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arxiv: 2604.08214 · v1 · submitted 2026-04-09 · 💻 cs.IT · math.IT

Quantum Integrated Communication and Computing Over Multiple-Access Bosonic Channel

Ioannis Krikidis This is my paper

Pith reviewed 2026-05-10 17:27 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords quantum integrated communication and computationbosonic multiple-access channelover-the-air computationcoherent-state signallingalternating optimizationsum-rate constraintquantum MAC
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The pith

A quantum multiple-access bosonic channel lets one receiver compute over-the-air from some devices while decoding data from others.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates integrating computation and communication in a quantum setting over a single-mode bosonic multiple-access channel that uses coherent-state inputs. It shows that the channel's natural superposition property lets a common receiver run analogue computation on signals from one group of devices and decode digital messages from another group at the same time. The design jointly tunes transmit powers and a receive coefficient to maximize computation accuracy while respecting a prescribed sum-rate for the communication traffic. An alternating-optimization procedure solves the resulting non-convex problem with closed-form updates and projected gradients, producing fast convergence and low complexity.

Core claim

The author establishes that the superposition property of the quantum MAC with coherent-state signalling permits a common receiver to simultaneously execute over-the-air computation on analogue symbols from one set of devices and decode multiple-access data from another set. The joint design of transmit power control and receive coefficient is formulated to maximize computation accuracy under a prescribed sum-rate communication constraint and is solved via a low-complexity alternating-optimization framework that incorporates closed-form linear minimum-mean square error updates for the receive coefficient, monotonicity properties of the quantum sum-rate constraint, and projected-gradient step

What carries the argument

The superposition property of the quantum multiple-access channel, which separates computation accuracy from the sum-rate communication constraint, together with the alternating-optimization framework that alternates closed-form MMSE receive updates and projected-gradient power refinements.

If this is right

  • Quantum networks can perform useful computation inside the channel without allocating separate resources for sensing and data transfer.
  • The low-complexity alternating solver makes the scheme practical for systems with many devices and modest classical processing power.
  • The trade-off surface between computation accuracy and sum-rate can be traced quickly, allowing real-time adaptation to changing channel conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same superposition idea might extend to multi-mode or continuous-variable quantum channels, potentially improving the accuracy-rate frontier further.
  • In a quantum internet setting the scheme could reduce end-to-end latency by computing partial results before full decoding occurs at the receiver.
  • Classical over-the-air computation schemes could adopt similar alternating power-control methods, though quantum noise statistics would change the resulting accuracy bounds.

Load-bearing premise

The alternating optimization reliably finds good solutions to the non-convex joint power-and-coefficient problem without poor local optima, and the quantum MAC superposition cleanly decouples computation accuracy from the sum-rate constraint.

What would settle it

A numerical test or physical realization in which the alternating procedure converges to a point whose computation accuracy is substantially lower than the true global optimum, or in which raising the sum-rate requirement forces a measurable increase in computation error.

Figures

Figures reproduced from arXiv: 2604.08214 by Ioannis Krikidis.

Figure 1
Figure 1. Figure 1: Achievable MSE–sum-rate performance for different [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Convergence behavior of the proposed AO algorithm fo [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
read the original abstract

We investigate a quantum integrated communication and computation (QICC) scheme for a single-mode bosonic multiple-access channel (MAC) with coherent-state signalling. By exploiting the natural superposition property of the quantum MAC, a common receiver simultaneously performs over-the-air computation (OAC) on the analogue symbols transmitted by one set of devices and decodes multiple-access data from another. The joint design of the transmit power control and the receive coefficient leads to a non-convex optimization problem that maximizes computation accuracy under a prescribed sum-rate communication constraint. To address this challenge, we develop a low-complexity alternating-optimization framework that incorporates: (i) closed-form linear minimum-mean square error updates for the receive coefficient, (ii) monotonicity properties of the quantum sum-rate constraint, and (iii) projected-gradient refinements for the communication powers. The proposed QICC scheme achieves an effective computation-communication trade-off with fast convergence and low computational complexity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes a quantum integrated communication and computation (QICC) scheme over a single-mode bosonic multiple-access channel with coherent-state inputs. It exploits the quantum MAC superposition property to enable simultaneous over-the-air computation on one set of devices and multiple-access decoding on another at a common receiver. The joint optimization of transmit powers and receive coefficient is cast as a non-convex problem maximizing computation accuracy subject to a sum-rate constraint; this is solved via an alternating framework using closed-form LMMSE receive updates, monotonicity properties of the quantum sum-rate, and projected-gradient power updates. The scheme is asserted to deliver an effective computation-communication trade-off together with fast convergence and low complexity.

Significance. If the alternating optimization can be shown to produce reliable local solutions and the superposition property continues to decouple the tasks under realistic noise and power limits, the work would offer a concrete low-complexity approach to integrated quantum networks. The emphasis on monotonicity exploitation and closed-form sub-steps is a practical strength that could translate to implementable protocols, but the current lack of supporting derivations, performance bounds, or numerical validation limits the immediate contribution to the information-theoretic literature.

major comments (3)
  1. Abstract: the headline claim that the QICC scheme 'achieves an effective computation-communication trade-off with fast convergence and low computational complexity' is unsupported by any derivation, convergence analysis, error bound, or simulation result in the supplied text.
  2. Optimization framework (alternating LMMSE + monotonicity + projected gradient): the problem is explicitly non-convex because the objective couples a quadratic computation error with a non-concave quantum-MAC sum-rate expression; the procedure supplies no convexity proof, duality-gap bound, or guarantee that different initializations reach comparable performance, so the claimed trade-off rests on unverified local behavior.
  3. Quantum MAC superposition assumption: the separation of computation accuracy from the sum-rate constraint is taken as given once the receive coefficient is chosen, yet the manuscript provides no explicit verification that this decoupling survives additive noise and the power constraints; if the separation fails, the entire alternating decomposition is invalidated.
minor comments (2)
  1. Notation: define the receive coefficient and its LMMSE update rule with explicit dependence on the bosonic channel parameters before invoking the closed-form expression.
  2. References: add citations to prior results on bosonic MAC capacity and over-the-air computation to situate the monotonicity argument.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment below and indicate the revisions we will incorporate to improve clarity and rigor.

read point-by-point responses

  1. Referee: Abstract: the headline claim that the QICC scheme 'achieves an effective computation-communication trade-off with fast convergence and low computational complexity' is unsupported by any derivation, convergence analysis, error bound, or simulation result in the supplied text.

    Authors: We agree that the abstract phrasing is assertive relative to the explicit derivations provided. The trade-off follows from the alternating procedure that jointly optimizes the quadratic computation error and the quantum sum-rate constraint; fast convergence and low complexity follow from the closed-form LMMSE receive update and the projected-gradient power step, both of which are computationally inexpensive per iteration. To strengthen the claim, we will revise the abstract to a more measured statement and add a dedicated subsection proving monotonic non-decrease of the objective (via the monotonicity property of the quantum sum-rate) together with numerical results illustrating convergence speed and the resulting trade-off curves. revision: yes

  2. Referee: Optimization framework (alternating LMMSE + monotonicity + projected gradient): the problem is explicitly non-convex because the objective couples a quadratic computation error with a non-concave quantum-MAC sum-rate expression; the procedure supplies no convexity proof, duality-gap bound, or guarantee that different initializations reach comparable performance, so the claimed trade-off rests on unverified local behavior.

    Authors: The joint problem is indeed non-convex, as stated in the manuscript. The alternating scheme is presented as a practical heuristic that exploits closed-form subproblem solutions and the monotonicity of the feasible set under the sum-rate constraint; each iteration therefore produces a feasible point with non-increasing computation error when the rate constraint is active. We do not claim global optimality or provide a duality-gap bound. In the revision we will add an explicit discussion of the local nature of the obtained solutions, the dependence on initialization, and a recommended equal-power starting point that yields consistent performance across the evaluated regimes. revision: partial

  3. Referee: Quantum MAC superposition assumption: the separation of computation accuracy from the sum-rate constraint is taken as given once the receive coefficient is chosen, yet the manuscript provides no explicit verification that this decoupling survives additive noise and the power constraints; if the separation fails, the entire alternating decomposition is invalidated.

    Authors: The bosonic MAC with coherent-state inputs admits a linear superposition at the receiver output (weighted sum of fields plus vacuum noise), which permits the receive coefficient to be chosen solely for MSE minimization while powers are adjusted to meet the sum-rate constraint. The decoupling therefore holds by construction of the alternating steps. We acknowledge that an explicit robustness check under additive noise and power limits is missing. In the revision we will insert a short derivation showing that the MSE expression remains separable from the rate constraint for any feasible power vector and that the noise term appears only as an additive constant in the MSE, thereby preserving the validity of the decomposition. revision: yes

Circularity Check

0 steps flagged

No circularity: alternating optimization derived directly from stated non-convex problem without reduction to inputs or self-citation.

full rationale

The paper sets up a non-convex joint optimization of transmit powers and receive coefficient to maximize computation accuracy subject to a sum-rate constraint on the quantum MAC. It then proposes an alternating framework using closed-form LMMSE updates, monotonicity of the sum-rate, and projected-gradient steps. These steps are standard algorithmic responses to the stated problem structure; the claimed trade-off, convergence, and complexity follow from the construction of the algorithm and its application, not from any fitted parameter renamed as prediction, self-definitional loop, or load-bearing self-citation. No equation or claim reduces to its own inputs by construction. The derivation remains self-contained against the bosonic MAC model.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard assumptions of bosonic channel physics and coherent-state signalling; the optimization introduces fitted parameters whose independence from the reported performance is not verifiable from the abstract alone.

free parameters (2)
  • transmit power allocations
    Chosen to satisfy the sum-rate constraint while maximizing computation accuracy
  • receive coefficient
    Updated via LMMSE within the alternating optimization loop
axioms (2)
  • domain assumption Coherent-state signalling is used over the single-mode bosonic MAC
    Standard modelling choice in quantum optics and communication
  • domain assumption Natural superposition property of the quantum MAC enables simultaneous OAC and decoding
    Invoked to justify the joint receiver operation

pith-pipeline@v0.9.0 · 5450 in / 1382 out tokens · 60241 ms · 2026-05-10T17:27:36.905909+00:00 · methodology

discussion (0)

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Reference graph

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