Analytical construction of $(n, n-1)$ quantum random access codes saturating the conjectured bound
Takayuki Suzuki
TL;DR
The paper resolves the longstanding open problem of analytically constructing optimal $(n,n-1)$-QRACs by introducing an explicit operator formalism based on the matrix $A_n$, proves that the construction saturates the conjectured bound $oxed{ frac{1}{2}igl(1+ frac{1}{2} oot2 rac{n-1}{n}igr)}$ for all $n$, and provides a systematic POVM-based decoding with an $O(n)$-depth circuit under linear connectivity. It also presents a Pauli-string decomposition of the optimal observables and a unitary circuit exploiting left and right contractions to realize the decoding, making the approach scalable to high dimensions. The asymptotic analysis reveals that non-commutativity between measurements is suppressed as $O(1/n)$, while a fundamental information-theoretic gap from the Holevo bound grows as $O( olinebreak ext{log} olinebreak n)$ for symmetric encoding, highlighting intrinsic quantum-classical trade-offs. Collectively, the results furnish a practical, hardware-friendly blueprint for high-dimensional quantum information processing and deepen understanding of quantum advantages near the quantum-classical boundary.
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.