Quantum Deep Learning for Massive MIMO User Scheduling

Anshu Mukherjee; Avishek Nag; Mouli Chakraborty; Ruining Fan; Xingyu Huang

REVIEW 4 major objections 5 minor 14 references

Quantum Deep Learning for Massive MIMO User Scheduling

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A hybrid quantum-classical neural network, trained by reinforcement learning on statistical channel information, can schedule users in massive MIMO downlinks with higher sum rate than a CNN benchmark, the paper claims.

desk verdict A plausible but under-supported first application of hybrid QNNs to mMIMO scheduling; the idea deserves scrutiny, but the evaluation needs far more rigor before the outperformance claim can be believed. read the letter →

arxiv 2508.03327 v1 pith:UIGE53EB submitted 2025-08-05 eess.SP

Xingyu Huang , Ruining Fan , Mouli Chakraborty , Avishek Nag , Anshu Mukherjee This is my paper · ORCID

classification eess.SP

keywords quantumneuralnetworkmassiveMIMOuserschedulingstatisticalCSIvariationalcircuitreinforcementlearninghybridquantum-classicalsum-rateoptimization

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that user scheduling in massive MIMO downlinks can be done by a hybrid quantum-classical neural network that reads only statistical channel information rather than full instantaneous CSI. The model is a three-layer architecture: a classical network compresses the beam-domain channel-gain matrix, a variational quantum circuit with angle encoding and entanglement learns a policy representation, and a classical layer maps measurement outcomes to a top-L user selection. Training uses a policy-gradient reinforcement learning algorithm whose reward is a proportional-fairness-weighted approximate sum rate. In simulated correlated Rician fading channels, the hybrid model is reported to converge faster and to achieve higher sum rates than a similarly sized CNN benchmark, with the gap growing in moderate-to-high SNR and denser user configurations. If these results hold, quantum-enhanced machine learning becomes a practical candidate for scheduling decisions in 5G/B5G systems where full CSI acquisition is too expensive.

What carries the argument

The central object is the hybrid-QNN policy network: a variational quantum circuit (the QNN kernel) wrapped by classical pre- and post-layers. Each input is a K-by-K matrix of beam-domain gains from statistical CSI; the classical pre-layer reduces it to nq real angles, which are encoded by RY gates; the circuit applies N variational layers of RZ-RY-RZ single-qubit rotations interlaced with CNOT entangling gates in a ring; Pauli-Z measurement yields nq real expectations; and a final linear layer maps those to K scheduling scores, with the L largest scores selected. The design's work is to turn a high-dimensional, noisy statistical-CSI matrix into a discrete scheduling decision whose quality is measured by the approximate sum-rate expression (6). The reinforcement-learning loop (epsilon-greedy exploration, cosine-annealed momentum baseline, matrix-based reward, gradient clipping) is what tunes the classical and quantum parameters.

What would settle it

Evaluate the trained hybrid-QNN and CNN policies under the full Monte-Carlo ergodic sum-rate (4) on the same Rician channel realizations across SNR; if the QNN's sum-rate advantage over the CNN shrinks below the noise floor or reverses, the central claim that the quantum kernel improves scheduling is refuted.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a variational quantum circuit can serve as the trainable kernel of a user-scheduling policy in a single-cell multi-user massive MIMO system, and that this policy outperforms a classical CNN benchmark under the same channel realizations. The model takes the statistical-CSI beam-domain gain matrix, flattens and standardizes it, compresses it with a classical layer to a number of qubits, encodes the result via RY rotations, applies stacked variational layers with ZYZ single-qubit decompositions and ring-topology CNOT entanglement, and reads Pauli-Z expectations; a final linear layer produces scheduling scores for a top-L selection. The whole pipeline is trained end-to-end with a reinforcement-learning algorithm that maximizes a proportional-fairness-weighted approximate sum rate. The paper reports faster policy convergence, lower training loss at stable configurations, and higher sum rates than the CNN baseline, especially at SNR above roughly 10 dB and with larger antenna arrays.

Load-bearing premise

The load-bearing premise is that the approximate ergodic sum-rate formula in equation (6), taken from earlier work, is accurate enough to serve both as the RL reward and as the performance metric; if it diverges from the true channel behavior in the tested Rician scenarios, the trained policy is optimizing a surrogate objective and the reported advantage over the CNN may not transfer to the real sum rate.

Editorial extensions

If this is right

If the hybrid QNN's advantage holds on real channels, user scheduling in dense 5G/B5G cells can be run off slowly varying statistical CSI, avoiding the overhead of instantaneous CSI acquisition.
The policy-gradient training with matrix-based reward gives a template for other discrete resource-allocation decisions such as beam selection, power control, and pilot assignment where the objective is a sum of rates.
Since the gap over CNN grows with user and antenna count, quantum kernels become more attractive exactly in the interference-rich regimes where classical scheduling is hardest.
The reported robustness in noisy channels suggests the variational circuit can tolerate imperfect statistical CSI, easing deployment.
The top-L selection from learned logits is a generic combinatorial-selection head that can be re-trained for different L and K without changing the circuit size.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension would be to use the same architecture with exact Monte-Carlo ergodic rate reward (4) instead of the approximation (6); if the QNN still beats CNN, the claimed advantage is not an artifact of the surrogate objective.
The comparison's fairness could be checked by scaling the CNN width and depth to match the QNN's parameter count and expressiveness; the paper does not report such an ablation, so part of the observed gap may come from an under-provisioned classical benchmark rather than a quantum effect.
Because the evaluation uses simulated Rician channels and idealized quantum circuit execution, the implicit assumption is that the variational unitary can be implemented at high gate fidelity; a hardware-in-the-loop test at current noisy-device error rates would clarify whether the advantage survives.
If equation (6) is accurate, the same reward can be used to train purely classical policies, and comparing those against the QNN would isolate whether the quantum feature map captures beam-domain interference patterns that classical convolutions miss.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a legitimate new application—hybrid QNN for user scheduling in massive MIMO with statistical CSI—but the evaluation as written doesn't back the headline claim. The paper is worth engaging, not because the results are convincing, but because the question is sensible and the approach is concrete.

What's new: prior work used deep RL for scheduling or QNNs for beamforming; this is the first combination of statistical CSI with a variational quantum circuit for scheduling, trained with a policy-gradient RL loop. The architecture is standard (angle encoding, RY rotations, CNOT ring, Pauli-Z measurements, classical pre/post layers), but that's not a flaw—it's a reasonable starting point. The system model and problem formulation are clear, and Algorithm 1 is detailed enough to be reimplemented in principle.

What undermines the central claim: the comparison in Fig. 6 measures sum-rate using Eq. (6), the same approximate expression used as the training reward. That means the QNN-vs-CNN gap could reflect which model better optimizes the surrogate, not necessarily the true ergodic sum-rate. The paper gives no Monte-Carlo validation of Eq. (6) in the tested Rician scenarios, so the stress-test concern is real. Additionally, there are no hyperparameters, no number of seeds, no error bars, and no comparison to any prior classical scheduler—only to a self-designed CNN. That's a weak evidential base for "outperforms classical schedulers." There are also small but telling errors in Algorithm 1: the loss line uses E as if it were an expectation, and the update line "L ← L + L" looks like a typo. These suggest the manuscript wasn't carefully proofread.

What's good: the authors acknowledge the intractability of the true ergodic rate and are explicit about using an approximation. The RL formulation with proportional fairness is sensible. The idea of using a quantum kernel for a combinatorial selection problem is not crazy—discrete scheduling over K users with L slots has a large search space, and quantum circuits can represent correlations that may help.

Bottom line: the paper is a preliminary idea, not a demonstrated result. With code, seeds, error bars, a validated reward proxy, and a comparison to an existing scheduler (e.g., the deep learning approach in [7]), it could be a solid contribution. As is, it deserves a serious referee—the topic is relevant and the approach is concrete—but the reviewer should push for substantial revision.

Recommendation: send to peer review with a strong request for reproducibility details, and consider desk-rejecting only if the authors can't provide code/data upon request.

Referee Report

4 major / 5 minor

Summary. The paper proposes a hybrid quantum-classical neural network (QNN) for user scheduling in a single-cell massive MIMO downlink system with statistical CSI. The scheduler is trained with a policy-gradient RL algorithm using a beam-domain approximate sum-rate expression as reward, and the architecture combines classical pre/post neural network layers with a variational quantum circuit. Simulation results report sum-rate comparisons against a CNN baseline across different user-antenna configurations and SNR values, and the authors claim faster convergence and higher sum-rate, especially in dense, high-SNR regimes. The manuscript is best understood as a proof-of-concept study: the problem formulation is sensible and the architectural idea is interesting, but the current evidence for the central outperformance claim is incomplete.

Significance. If the claims were fully validated, the work would be a useful proof-of-concept for applying hybrid quantum-classical models to wireless scheduling, an area where quantum machine learning applications are still scarce. The use of statistical CSI to avoid instantaneous CSI overhead, the RL-based training formulation, and the explicit comparison to a CNN baseline are all appropriate components. However, the paper currently ships no code, no complete hyperparameter configuration, and no validation of the approximate reward surrogate that drives all reported results. Because the evaluation metric is the same as the training reward, the comparison is self-consistent but not yet convincing as an actual ergodic-sum-rate advantage. The contribution is potentially valuable, but the evidence presented is preliminary.

major comments (4)

[Section II-B, Eq. (6), and Section IV] The approximate sum-rate expression in Eq. (6) is used both as the reward in Algorithm 1 and as the performance metric in Figs. 4-6. The manuscript does not validate this approximation against the exact ergodic sum-rate in Eq. (4) for the specific Rician scenarios tested (K-factor, M, K, and SNR ranges). If Eq. (6) systematically misranks candidate schedules relative to Eq. (4), the reported QNN advantage could be an artifact of the surrogate objective. Please add a Monte-Carlo comparison of Eq. (6) versus Eq. (4) over the tested parameter grid, or explicitly restrict the claim to the approximate sum-rate and justify the restriction from the cited source [8].
[Section IV, Figs. 3-6] No complete experimental configuration is reported: the number of qubits nq is only given in figure captions, and the number of variational layers N, learning rate, optimizer, batch size, number of epochs E, exact CNN architecture, epsilon schedule, and number of random seeds are all absent. The figures show single curves with no error bars or confidence intervals, so the performance gap in Fig. 6 cannot be distinguished from run-to-run randomness. Please provide a full hyperparameter table, report mean and standard deviation over at least several seeds, and include statistical significance tests for the QNN-versus-CNN comparison.
[Algorithm 1, lines 8-14] The policy gradient update is not precisely defined. Line 8 defines π = σ(logits), line 9 samples p ∼ Bernoulli(π) with ε-greedy exploration, and line 12 uses log π, but it is unclear whether the loss uses the log-probability of the sampled action p under the Bernoulli policy or something else. In addition, line 14 contains 'L ← L + L', which appears to be a typo for accumulating the loss, and the baseline b is updated with mean(r) while the loss uses (r - b) at the batch-element level; the variance-reduction property of the baseline depends on this dimension mismatch being resolved. Please write out the exact per-sample loss and gradient computation.
[Section III-B, Eq. (7), and Algorithm 1] The problem formulation includes proportional fairness through the update of R̄_l(t) in Eq. (7), but Algorithm 1 describes the reward only as 'sumrate reward(Gin, p)' and shows no update of the historical average rate R̄. If fairness is included in the reward, the algorithm should specify how; if it is not, the text overstates the objective. This matters because the fairness term changes the optimal policy and therefore affects the interpretation of the QNN-versus-CNN comparison.

minor comments (5)

[Abstract and Section I] There are typographical issues: 'Convolutional Neural Networkss' in the abstract and 'Tertiary' in the related-work paragraph should be corrected.
[Figures 3-6] The figures do not label the axes or show units, and Fig. 3 has no axis labels. The text also refers to '20 training iterations' while Algorithm 1 uses E epochs; please clarify the correspondence.
[Section II-A, Eqs. (3) and (6)] The selection of x_l, the index of the maximum diagonal element of U_l used to choose the beamforming direction, is not defined precisely. Please state how x_i is selected for each user, since it directly enters the approximate rate expression in Eq. (6).
[Section IV, Figs. 5-6] The caption for Fig. 5 states 'qubits=8' while Fig. 6 states 'qubits=10'; if this is intentional, give the reason for using a different number of qubits in the comparison; otherwise the inconsistency should be corrected.
[Section V] The conclusion says the model 'maximises ergodic sum-rate', but the actual objective is the proportional-fair approximate sum-rate of Eq. (7) with an approximation from Eq. (6). Please rephrase to match what is optimized and evaluated.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the QNN-vs-CNN comparison is an empirical RL evaluation on the same surrogate reward; the use of Eq. (6) as both reward and metric is a validity concern, not a circular reduction.

full rationale

The paper's chain is: true ergodic rate (4) is approximated by the beam-domain expression (6) from prior work [8]; the same beam-domain gains a_l^(x_i) form the input feature matrix G_in in (8); the hybrid-QNN policy is trained with the reward r = sumrate reward(G_in, p) in Algorithm 1; and the reported sum-rate curves in Figs. 4-6 evaluate the same surrogate objective. This is a standard reinforcement-learning training/evaluation loop, not a derivation that reduces to its own inputs. Both the proposed QNN and the CNN benchmark are trained and evaluated on the identical objective, so the comparative claim is empirically meaningful rather than forced by construction. No parameter is fitted to a subset of data and then announced as a prediction of that same subset; no load-bearing self-citation appears (references [7] and [8] are prior works by different authors and supply the problem formulation and the surrogate rate model, not the claimed QNN advantage); no uniqueness theorem is imported from the authors' own prior work; no ansatz is smuggled in via self-citation; and no known result is merely renamed. The legitimate concern is that Eq. (6) replaces the expectation in Eq. (4) with a deterministic function of the beam-domain covariance matrix, and the paper does not demonstrate that this approximation preserves the ordering of scheduling policies in the tested Rician scenarios. If Eq. (6) is biased, then the trained policies are optimized for a surrogate objective and the headline claim about maximizing the true ergodic sum-rate would be over-stated. That is a validation/correctness risk, not circularity: the paper's own derivation does not define its conclusion into existence, and the comparison between architectures is self-consistent. Under the hard rule requiring a quoted equation-level reduction or a fitted parameter renamed as a prediction, no circular step can be exhibited. The honest finding is therefore no significant circularity, score 0.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a standard RL system model and a hybrid QNN architecture. There are no invented particles or forces. The free parameters are typical hyperparameters, but many are unreported, which hurts reproducibility.

free parameters (6)

Number of qubits nq = 8 or 10
Chosen per experiment, controls input compression and circuit width.
Number of variational layers N = Not specified
Controls circuit depth and expressiveness.
Learning rate = Not specified
Used in the optimizer, not reported in the paper.
Epochs E = Not specified
Training length, not reported.
Exploration schedule epsilon = 0.6 decaying to 0.05
Set in Algorithm 1, part of the RL training.
Baseline momentum alpha = Cosine schedule from 0.3 to 0.7
Set in Algorithm 1, used for baseline update.

assumptions (4)

domain assumption Rate approximation (6) accurately represents the ergodic sum-rate.
Taken from [8] and used as the reward and the evaluation metric.
domain assumption Statistical CSI is sufficient for user scheduling.
Core premise of the paper, stated in the abstract and Section I.
standard math The Rician channel model (2) from prior work applies.
Used as the system model without modification, citing [8].
domain assumption The variational quantum circuit can be executed or simulated as described.
No simulator or hardware details are provided, so the reader cannot verify the QNN implementation.

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Cite this review

Pith. "Pith review of Quantum Deep Learning for Massive MIMO User Scheduling." pith.science (2026). https://pith.science/paper/UIGE53EB

@misc{pith2026250803327,
  author       = {Pith},
  title        = {Pith review of: Quantum Deep Learning for Massive MIMO User Scheduling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UIGE53EB}},
  note         = {Machine review of arXiv:2508.03327}
}

read the original abstract

We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addressing the scalability issues of traditional methods. By leveraging statistical Channel State Information (CSI), our model reduces computational overhead and enhances spectral efficiency. It integrates classical neural networks with a variational quantum circuit kernel, outperforming classical Convolutional Neural Networks (CNNs) and maintaining robust performance in noisy channels. This demonstrates the potential of quantum-enhanced Machine Learning (ML) for wireless scheduling.

Figures

Figures reproduced from arXiv: 2508.03327 by the authors.

**Figure 1.** Three-layered Model Architecture simplified expression that matches the Monte-Carlo result as follows [8] T¯ l ≈ Tˆ l = log2 PK i=1 βia (xi) l ξ i + 1 PK i=1,i̸=l βia (xi) l ξ i + 1! (6) where βi = Pi/σ2 l denotes the transmit Signal-to-noise Ratio (SNR) in linear, a (xi) l is the xi-th diagonal element of Ul , denoting the channel gain of beamforming in the optimal transmission direction if i = l, otherwise, denot… view at source ↗

**Figure 2.** An example of learnable variational quantum circuits. An input vector [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 6.** Sum-rate comparison between QNN and CNN across [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Sum-rate versus the number of antennas for different [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Sum-rate against SNR for different user-antenna con [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

14 extracted references · 13 canonical work pages

[7]

Deep learning based user schedul- ing for massive mimo downlink system,

X. Yu, J. Guo, X. Li, and S. Jin, “Deep learning based user schedul- ing for massive mimo downlink system,” Science China Information Sciences, vol. 64, no. 8, p. 182304, 2021

work page 2021
[8]

Joint scheduling and deep learning-based beamforming for fd-mimo systems over correlated rician fading,

X. Li, X. Yu, T. Sun, J. Guo, and J. Zhang, “Joint scheduling and deep learning-based beamforming for fd-mimo systems over correlated rician fading,” IEEE Access , vol. 7, pp. 118 297–118 309, 2019

work page 2019
[1]

Machine learning assisted user- scheduling method for massive mimo system,

J. Shi, W. Wang, J. Wang, and X. Gao, “Machine learning assisted user- scheduling method for massive mimo system,” in 2018 10th Interna- tional Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2018, pp. 1–6

work page 2018
[2]

Pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels,

L. You, X. Gao, X.-G. Xia, N. Ma, and Y . Peng, “Pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels,” IEEE Transactions on Wireless Communications , vol. 14, no. 6, pp. 3352–3366, 2015

work page 2015
[3]

Dealing with csi compression to reduce losses and overhead: An artificial intelligence approach,

M. K. Shehzad, L. Rose, and M. Assaad, “Dealing with csi compression to reduce losses and overhead: An artificial intelligence approach,” in 2021 IEEE international conference on communications workshops (ICC Workshops). IEEE, 2021, pp. 1–6

work page 2021
[4]

Quantum machine learning—an overview,

K. A. Tychola, T. Kalampokas, and G. A. Papakostas, “Quantum machine learning—an overview,” Electronics, vol. 12, no. 11, p. 2379, 2023

work page 2023
[5]

A deep reinforcement learning-based resource scheduler for massive mimo networks,

Q. An, S. Segarra, C. Dick, A. Sabharwal, and R. Doost-Mohammady, “A deep reinforcement learning-based resource scheduler for massive mimo networks,” IEEE transactions on machine learning in communi- cations and networking , vol. 1, pp. 242–257, 2023

work page 2023
[6]

Hybrid quantum-classical neural networks for downlink beamforming optimization,

J. Zhang, G. Zheng, T. Koike-Akino, K.-K. Wong, and F. Burton, “Hybrid quantum-classical neural networks for downlink beamforming optimization,” IEEE Transactions on Wireless Communications , 2024

work page 2024

Show all 14 references

[9]

Beam di- vision multiple access transmission for massive mimo communications,

C. Sun, X. Gao, S. Jin, M. Matthaiou, Z. Ding, and C. Xiao, “Beam di- vision multiple access transmission for massive mimo communications,” IEEE Transactions on Communications , vol. 63, no. 6, pp. 2170–2184, 2015

2015
[10]

Proportional fair space-time scheduling for wireless com- munications,

V . K. Lau, “Proportional fair space-time scheduling for wireless com- munications,” IEEE Transactions on Communications , vol. 53, no. 8, pp. 1353–1360, 2005

2005
[11]

Quantum angle encoding with learnable rotation applied to quantum–classical convolutional neural networks,

E. Ovalle-Magallanes, D. E. Alvarado-Carrillo, J. G. Avina-Cervantes, I. Cruz-Aceves, and J. Ruiz-Pinales, “Quantum angle encoding with learnable rotation applied to quantum–classical convolutional neural networks,” Applied Soft Computing , vol. 141, p. 110307, 2023

2023
[12]

Advances in quantum deep learning: An overview,

S. Garg and G. Ramakrishnan, “Advances in quantum deep learning: An overview,” arXiv preprint arXiv:2005.04316 , 2020

2005 arXiv
[13]

Efficient decomposition of unitary matrices in quantum circuit compilers,

A. M. Krol, A. Sarkar, I. Ashraf, Z. Al-Ars, and K. Bertels, “Efficient decomposition of unitary matrices in quantum circuit compilers,” Ap- plied Sciences , vol. 12, no. 2, p. 759, 2022

2022
[14]

Quantum entanglement in neural network states,

D.-L. Deng, X. Li, and S. Das Sarma, “Quantum entanglement in neural network states,” Physical Review X , vol. 7, no. 2, p. 021021, 2017

2017

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [7]

Deep learning based user schedul- ing for massive mimo downlink system,

X. Yu, J. Guo, X. Li, and S. Jin, “Deep learning based user schedul- ing for massive mimo downlink system,” Science China Information Sciences, vol. 64, no. 8, p. 182304, 2021

work page 2021

[2] [8]

Joint scheduling and deep learning-based beamforming for fd-mimo systems over correlated rician fading,

X. Li, X. Yu, T. Sun, J. Guo, and J. Zhang, “Joint scheduling and deep learning-based beamforming for fd-mimo systems over correlated rician fading,” IEEE Access , vol. 7, pp. 118 297–118 309, 2019

work page 2019

[3] [1]

Machine learning assisted user- scheduling method for massive mimo system,

J. Shi, W. Wang, J. Wang, and X. Gao, “Machine learning assisted user- scheduling method for massive mimo system,” in 2018 10th Interna- tional Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2018, pp. 1–6

work page 2018

[4] [2]

Pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels,

L. You, X. Gao, X.-G. Xia, N. Ma, and Y . Peng, “Pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels,” IEEE Transactions on Wireless Communications , vol. 14, no. 6, pp. 3352–3366, 2015

work page 2015

[5] [3]

Dealing with csi compression to reduce losses and overhead: An artificial intelligence approach,

M. K. Shehzad, L. Rose, and M. Assaad, “Dealing with csi compression to reduce losses and overhead: An artificial intelligence approach,” in 2021 IEEE international conference on communications workshops (ICC Workshops). IEEE, 2021, pp. 1–6

work page 2021

[6] [4]

Quantum machine learning—an overview,

K. A. Tychola, T. Kalampokas, and G. A. Papakostas, “Quantum machine learning—an overview,” Electronics, vol. 12, no. 11, p. 2379, 2023

work page 2023

[7] [5]

A deep reinforcement learning-based resource scheduler for massive mimo networks,

Q. An, S. Segarra, C. Dick, A. Sabharwal, and R. Doost-Mohammady, “A deep reinforcement learning-based resource scheduler for massive mimo networks,” IEEE transactions on machine learning in communi- cations and networking , vol. 1, pp. 242–257, 2023

work page 2023

[8] [6]

Hybrid quantum-classical neural networks for downlink beamforming optimization,

J. Zhang, G. Zheng, T. Koike-Akino, K.-K. Wong, and F. Burton, “Hybrid quantum-classical neural networks for downlink beamforming optimization,” IEEE Transactions on Wireless Communications , 2024

work page 2024