Random linear codes over finite fields satisfy near-optimal discrepancy properties, enabling list-decoding and zero-error list-recovery above capacity that match random codes.
Arcs in finite projective spaces.EMS Surveys in Mathematical Sciences, 6(1):133–172
4 Pith papers cite this work. Polarity classification is still indexing.
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Existence of asymptotically better solutions than the semicircle law for worst-case OPI over prime fields when n/m exceeds thresholds like 0.6225 for rho approximately 1/2, via connection to local leakage resilience of secret sharing.
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.
Decoded quantum interferometry is generalized to translation association schemes, reducing analysis to tridiagonal eigenvalue problems, with a finite-field matrix rank-difference protocol that produces constant-probability residual-rank bounds but no additive optimality guarantee.
citing papers explorer
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Discrepancy for Random Linear Codes
Random linear codes over finite fields satisfy near-optimal discrepancy properties, enabling list-decoding and zero-error list-recovery above capacity that match random codes.
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On Worst-Case Optimal Polynomial Intersection
Existence of asymptotically better solutions than the semicircle law for worst-case OPI over prime fields when n/m exceeds thresholds like 0.6225 for rho approximately 1/2, via connection to local leakage resilience of secret sharing.
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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.
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Decoded Quantum Interferometry Beyond Hamming: Rank-Metric and Translation Association Schemes
Decoded quantum interferometry is generalized to translation association schemes, reducing analysis to tridiagonal eigenvalue problems, with a finite-field matrix rank-difference protocol that produces constant-probability residual-rank bounds but no additive optimality guarantee.