Paper: Topological data analysis on noisy quantum computers (ICLR-2024)

Reviewer comment (Writing):
The reviewer has a basic understanding of quantum computing, but not familiar with TDA. The paper does not provide sufficient background and related work on quantum computing, TDA and QTDA. It assumes that the reader is familiar with these topics and does not cite relevant literature or explain the key concepts and notations.

Author response:
We thank the reviewer for this point. Our work brings together three distinct fields (algebraic topology/homology, quantum computing, and machine learning), and includes a new algorithm, rigorous theoretical results, experimental results, and new applications. Unfortunately, it was difficult to fit all the different details within 9 pages. Therefore, we tried to include detailed descriptions for each of these in the Appendix (of about 20 additional pages), where we provide pictorial illustrations, flow charts, circuit diagrams, detailed equations, and descriptions to supplement the main paper.
We will further revise the main paper to address this comment and point to the relevant portions of the appendix for more details.

Reviewer comment (Experiments):
The paper does not provide any empirical evidence of the quantum advantage or noise-resiliency of the NISQ-TDA algorithm. It only shows some preliminary results on small datasets and noisy simulations, without any statistical analysis or comparison with baselines.

Author response:
As we discuss in the paper, in order to demonstrate real quantum advantage, we will require quantum computers (QCs) with at least a few hundred qubits that are very well connected (or have extremely efficient "swap" operations) and have very high multi-qubit gate fidelity. Unfortunately, such devices are likely few years away, and we will not be able to demonstrate true quantum advantage for NISQ-TDA using existing quantum computers. 
Using current quantum computers, we can compute Betti numbers of small datasets, as demonstrated in our paper, and for such datasets, exact Betti numbers can be easily known. To the best of our knowledge, the baseline QTDA algorithm cannot be implemented on current hardware without fault tolerance, and there are no classical baselines to compute high-order Betti numbers (the problem is NP hard). 
We wish to note that real quantum advantage has not been demonstrated on any practically useful problems so far. We strongly believe our work makes an important contribution in this direction, by presenting a novel quantum algorithm for a useful problem (that is known to be classical hard), which does not suffer from data loading problems, requires short depth and with potential speedup.   We present rigorous error guarantees for the algorithm and an actual end-to-end implementation of the algorithm on a real hardware. We will revise the discussion in the paper to make all these points more clearly.

Reviewer comment (Theory):
For real quantum advantage, the input data must satisfy several conditions listed at the end of Section 3.

Author response:
This is a good point. Indeed, in order to achieve real quantum advantage (super-polynomial to exponential speedup), we require simplicial complexes with exponentially many holes, and it is not clear whether such complexes exist in real-world data. However, Schmidhuber and Lloyd (2022) recently showed that the original QTDA algorithm achieves a quadratic (to 4th power) speedup over classical algorithms. Since our algorithm improves the runtime of QTDA, we should expect a further speedup over classical algorithms for real-world instances. We will revise the paper to clarify and emphasize these points.

Reviewer comment (Novelty):
The advantage for solving the problem of deciding whether a simplicial complex has exponentially many holes seems less clear and perhaps less practical.

Author response:
This is a good point. Indeed, in order to achieve real quantum advantage (super-polynomial to exponential speedup), we require simplicial complexes with exponentially many holes, and it is not clear whether such complexes exist in real-world data. However, Schmidhuber and Lloyd (2022) recently showed that the original QTDA algorithm achieves a quadratic (to 4th power) speedup over classical algorithms. Since our algorithm improves the runtime of QTDA, we should expect a further speedup over classical algorithms for real-world instances. We will revise the paper to clarify and emphasize these points.

Reviewer comment (Experiments):
It would be great to know if the algorithm still provides speedup for real-world instances.

Author response:
This is a good point. Indeed, in order to achieve real quantum advantage (super-polynomial to exponential speedup), we require simplicial complexes with exponentially many holes, and it is not clear whether such complexes exist in real-world data. However, Schmidhuber and Lloyd (2022) recently showed that the original QTDA algorithm achieves a quadratic (to 4th power) speedup over classical algorithms. Since our algorithm improves the runtime of QTDA, we should expect a further speedup over classical algorithms for real-world instances. We will revise the paper to clarify and emphasize these points.

Reviewer comment (Presentation):
I am a bit confused by Figure 1B, C, and D. What exactly are the bars representing? I assume it shows the probabilities of obtaining results corresponding to vertices, edges, triangles, etc., but is it the case that several are omitted for the cube and square?

Author response:
This is correct. We have plotted the fractional counts (probabilities) measured after applying the Laplacian circuit once for the datasets. We have plotted the probabilities for only those states, which have significant enough counts (more than 1% of the total counts). As expected, most of the states will have negligible probabilities/counts, particularly for larger $n$.  
We will clarify exactly what the bars are representing in Figure 1.

Reviewer comment (Theory):
I wonder if there is any intuitive, high-level reasoning for what kind of problem structure is being leveraged here that enables quantum speedup.

Author response:
At a high-level, we are taking advantage of the following fact: A simplicial complex with $n$ points can have up to $2^n$ simplices of all possible $n$ orders, each of these simplices of any given order $k$ can be represented as basis vectors/states of a certain Hilbert space $H_k$, and the union of these is simply the $n$-qubit Hilbert space. Thus, we can represent any of the $2^n$ simplices as an $n$-qubit state. Next, we take advantage of quantum parallel search (since a uniform superposition state represents existence of all simplices), and that a $k$-simplex is fully defined by the $k$ edges (small input). Therefore, entangling qubits pairwise suffices to represent the simplices present in the complex. 
We appreciate this point and will add to the paper a discussion of such intuitive, high-level reasoning (also see the Appendix for a brief discussion on this).

Reviewer comment (Writing):
I feel the technical discussion in Section 3 (especially the projection to a simplicial order) is a bit hard to follow. Is it possible to give some concrete examples (e.g., in terms of explicit circuit & Pauli operators)?

Author response:
We thank the reviewer for this valuable suggestion. We have addressed your suggestion in the Appendix of the revised paper by adding two example circuits for the two projection operations (projection onto the complex $P_{\Gamma}$ and projection onto the order $P_k$), respectively; please see Figures 6 and 7. For the experiments on the Quantinuum hardware, for the (2, 4, 8) vertices experiments, the number of measurements were $10^3$, $10^4$, and $2\times 10^4$, respectively.

Reviewer comment (Reproducibility):
It would be better to discuss more on the actual resources (gate counts, # of measurements, etc.) spent on the Quantinuum hardware. See the following "Questions" section.

Author response:
We thank the reviewer for this valuable suggestion. We have addressed your suggestion in the Appendix of the revised paper by adding two example circuits for the two projection operations (projection onto the complex $P_{\Gamma}$ and projection onto the order $P_k$), respectively; please see Figures 6 and 7. For the experiments on the Quantinuum hardware, for the (2, 4, 8) vertices experiments, the number of measurements were $10^3$, $10^4$, and $2\times 10^4$, respectively.

Reviewer comment (Theory):
The circuit depth of NISQ-TDA heavily depends on the Chebyshev truncation number. What is the exact dependence of the Chebyshev truncation number in terms of the input data set (or the projection operator)? Does the resource analysis (Fig 1A) treat the Chebyshev truncation number as a parameter depending on the number of vertices, or is it fixed as a constant?

Author response:
The degree of the truncated Chebyshev polynomial $m$ will depend on the spectral gap as $1/\sqrt{\delta}$; see Theorem 1. In the Appendix, we present many simplicial complex examples and discuss the behavior of their spectral gap with respect to the number of vertices. For certain simplicial complexes, the spectral gap is constant, while for others the gap depends inversely on the number of vertices.
We will improve the discussion of these points in the main paper and point to the appendix for more details.

In Fig 1A, the plots are for fixed degrees $m=1$ and $m=3$, respectively, which we will clarify.

Reviewer comment (Experiments):
In Fig 1B-D, how many shots were used on the hardware to estimate the probability?

Author response:
For the (2, 4, 8) vertices experiments, the number of shots used were $10^3$, $10^4$, and $2\times 10^4$, respectively, which we will clarify in the revised paper.