Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The authors present a quantum neural network architecture for 3D graph learning. the main idea of the paper is to encode the spatial information into a qubit before training the neural network. This idea is quite straightforward and the evidence that this outperforms classical or other quantum architectures is really minimal.  
strength_and_weaknesses: + interesting idea of encoding directly the spatial information in the degrees of freedom of a qubit
- very little evidence that this outperforms many other quantum neural network ideas out there
- no theoretical understanding of the architecture. For example, barren plateau will appear in this architecture
clarity,_quality,_novelty_and_reproducibility: The ideas here are clear and straightforward. The results could be reproduced.
summary_of_the_review: A simple idea for a quantum neural network for 3D structure that misses both theoretical analysis of convergence (barren plateau, etc) and also no siginificant experimental evidence that it is better than other techniques.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper provides a novel aspect of using quantum ML for geometric data modeling. This is the first work in this research line, especially for quantum chemistry.
strength_and_weaknesses: Strengths:
- This paper introduces using the quantum computing for geometric data modeling. This is a promising direction.
- The paper is self-contained, covering the basic information for quantum ML.

Weaknesses:
- Turn the atom type, distance, and angle into qubits. Then what is the advantage of this operation? The authors claim that this is more “intuitive”, which can be subjective. Can authors provide more objective reason for this?

- Since this work is problem driven, i.e., the main focus is on modeling the quantum chemistry data, then authors may give an overview of this research line. But it is not present in Sec 2. Now the authors give a concrete discussion on quantum ML (sec 2), which is good. But none of them can be used as the baseline (or to be directly related to this problem).

- The insights or motivation of the method is missing. Existing SE(3)-invariant GNNs (DimeNet, SphereNet, GemNet) are using the exact same information for modeling (atom type, distance, and angle), not directly on the 3D Euclidean distance. They can be formulated with the Spherical Fourirer-Bessel basis framework, and this work seems to be using an alternative solution. 

- The experimental results are not strong enough to verify the effectiveness of using quantum computing for geometric data modeling. Besides, there are more tasks in QM9 and some key baselines are missing. The baseline compared in this work only covers the SE(3)-invariant NN. There exists another large track of method on SE(3)-equivariant GNNs, such as EGNN[1], Allegro[2].



- A minor point. “However, these approaches are still simulating the energies of certain small molecules like H2…” I think the authors should give reference for this sentence. Actually a lot of work, like NequIP[3], Allegro[2] are considering the MD17 dataset, where the drug molecules are much larger.

[1] Satorras, Vıctor Garcia, Emiel Hoogeboom, and Max Welling. "E (n) equivariant graph neural networks." International conference on machine learning. PMLR, 2021.

[2] Musaelian, Albert, et al. "Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics." arXiv preprint arXiv:2204.05249 (2022).

[3] Batzner, Simon, et al. "E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials." Nature communications 13.1 (2022): 1-11.

clarity,_quality,_novelty_and_reproducibility: Generally, this paper is well written. I’m not familiar with quantum computing, but I can get the high-level idea of that through the paper.

summary_of_the_review: I appreciate the authors for introducing quantum computing for geometric modeling. I think it is a promising direction. I’m familiar with the existing SE(3)-equivariant GNN literature, and from this aspect, the current story is not clear to me because:
- There have been a lot of existing SE(3)- / E(3)-invariant or equivariant GNN models. They are mainly using the spherical harmonic basis (not 3D coordinates), and they have done quite well on the QM9 datasets.
- Empirically, this work is not doing overwhelmingly well on QM9 prediction; and this work is not giving sufficient insights on how this new framework is better than previous work. I would like to raise my score if either of the questions could be solved.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This manuscript tackles the challenging problem of graph learning in 3D. The authors proposed the first use of quantum computing to learn quantum 3D latent representation of 3D structures from the Hilbert space composed of the Bloch sphere of each qubit. The proposed method preserves equivariance and invariance of 3D graphs. The authors implemented the quantum circuit full-amplitude simulator on a classical processor. The model has a smaller number of parameters compared with existing models but achieved comparable results on small graphs. 
strength_and_weaknesses: Strengths:
(1) The paper proposed an interesting methodology and is the first to use qubits to encode 3D relative positional information. It translates the 3D Cartesian coordinates to the relative position tuple (d,θ,ϕ) between the two atoms. Relative coordinates are used to ensure equivariance and invariance.  
(2) The paper is clearly written with a coherent structure. 

Weaknesses:
(1) In Section 3.3, the authors justify their usage of a distance threshold by saying “As the bond length increases, the interaction becomes much weaker, which means we barely have multi-hop message passing in our graph”. It is known that multi-hop interactions can have non-trivially effects the quantum properties of molecules. It may be interesting to compare the performance of 1-hop vs 2-hop. Section 4, the distance threshold is set to 1.77. Some explanations about this choice of threshold will help. How would a higher distance threshold affect the computing speed and results? 
(2) Table 2, why not report the std of each property prediction task? SphereNet performs significantly better than the proposed approach. Is it possible to give some (theoretical) estimations on improvement of performance if more parameters are used in the proposed model?
(3) The idea of using relative coordinates is not novel.

clarity,_quality,_novelty_and_reproducibility: The paper is in general clearly written and the model is described in detail. The idea of embedding a 3D graph using qubit is novel. However, the competitive edge of quantum computing in the task at hand should be more thoroughly justified through more experiences and ablation study. 
summary_of_the_review: This paper present a novel application of quantum computing to learn 3D graphs, which lead to a model with substantially small number of parameters. The lackluster experiment results show that the proposed model works on small graphs. Several weaknesses should be addressed. The proposed method can potentially take advantage of large-scale quantum devices.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: In this work, the authors propose using quantum computing approach to learn the 3D latent representation of molecular structures, with two solid demonstration in downstream tasks. Overall this work is well presented and reader friendly overall. The major concern is that the overall scope of this work is limited.
justification_for_why_not_higher_score: While this work proposes interesting ideas, there are still improvements needed to further show the significant advancement of the proposed method as noted by the reviewers.
justification_for_why_not_lower_score: NA
Decision: Reject