Multivariate DQI uses N-variable polynomials for weighted Max-LINSAT, derives closed-form asymptotics for expectation and concentration, provides a single-decoder preparation circuit, and shows outperformance over weighted Prange for some OPI cases while extending to Hamiltonian DQI.
Decoded quantum interferometry requires structure
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
fields
quant-ph 5verdicts
UNVERDICTED 5roles
background 2representative citing papers
A quantum decoder for LDPC codes with coherent errors outperforms belief propagation on average-case D-regular max-k-XORSAT for several k and D, matching an enhanced version of Prange's algorithm.
DQI-Kit automates encoding of objectives and constraints into Max-LINSAT instances and estimates expected DQI performance on the resulting problems.
A Master Theorem gives a strictly tighter lower bound on quantum advantage in DQI by replacing the worst-case error penalty with an eigenvector-weighted Rayleigh quotient penalty.
The paper identifies four key hurdles in the transition from NISQ to FASQ quantum computers and argues that targeting them will accelerate progress toward useful quantum advantage.
citing papers explorer
-
Multivariate Decoded Quantum Interferometry for Weighted Optimization
Multivariate DQI uses N-variable polynomials for weighted Max-LINSAT, derives closed-form asymptotics for expectation and concentration, provides a single-decoder preparation circuit, and shows outperformance over weighted Prange for some OPI cases while extending to Hamiltonian DQI.
-
Optimization Using Locally-Quantum Decoders
A quantum decoder for LDPC codes with coherent errors outperforms belief propagation on average-case D-regular max-k-XORSAT for several k and D, matching an enhanced version of Prange's algorithm.
-
From Constraint to Code: DQI-Kit -- A Software Framework for Decoded Quantum Interferometry
DQI-Kit automates encoding of objectives and constraints into Max-LINSAT instances and estimates expected DQI performance on the resulting problems.
-
Hidden Quantum Advantage near the Decoding Threshold of Decoded Quantum Interferometry
A Master Theorem gives a strictly tighter lower bound on quantum advantage in DQI by replacing the worst-case error penalty with an eigenvector-weighted Rayleigh quotient penalty.
-
Mind the gaps: The fraught road to quantum advantage
The paper identifies four key hurdles in the transition from NISQ to FASQ quantum computers and argues that targeting them will accelerate progress toward useful quantum advantage.