The Capacity of Classical Summation Over a Quantum MAC With Arbitrarily Distributed Inputs and Entanglements

The $\Sigma $ -QMAC problem is introduced, involving S servers, K classical ( $\mathbb {F}_{d}$ ) data streams, and T independent quantum systems. Data stream ${\mathsf { W}}_{k}, k\in [K]$ is replicated at a subset of servers $\mathcal {W}(k)\subset [S]$ , and quantum system ${\mathcal {Q}}_{t}, t\in [T]$ is distributed among a subset of servers ${\mathcal {E}}(t)\subset [S]$ such that Server $s\in {\mathcal {E}}(t)$ receives subsystem ${\mathcal {Q}}_{t,s}$ of ${\mathcal {Q}}_{t}=({\mathcal {Q}}_{t,s})_{s\in {\mathcal {E}}(t)}$ . Servers manipulate their quantum subsystems according to their data and send the subsystems to a receiver. The total download cost is $\sum _{t\in [T]}\sum _{s\in {\mathcal {E}}(t)}\log _{d}|{\mathcal {Q}}_{t,s}|$ qudits, where $|{\mathcal {Q}}|$ is the dimension of ${\mathcal {Q}}$ . The states and measurements of $({\mathcal {Q}}_{t})_{t\in [T]}$ are required to be separable across $t\in [T]$ throughout, but for each $t\in [T]$ , the subsystems of ${\mathcal {Q}}_{t}$ can be prepared initially in an arbitrary (independent of data) entangled state, manipulated arbitrarily by the respective servers, and measured jointly by the receiver. From the measurements, the receiver must recover the sum of all data streams. Rate is defined as the number of dits ( $\mathbb {F}_{d}$ symbols) of the desired sum computed per qudit of download. The capacity of $\Sigma $ -QMAC, i.e., the supremum of achievable rates is characterized for arbitrary data and entanglement distributions ${\mathcal {W}}, {\mathcal {E}}$ . For example, in the symmetric setting with $K={\binom {S}{\alpha }}$ data-streams, each replicated among a distinct $\alpha $ -subset of $[S]$ , and $T={\binom {S}{\beta }}$ quantum systems, each distributed among a distinct $\beta $ -subset of $[S]$ , the capacity of the $\Sigma $ -QMAC is $\frac {1}{\beta T}\sum _{\gamma = (\alpha +\beta -S)^{+}}^{\min (\alpha,\beta)} \min (\beta,2\gamma) \cdot \binom {\alpha }{\gamma }\cdot \binom {S-\alpha }{\beta -\gamma }$ . Coding based on the N-sum box abstraction is optimal in every case. Notably, for every $S\neq 3$ there exists an instance of the $\Sigma $ -QMAC where S-party entanglement is necessary to achieve the fully entangled capacity.