REVIEW 2 cited by
Multiscale Quantum Approximate Optimization Algorithm
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The quantum approximate optimization algorithm (QAOA) is one of the canonical algorithms designed to find approximate solutions to combinatorial optimization problems in current noisy intermediate-scale quantum (NISQ) devices. It is an active area of research to exhibit its speedup over classical algorithms. The performance of the QAOA at low depths is limited, while the QAOA at higher depths is constrained by the current techniques. We propose a new version of QAOA that incorporates the capabilities of QAOA and the real-space renormalization group transformation, resulting in enhanced performance. Numerical simulations demonstrate that our algorithm can provide accurate solutions for certain randomly generated instances utilizing QAOA at low depths, even at the lowest depth. The algorithm is suitable for NISQ devices to exhibit a quantum advantage.
Forward citations
Cited by 2 Pith papers
-
Reducing QAOA Circuit Depth by Factoring out Semi-Symmetries
A QUBO preprocessing algorithm factors out partial coupling symmetries into ancilla qubits, reducing QAOA CNOT count and circuit depth while preserving the ground state energy.
-
Reducing QUBO Density by Factoring Out Semi-Symmetries
Semi-symmetries in QUBO matrices can be factored into ancilla qubits, reducing couplings and QAOA depth by up to 45% while preserving the ground state if the anchoring parameter is large enough.
Discussion (0). Continue with ORCID to comment.