Affine filtering measurements enable code-aware decoding of linear codes over pure-state quantum channels by identifying affine subspaces and outperform symbol-wise USD and pretty good measurement in LDPC code simulations.
& Tillich, J.-P
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 3years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Extends NP-hardness of exceeding r/q + O(1/sqrt(D)) for bounded-degree max-Ek-LINSAT(q,r) over F_q and shows quantum decoding is required for DQI to achieve the hardness-optimal 1/sqrt(D) scaling.
A review describing the Decoded Quantum Interferometry algorithm for quantum speedups in max-LINSAT optimization, with claimed superpolynomial advantage in the OPI problem.
citing papers explorer
-
Affine Filtering Measurements and Their Applications to Quantum Decoding
Affine filtering measurements enable code-aware decoding of linear codes over pure-state quantum channels by identifying affine subspaces and outperform symbol-wise USD and pretty good measurement in LDPC code simulations.
-
Approximability limits for bounded-degree max-LINSAT and implications for decoded quantum interferometry
Extends NP-hardness of exceeding r/q + O(1/sqrt(D)) for bounded-degree max-Ek-LINSAT(q,r) over F_q and shows quantum decoding is required for DQI to achieve the hardness-optimal 1/sqrt(D) scaling.
-
Quantum Decoding Algorithms: Quantum Speedups in Optimization
A review describing the Decoded Quantum Interferometry algorithm for quantum speedups in max-LINSAT optimization, with claimed superpolynomial advantage in the OPI problem.