeno dynamics
Constrained optimization problems are ubiquitous in science and industry. Quantum
algorithms have shown promise in solving optimization problems, yet none of the current …
algorithms have shown promise in solving optimization problems, yet none of the current …
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 …