Quantum Optical Experiments Modeled by Long Short-Term Memory
Authors: Thomas Adler
Manuel Erhard
Mario Krenn
Johannes Brandstetter
Johannes Kofler
Sepp Hochreiter
Venue:  (2020)
Keywords: Recurrent Networks
LSTM
Sequence Analysis
Binary Classification

Abstract: We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search but is also an essential step towards automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.

Under review as a conference paper at ICLR 2020
QUANTUM OPTICAL EXPERIMENTS MODELED BY LONG SHORT- TERM MEMORY
Anonymous authors Paper under double-bling review
ABSTRACT
We demonstrate how machine learning is able model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technolo- gies such quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-tern memory (LSTM) neural network can successfully Iearn to model quantum experiments by correctly predict- ing output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search but is also an essential step towards automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.
computer and calculate the respective output states. In this work,
INTRODUCTION
In the past decade, artificial neural networks have been applied to plethora of scientific disciplines,
commercial applications, and every-day tasks with outstanding performance in, e.g., medical diagno- sis, self-driving, and board games (Esteva et al.] 2017] (iilver al. 2017). In contrast to standard feedforward neural networks, long short-term memory (LSTM) (Hochreiter) T99T Hochreiter Schmidhuber] 1997) architectures have recurrent connections, which allow them process sequential
data such as text and speech (Sutskever et al. 2014).
Such sequence-processiige capabilities can be particularly useful for designing complex quantum experiments, since the final state of quantum particles depends on the sequence of elements, i.e. the experimental setup, these particles pass through. For instance, in quantum optical experiments, photons may traverse a sequence of wave plates, beam splitters, and holographic plates. High- dimensional quantum states important for multiparticle and multisetting violations of local realist models as well as for applications in emerging quantum technologies such as quantum communication and eгror correction in quantum computers (Shor 2000 Kaszlikowski al. (0000).
Already for three photons and only a few quantum levels, it becomes in general infeasible for humans determine the required setup for a desired final quantum state, which makes automated design procedures for this inverse problem necessary. One example of such an automated procedure is the algorithm MELVIN (Krenn et al., 2016). which uses toolbox of optical elements, randomly generates sequences of these elements, calculates the resulting quantum state, and then checks whether the state interesting, i.e. maximally entangled and involving many quantum levels. The setups proposed by MELVIN have been realized in laboratory experiments (Malik et al., 2016)] Erhard cet al. 201 8b). Recently. also a reinforcement learning approach has been applied to design new experiments (Melnikov al. 2018).
Inspired by these advances, we investigate how LSTM networks can learn quantum optical setups and prediet the characteristies of the resulting quantum states. We train the neural networks using millions of setups generated by MELVIN. The huge amount of data makes deep learning approaches the first choice. We cluster cross validation (Mayr et al. 2016) to evaluate the models.

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2 METHODS
2.1 TARGET VALUES
Let us consider a quantum optical experiment using three photons with orbital angular momentum (OAM) (Yao & Padgett 2011 Erhard et al. 2018a). The OAM of photon is characterized by an integer whose size and sign represent the shape and handedness of the photon wavefront, respectively. For instance, after a series of optical elements, a three particle quantum state may have the following form:
V = (10,0,0) + 11,0,1) + 2, + 1,).
(1)
2
This state represents a physical situation, in which there is 1/4 chance (modulus square of the amplitude value 1/2) that all three photons have OAM value 0 (first term), and 1/4 chance that photons and 3 have OAM value 1, while photon 2 has OAM value O (second term), and so on for the two remaining terms.
We are generally interested in two main characteristics of the quantum states: (1) Are they maximally entangled? (2) Are they high-dimensional? The dimensionality of state represented by its Schmidt rank vector (SRV) (Huber & de Vicente 2013 Huber et al. 2013).. State (V) is indeed maximally entangled because all terms on the right hand side have the same amplitude value. Its SRV (4,2,2), as the first photon is four-dimensionally entangled with the other two photons, whereas photons two and three are both only two-dimensionally entangled with the rest.
A setup is labeled "positive" (yE 1) if its output state is maximally entangled and if the setup obeys some further restrictions, e.g., behaves well under multi-pat pair emission, and otherwise labeled "negative" (yE 0). The target label capturing the state dimensionality is the SRV ysnv (n, m, k)). We train LSTT networks to directly predict these state characteristics (entanglement and SRV) from given experimental setup without actually predicting the quantum state itself.
2.2 LOss FUNCTION
For classification, use binary cross entropy (BCE) in combination with logistic sigmoid output activation for learning. For regression, it is always possible to reorder the photon labels such that the SRV has entries in non-increasing order. An SRV label thus represented by 3-tuple ysnv (n, m. kc) which satisfies n 2 m 2 k. With slight abuse of notation, we model ~ P(A) random variable and m B(n, p), k ~ B(m, g) as Binomials with ranges m {1....n) n and & E {1...., m) and success probabilities and q, respectively. The resulting log-likelihooo objective (omitting all terms not depending on X, p. q) for a data point æ with label (n, (n,m,k k,))
as Poisson-distribune
k)T is
((), P. x) = n log Ā - Å m log + (n m) log(1 p) + log + (m kb) log(1 - 9) (2)
where A. ② are the network predictions (i.e. functions of x:) for the distribution parameters of n, m, respectively. The Schmidt rank value predictions are ń = X, m pi, k P... To see this, we need to consider the marginals of the joint probability mass function
(n - ^ pm -p) pm" m
f(n, m,k)
n!. m
(1-
(3)
To obtain the marginal distribution of m, can first over all possible k which is easy. To sum out n we first observe that (m) O for n < m, i.c. the first m terms are zero and we may write
f(m) Σ [(n = Σ {mm + n, m)
(4)
n=0
n=0
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LSTM
LSTM
LSTM
LSTM
22
33
N
Figure 1: Sequence processing model for a many-to-one mapping. The target value ý can be either an estimate for ye (entanglement classification) ySRv (SRV regression).
capturing only non-zero terms. It follows that
x
n m
f(m)
pd p)"
n= n2 + m)!
X² p)"
(n + m
> p A
(5)
(n + m)!
n=U
pm m
X p)" e PT (pA)"
m!!
n!!
m!!
which is P(pA)-distribuue Using the same argument for we get that the marginal of k is P(p distributed. The estimates îí m, h are obtained by taking the means of their respective marginals.
2.3 NETWORK ARCHITECTURE
The used sequence processing model is depicted in Figure 1 We train two networks, one for entanglement classification (target yg), and one for SRV regression (target USRV). The reason why we avoid multitask learning this context is that do not want to incorporate correlations between entanglement and SRV into our models. For instance, the SRV (6,6,6) was only observed in non- maximally entangled samples so far, which a perfect correlation. This would cause multitask network to automatically label such sample as negative only because of its SRV. By training separate networks we lower the risk incorporating such correlations.
A setup of N elements is being fed into network by its sequence of individual optical components (r 22. ...; NN where our data N ranges from 6 15. We use an LSTT with 2048 hidden units and component embedding space with 64 dimensions. The component embedding technique similar to word embeddings (Mikolov et al. 2013).
EXPERIMENTS
3.1 DATASET
The dataset produced by MELVIN consists of 7,853,853 different setups of which 1,638,233 samples labeled positive. Each setup consists of sequence of optical elements, and the two target values yE and JSRV. We are interested in whether the trained model is able to extrapolate unseen SRVs. Therefore, we cluster the data by leading Schmidt rank n.. Figure shows the the number of positive and negative samples in the data set for each n..
3.2 WORKFLOW
All samples with n 2 are moved to a special extrapolation set consisting of only 1,754 setups (gray cell in Table I The remainder of the data, i.e. all samples with < 9, is then divided into training set and a conventional test set with 20 % the data drawn at random (ii).. This workflow is shown in Figure
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negative positive
10c
number of samples
10a
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leading Schmidt rank
Figure 2: Negative and positive samples in the data set as a function of the leading Schmidt rank n.
0.
2
3
4
5
6
7
8
9-12
0,
Table I: Cluster cross validation folds (0-8) and extrapolation set (9-12) characterized by leading Schmidt rank n. Samples with and samples with are combined and then split into two folds (0,1) at random.
The test set is used to estimate the conventional generalization error, while the extrapolation set is used to shed light on the ability of the learned model to perform on higher Schmidt rank numbers. If the model extrapolates successfully, we can hope find experimental setups that lead to new interesting quantum states.
Cluster cross validation (CCV) is evaluation method similar standard cross validation. Instead of grouping the folds iid, CCV groups them according to clustering method. Thus, CCV removes similarities between training and validation set and simulates situations which the withheld folds have not been obtained yet, thereby allowing to investigate the ability of the network discover these withheld setups. We use CCV with nine folds (white cells in Table TT. Seven of these folds correspond to the leading Schmidt ranks 2....,& 8. The samples with 1 (not entangled) and 0 (not even valid three-photon state) are negative by definition. These samples represent special cases of VE = 0 setups and it is not necessary to generalize to these cases without training them. Therefore, the 4,300,268 samples with n < 2 are divided into two folds at random such that the model will always some of these special samples while training.
3.3 RESULTS
Let us examine if the LSTM network has learned something about quantum physics. A good model will identify positive setups correctly while discarding many negative setups as possible. This behavior is reflected in the metrics true positive rate TPR = TP/(TP + FN) and true negative rate TNR TN/(TN + FP), with TP, TN, FP, the true positives, true negatives, false positives, false negatives, respectively. A metric that quantifies the success rate within the positive predictions is the hit rate (i.e. precision or positive predicted value), defined as HR = TP TP/((P + FP) (Simm et al. 2018).
For each withheld CCV fold n,, we characterize a setup be "interesting" when fulfills the following two criteria: (i) It is classified positive (ŪE > 7) with the classification threshold of the sigmoid output activation. (ii The SRV prediction ýSRV = (f,, m, R) is such that there exists a ySRv = (n, m. k)) T with [|ssy ISRvll2 < r. We call r the SRV radius. We denote samples which classified interesting (uninteresting) and indeed positive (negative) true positives
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entire dataset
< 9: in distribution
2 9: extrapolation set
20 % test set
80 training set.
generalization error
cluster cross validation
out-of-distribution capabilities
Figure 3: Workflow. We split the entire data by their leading Schmidt rank n. All samples with constitute the extrapolation set, which we use to explore the out-of of-distribution capabilities of our model. For the remaining samples (i.e. n 9) we make a random test split a ratio of 1/4. The test set is used to estimate the conventional generalization error of our model. We use the training set to perform cluster cross validation.
(negatives). And we denote samples which are classified as interesting (uninteresting) and indeed negative (positive) as false positives (false negatives).
We employ stochastic gradient descent for training the LSTM network with momentum 0.5 and batch size 128. We sample baihhebtchees in such a way that positive and negative samples appear equally often in training. For balanced SRV regression, the leading Schmidt rank vector number is misused as class label. The models were trained using early stopping after 40000 weight update steps for the entanglement classification network and 14000 update steps for the SRV regression network. Hyperparameter search was performed in advance on data set similar to the training set.
Figure shows the TNR, TPR, and rediscovery ratio for sigmoid threshold r = 3. The rediscovery ratio is defined as the number of distinct SRVs, for which at least 20% of the samples are rediscovered by our method, i.e. identified as interesting, divided by the number of distinct SRVs in the respective cluster. The TNR for fold 0,1 is 0.9996, and the hit rate HR on the extrapolation set 9-12 0.659. Error bars Figure 4 and later the text are 95 binomial proportion confidence intervals. Model performance depends heavily parameters and Figure 5 shows the "beyond distribution" results for variety of sigmoid thresholds and SRV radii.
0.5 and SRV radius
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Rediscovery Ratio
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Cluster
Figure 4: True negative rate (TNR), true positive rate (TPR), rediscovery ratio of the LSTM network using cluster cross validation for different folds 0-8. True negative rates are high for all validation folds. All metrics well on data beyond the training set distribution, covering only Schmidt rank numbers ()-8. Error bars represent 95 % binomial proportion confidence intervals.
good for the extrapolation set 9-12, demonstrating that the models perform
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Figure 5: True negative rate (scale starts 0.6), true positive rate, rediscovery ratio, and hit rate for the extrapolation set 9-12 for varying sigmoid threshold r and SRV radius r. For too restrictive parameter choices approach 0, such that no interesting new setups would be identified. For too loose choices (small т, large r), too few negative samples would be rejected, such that the advantage over random search becomes negligible. For large variety of 7 and r the models perform satisfyingly well, allowing decent compromise between TNR and TPR. This is reflected in large values for hit rate, which 0.736 on average over all depicted thresholds.
1 and 0.5) the TNR approaches while TPR and rediscovery ratio
Finally, we investigate the conventional in in-distribution generalization eтrоr using the test set (20 % of the data). Entanglement classification: The entanglement training BCE loss value 10.2. TNR and TPR are 0.9271 + 0.00024 and 0.9469 + 0.00041, respectively. The corresponding test сπrоr is 10.4. TNR and TPR 0.9261 + 0.00038 and 0.9427 0.00065, respectively. SRV regression: The SRV training loss value according to Equation (2) is 2.247, the accuracy with r r= 3 is 93.82 % and the mean distance between label and prediction is 1.3943. The SRV test error is 2.24, the accuracy with 3 is 0.938 and the mean distance between label and prediction is 1.40. These figures are consistent with a clean training procedure.
4 OUTLOOK
Our experiments demonstrate that an L.STM-based neural network can be trained model certain properties of complex quantum systems. Our approach is not limited to entanglement and Schmidt rank but may be generalized to employ other objective functions such as multiparticle transformations, interference and fidelity qualities, and
Another possible next step to expand our approach towards the goal of automated design of multiparti- cle righ--dmensional quantum experiments is the exploitation of generative models. Here, consider Generative Adversarial Networks (GANs) (Goodfellow et al. 2014) and beam search (Lowerre 1976) possible approaches.

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Generating sequences such text in adversarial settings has been done using ID CNNS (Gulrajani al. 2017) and LSTMs (Yu et al. [016) Fedus et al. 2018). The LSTM- based approaches employ ideas from reinforcement learning to alleviate the problem of propagating gradients through the softmax outputs of the network. Since our data is in structure similar to text, these approaches are setting.
directly applicable
For beam search, there exist two different ideas, namely a discriminative approach and generative approach. The discriminative approach incorporates the entire data (positive and negative samples). The models trained for this work can be used for the discriminative approach in that one constructs new sequences by maximizing the belief of the network that the outcome will be positive setup, For the generative upproach, the idea is to train model on the positive samples only to learn their distribution via next element prediction. On inference, beam search can be used approximate the most probable sequence given some initial condition (Bengio et al. (015). Another option to generate new sequences is to sample from the softmax distribution of the network output each sequence position as has been used for text generation models (Graves) 2013 Karpathy & Fei-Fei 2015).
In general, automated design procedures of experiments has much broader applications beyond quantum optical setups and can be of importance for many scientific disciplines other than physics.
5 CONCLUSION
We have shown that an LSTM-bbsed neural network can be trained successfully predict certain characteristics of high-dimensional multiparticle quantum states from the experimental setup without any explicit knowledge of quantum mechanics. The network performs well even unseen data beyond the training distribution, proving its extrapolation capabilities. This paves the way to automated design of complex quantum experiments using generative machine learning models.
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Reviews:
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The authors train 2 separate recurrent networks (LSTMs). The first one takes a sequence of optical elements and predicts a binary boolean label that answers the question of whether the resulting quantum state would be maximally entangled. The second one takes a sequence of optical elements and predicts the Schmid rank vector of the resulting state -- a linear algebraic quantity that is used in quantum information science. The authors explore whether the trained networks generalize to longer sequences of optical elements than trained on, and conduct other checks on the result.\n\nI really appreciate that the authors checked the applicability to sequences of elements longer than trained on. Another great point is the separation of the problem into the two LSTM, so that spurious correlations between the Schmid rank vector and entanglement cannot be used by the NN.\n\nWhile overall I find the problem interesting and the authors’ approach reasonable, I have a large number of questions / points of confusion that I will detail below.\n\n--------------- Point 1 ----------------\nIt is unclear what is actually fed into the optical table. My interpretation of the paper is that the input state to the optical table *must be fixed*, since it is not an input to the LSTM. This severely limits the applicability of the framework presented.\n\nI was unable to find what quantum state is actually being input into the sequence of optical elements whose effect you are trying to model. Considering that the properties of the quantum state /after/ the application of the set of N optical elements depend very strongly on the *input quantum state*, I find this omission significant. For an input state rho_0, each element L (which in this particular case is the input to the LSTM), will transform the state as rho -> L rho -> rho’. A series of N elements can be viewed as an application of a sequence of N operations L_1, …. , L_N. Therefore the resulting state is rho_final = L_N L_{N-1} …. L_1 rho_0. As it is clear here, the output state rho_final depends *both* on a) the sequence of applied operations / optical elements (L1,L2, …, LN) as modeled in this paper, and b) the actual input state rho_0. Since what is actually fed into the LSTM is only the sequence of optical elements  (L1,L2, …, LN), while the loss depends on the properties of rho_final, I believe rho_0 must have been assumed fixed and constant throughout the whole paper (i.e. that every data point in the train and test set actually had the same rho_0). If that is the case, the problem that the paper addresses is not /predicting the properties of a quantum state produced by a particular sequence of optical elements/, but rather /predicting the properties of a quantum state produced by a particular sequence of optical elements *given a fixed, constant input state, and not other input state*/. That is a much more restricted regime. I think authors should be clearer about the actual problem their paper is addressing.\n\n--------------- Point 2 ----------------\nLack of baselines. The problem setup and metrics used are quite specific (understandably) to the problem at hand. However, I do not have intuitions for what a “baseline” performance should be and therefore cannot validate whether the approach presented improves in it. I would like to see something equivalent to the 10% chance result on a 10-class classification task. To be more precise, while the binary classification of the maximally entangled nature of the quantum state is clear, the L_2 distance on the sorted 3-tuple of the Schmid rank vector is less so. The explicitly encoded sorted nature of the output 3-tuple (it is parameterized as such) makes it hard for me to make sense of the L_2 distance threshold r that the authors use (which is in fact at some sections r=3, i.e. quite large). I think that adding a baseline performance of some sort would greatly improve my ability to see how much better the LSTM approach performs.\n\n--------------- Point 3 ----------------\nThe datatype of the LSTM inputs is unclear to me. As I discussed in point 1), the resulting quantum properties of the output state after the application of N optical elements depends on the input state as well as the sequence of elements applied. I am not clear on how the authors encode the optical elements themselves -- i.e. what datatype are the inputs x1, … , xN to the LSTM. If they model them categorically as one-hot vectors, i.e. a 0 degree orientation polarization plate as (1,0,0,0,...0), a beam splitter as (0,1,0,0,....,0), a 90 degrees polarization place as (0,0,1,0,....,0) etc, they are restricting the applicability of the trained models to that particular set of optical elements. For example, if I wanted to know what adding a polarization plate with orientation alpha will do, I would not be able to do that. If it indeed is true that the inputs are drawn from a small number of possible optical elements that has to be specified prior to training, this would again limit the scope of applicability of the results presented.\n\n--------------- Point 4 ----------------\nStates as state vectors or density matrices? It wasn’t clear to me what the actual state that is being fed into the optical table is. This might be a minor point, but Equation 1 suggests it is in fact a state vector, while I thought quantum optical experiments work with density matrices. This is important for the dimensionality considerations involved, since e.g. for a set of M qubits, the state vector would have 2^M elements (-constraints), while the density matrix would have 2^(2M) elements (-constraints). Solving the latter problem would therefore be more impressive than the former.\n\n--------------- Point 5 ----------------\nAre you always working with 3 subsystems/photons? From reading the paper I didn’t understand whether you were restricting your setup to always work in the 3 photon regime that you mentioned in Equation 1. What is the range of the values that can appear in the Schmid rank vector? If they are small, then setting the threshold r=3 would be very generous and making it easy for the LSTM. \n\n--------------- Conclusion ----------------\nWhile I like the problem and the attempted solution, the results presented (as I understood them) are more restricted and therefore weaker than I originally expected. I believe the results are promising, but more clarity and more work on generalizing beyond the restricted regime presented would greatly improve the paper. \n", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "rJe7c2NjKB", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposed to use machine learning models to predict certain properties of complex quantum systems. In quantum physics experiments, one need to randomly search millions of experimental setups to search for interesting experiments. This paper shown that machine learning models can provide significant improvement over random search.\n\nIn general, this paper is easy to follow and clearly presents the main idea and verifies the effectiveness of the proposed method. However, I still have some concerns about this paper. This paper only used the machine learning method, i.e. LSTM model and binary cross entropy loss function for solving quantum physical tasks, which provides less knowledge to the machine learning or representation learning community. In my opinion, this paper should be submitted to a quantum physics journal or conference, rather than a machine learning conference.", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "B1gPU8eTtS", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper investigates how LSTM networks learn quantum optical experimental setup and predict characteristics of resulting quantum states. While the content of this paper may be of interest to quantum computing specialists (I am unable to actually judge that), my impression is that it is out of scope for ICLR. Not by default, but by the style of writing and lack of effort to make the studied problem accessible to non-expert audience. After a general introduction the paper starts immediately with a physics notation for wavefunctions (ok this one could argue is still generic enough for many scientists to understand), but right after is talks of orbital angular momentum and Schmidt rank vector. I do have a background in physics, but not speciality in quantum computing, and at the end of section 2.1 I am already lost. Subsequent section are clearer but since I did not understand how is the data really represented it is impossible for me to gain insight on what is actually being done. The same is true with the evaluation as I do not know what I should be comparing to and what the standard methods give. The paper could have been a nice opening towards new applications for ICLR but it would have to be written in a much more pedestrian manner. In the present form I argue for rejection. ", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_experiments": "N/A", "review_assessment:_checking_correctness_of_derivations_and_theory": "N/A"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper predicts properties of quantum states through RNNs.  The idea is nice, but the results are very limited and require more work.  It seems to be more suited for a conference focussing on quantum ML---even when the authors have an ML background.

All reviewers agree on a rejection, and their arguments are solid.  The authors offered no rebuttal.
Decision: Reject