Analytical construction of $(n, n-1)$ quantum random access codes saturating the conjectured bound

TL;DR

This paper presents an analytical method to construct optimal $(n, n-1)$ quantum random access codes that achieve the conjectured success probability bound, with efficient implementation and insights into quantum-classical information limits.

Contribution

It introduces a systematic analytical construction for $(n, n-1)$-QRACs that saturate the conjectured success probability bound and provides a practical decoding circuit compatible with linear qubit connectivity.

Findings

Achieves the conjectured success probability bound for all n

Provides a linear-depth decoding circuit for implementation

Demonstrates an information-theoretic gap of O(log n) from the Holevo bound

Abstract

Quantum Random Access Codes (QRACs) embody the fundamental trade-off between the compressibility of information into limited quantum resources and the accessibility of that information, serving as a cornerstone of quantum communication and computation. In particular, the $(n, n-1)$-QRACs, which encode $n$ bits of classical information into $n-1$ qubits, provides an ideal theoretical model for verifying quantum advantage in high-dimensional spaces; however, the analytical derivation of optimal codes for general $n$ has remained an open problem. In this paper, we establish an analytical construction method for $(n, n-1)$-QRACs by using an explicit operator formalism. We prove that this construction strictly achieves the numerically conjectured upper bound of the average success probability, $\mathcal{P} = 1/2 + \sqrt{(n-1)/n}/2$, for all $n$. Furthermore, we present a systematic algorithm to decompose the derived optimal POVM into standard quantum gates. Since the resulting decoding circuit consists solely of interactions between adjacent qubits, it can be implemented with a circuit depth of $O(n)$ even under linear connectivity constraints. Additionally, we analyze the high-dimensional limit and demonstrate that while the non-commutativity of measurements is suppressed, an information-theoretic gap of $O(\log n)$ from the Holevo bound inevitably arises for symmetric encoding. This study not only provides a scalable implementation method for high-dimensional quantum information processing but also offers new insights into the mathematical structure at the quantum-classical boundary.