Despite the great promise of quantum machine learning models, there are several
challenges one must overcome before unlocking their full potential. For instance, models …

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 …

The quantum approximate optimization algorithm (QAOA) is one of the most prominent
proposed applications for near-term quantum computing. Here we study the ability of QAOA …

Abstract Recently, digitized-counterdiabatic (CD) quantum approximate optimization
algorithm (QAOA) has been proposed to make QAOA converge to the solution of an …

The Quantum Alternating Operator Ansatz (QAOA) is a hybrid classical-quantum algorithm
that aims to sample the optimal solution (s) of discrete combinatorial optimization problems …

The quantum approximate optimization algorithm (QAOA) is a promising method for solving
certain classical combinatorial optimization problems on near-term quantum devices. When …

Propositional satisfiability problem (SAT) is represented in a conjunctive normal form with
multiple clauses, which is an important non-deterministic polynomial-time (NP) complete …

The Boolean satisfiability problem (SAT), in particular 3SAT with its bounded clause size, is
a well-studied problem since a wide range of decision problems can be reduced to it. The …

It is the first step for understanding how RNA structure folds from base sequences that to
know how its secondary structure is formed. Traditional energy-based algorithms are short of …

Recently, Zhou et al. have proposed an Interpolation‐based (INTERP) strategy to generate
the initial parameters for Quantum Approximate Optimization Algorithm (QAOA). INTERP …