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Documents authored by Cornelissen, Arjan

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RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2026.72
Quantum Algorithms for Path and Cycle Containment Problems

Authors: Arjan Cornelissen, Amin Shiraz Gilani, and Subhasree Patro

Published in: LIPIcs, Volume 392, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2026)

Abstract
The quantum query complexity of subgraph-containment problems, which ask whether a given subgraph H is present in an input graph G, has been the subject of considerable study. This interest stems not only from the natural and well-motivated formulation of these problems, but also from a flurry of novel quantum algorithmic techniques that were developed specifically to solve them. Notably, even for relatively simple subgraphs, such as paths and cycles, a complete understanding of their query complexities remains elusive. In this work, we consider several variants of path- and cycle-containment problems in the adjacency matrix model, where we search for paths or cycles of constant length k ∈ O(1). We compare the settings where the graphs are directed or undirected, where the goal is to detect or find the existence of a path/cycle, and where the path/cycle we're looking for has length exactly k, or at most k. We also consider several promise versions of these problems, where we know beforehand that the input graph has a certain structure. We characterize the relative difficulty of these variants of the path- and cycle-containment problems, by relating them to one another using randomized reductions, and grouping them into several equivalence classes. When we restrict our attention to path-containment problems, this implies a dichotomy result. Some of the path-containment problems can be solved using a linear number of queries, and all the others are equivalent to one another (and additionally to several cycle-containment problems as well) under randomized reductions and up to constant multiplicative overhead. For the latter equivalence class, we prove a novel quantum-walk-based algorithm that achieves query complexity Õ(n^{3/2-α_k}), where α_k ∈ Θ(c^{-k}) and c = √{3+√17}/2 ≈ 1.33, beating the previous best upper bound O(n^{3/2}) on its query complexity. We also provide a conditional lower bound based on the graph-collision problem, which implies that this equivalence class does not admit linear-query quantum algorithms unless graph collision admits an O(√n) query algorithm.
Cite as

Arjan Cornelissen, Amin Shiraz Gilani, and Subhasree Patro. Quantum Algorithms for Path and Cycle Containment Problems. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 392, pp. 72:1-72:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{cornelissen_et_al:LIPIcs.APPROX/RANDOM.2026.72,
  author =	{Cornelissen, Arjan and Gilani, Amin Shiraz and Patro, Subhasree},
  title =	{{Quantum Algorithms for Path and Cycle Containment Problems}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2026)},
  pages =	{72:1--72:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-449-9},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{392},
  editor =	{Singh, Mohit and Gur, Tom},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2026.72},
  URN =		{urn:nbn:de:0030-drops-277892},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2026.72},
  annote =	{Keywords: Quantum algorithms, query complexity, graph problems, fine-grained reductions}
}
Document
DOI: 10.4230/LIPIcs.TQC.2026.3
Quantum Search with Generalized Wildcards

Authors: Arjan Cornelissen, Nikhil S. Mande, Subhasree Patro, Nithish Raja, and Swagato Sanyal

Published in: LIPIcs, Volume 389, 21st Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2026)

Abstract
In the "search with wildcards" problem [Ambainis, Montanaro, Quantum Inf. Comput.'14], one’s goal is to learn an unknown bit-string x ∈ {-1,1}ⁿ. An algorithm may, at unit cost, test equality of any subset of the hidden string with a string of its choice. Ambainis and Montanaro showed a quantum algorithm of cost O(√n log n) and a near-matching lower bound of Ω(√n). Belovs [Comput. Comp.'15] subsequently showed a tight O(√n) upper bound. We consider a natural generalization of this problem, parametrized by a subset Q ⊆ 2^{[n]}, where an algorithm may test whether x_S = b for an arbitrary S ∈ Q and b ∈ {-1,1}^S of its choice, at unit cost. We show the following: - For all k ∈ [n], when Q is the collection of all sets of size at most k, the quantum query complexity is Θ(n/√k). In particular when k = n, this corresponds to the standard search with wildcards setting. This recovers and generalizes the tight characterization of Belovs, and Ambainis and Montanaro, using completely different techniques. - When Q is the collection of contiguous blocks, the quantum query complexity is Θ̃(n). - When Q is the collection of prefixes, the quantum query complexity is Θ(n). All of these results are derived using a framework that we develop. We apply a symmetry reduction to the primal version of the negative-weight adversary bound, and show that the quantum query complexity of learning x is characterized, up to a constant factor, by a particular optimization program, which can be succinctly described as follows: `maximize over all odd functions f : {-1,1}ⁿ → ℝ the ratio of the maximum value of f to the maximum (over T ∈ Q) standard deviation of f on a subcube whose free variables are exactly T.' To the best of our knowledge, ours is the first work to use the primal version of the negative-weight adversary bound (which is a maximization program typically used to show lower bounds) to show new quantum query upper bounds without explicitly resorting to SDP duality.
Cite as

Arjan Cornelissen, Nikhil S. Mande, Subhasree Patro, Nithish Raja, and Swagato Sanyal. Quantum Search with Generalized Wildcards. In 21st Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 389, pp. 3:1-3:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{cornelissen_et_al:LIPIcs.TQC.2026.3,
  author =	{Cornelissen, Arjan and Mande, Nikhil S. and Patro, Subhasree and Raja, Nithish and Sanyal, Swagato},
  title =	{{Quantum Search with Generalized Wildcards}},
  booktitle =	{21st Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2026)},
  pages =	{3:1--3:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-439-0},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{389},
  editor =	{Arnon, Rotem and Harrow, Aram W.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TQC.2026.3},
  URN =		{urn:nbn:de:0030-drops-273007},
  doi =		{10.4230/LIPIcs.TQC.2026.3},
  annote =	{Keywords: quantum algorithms, quantum query complexity, adversary bound, symmetry reduction, substring queries}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2026.77
Randomized and Quantum Approximate Matrix Multiplication

Authors: Simon Apers, Arjan Cornelissen, and Samson Wang

Published in: LIPIcs, Volume 386, 51st International Symposium on Mathematical Foundations of Computer Science (MFCS 2026)

Abstract
The complexity of matrix multiplication is a central topic in computer science. While the focus has traditionally been on exact algorithms, a long line of literature also considers randomized algorithms, which return an approximate solution in faster time. In this work, we adopt a unifying perspective that frames these randomized algorithms in terms of mean estimation. Using it, we first give refined analyses of classical algorithms based on random walks by Cohen-Lewis (`99), and based on sketching by Sarlós (`06) and Drineas-Kannan-Mahoney (`06). We then propose an improvement on Cohen-Lewis that yields a single classical algorithm that is faster than all the other approaches, if we assume no use of (exact) fast matrix multiplication as a subroutine. Second, we demonstrate a quantum speedup on top of these algorithms by using the recent quantum multivariate mean estimation algorithm by Cornelissen-Hamoudi-Jerbi (`22).
Cite as

Simon Apers, Arjan Cornelissen, and Samson Wang. Randomized and Quantum Approximate Matrix Multiplication. In 51st International Symposium on Mathematical Foundations of Computer Science (MFCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 386, pp. 77:1-77:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{apers_et_al:LIPIcs.MFCS.2026.77,
  author =	{Apers, Simon and Cornelissen, Arjan and Wang, Samson},
  title =	{{Randomized and Quantum Approximate Matrix Multiplication}},
  booktitle =	{51st International Symposium on Mathematical Foundations of Computer Science (MFCS 2026)},
  pages =	{77:1--77:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-442-0},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{386},
  editor =	{Kouck\'{y}, Michal and Petrișan, Daniela},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2026.77},
  URN =		{urn:nbn:de:0030-drops-274591},
  doi =		{10.4230/LIPIcs.MFCS.2026.77},
  annote =	{Keywords: randomized algorithms, quantum algorithms, streaming, approximate matrix multiplication, mean estimation}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.61
How to Compute the Volume in Low Dimension?

Authors: Arjan Cornelissen, Simon Apers, and Sander Gribling

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
Estimating the volume of a convex body is a canonical problem in theoretical computer science. Its study has led to major advances in randomized algorithms, Markov chain theory, and computational geometry. In particular, determining the query complexity of volume estimation to a membership oracle has been a longstanding open question. Most of the previous work focuses on the high-dimensional limit. In this work, we tightly characterize the deterministic, randomized and quantum query complexity of this problem in the high-precision limit, i.e., when the dimension is constant.
Cite as

Arjan Cornelissen, Simon Apers, and Sander Gribling. How to Compute the Volume in Low Dimension?. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 61:1-61:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{cornelissen_et_al:LIPIcs.ICALP.2025.61,
  author =	{Cornelissen, Arjan and Apers, Simon and Gribling, Sander},
  title =	{{How to Compute the Volume in Low Dimension?}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{61:1--61:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.61},
  URN =		{urn:nbn:de:0030-drops-234381},
  doi =		{10.4230/LIPIcs.ICALP.2025.61},
  annote =	{Keywords: Query complexity, computational geometry, quantum computing, volume estimation, high-precision limit}
}
Document
DOI: 10.4230/LIPIcs.FSTTCS.2024.19
Quantum Sabotage Complexity

Authors: Arjan Cornelissen, Nikhil S. Mande, and Subhasree Patro

Published in: LIPIcs, Volume 323, 44th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2024)

Abstract
Given a Boolean function f : {0,1}ⁿ → {0,1}, the goal in the usual query model is to compute f on an unknown input x ∈ {0,1}ⁿ while minimizing the number of queries to x. One can also consider a "distinguishing" problem denoted by f_sab: given an input x ∈ f^{-1}(0) and an input y ∈ f^{-1}(1), either all differing bits are replaced by a *, or all differing bits are replaced by †, and an algorithm’s goal is to identify which of these is the case while minimizing the number of queries. Ben-David and Kothari [ToC'18] introduced the notion of randomized sabotage complexity of a Boolean function to be the zero-error randomized query complexity of f_sab. A natural follow-up question is to understand the 𝖰(f_sab), the quantum query complexity of f_sab. In this paper, we initiate a systematic study of this. The following are our main results for all Boolean functions f : {0,1}ⁿ → {0,1}. - If we have additional query access to x and y, then 𝖰(f_sab) = O(min{𝖰(f),√n}). - If an algorithm is also required to output a differing index of a 0-input and a 1-input, then 𝖰(f_sab) = O(min{𝖰(f)^{1.5}, √n}). - 𝖰(f_sab) = Ω(√{fbs(f)}), where fbs(f) denotes the fractional block sensitivity of f. By known results, along with the results in the previous bullets, this implies that 𝖰(f_sab) is polynomially related to 𝖰(f). - The bound above is easily seen to be tight for standard functions such as And, Or, Majority and Parity. We show that when f is the Indexing function, 𝖰(f_sab) = Θ(fbs(f)), ruling out the possibility that 𝖰(f_sab) = Θ(√{fbs(f)}) for all f.
Cite as

Arjan Cornelissen, Nikhil S. Mande, and Subhasree Patro. Quantum Sabotage Complexity. In 44th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 323, pp. 19:1-19:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{cornelissen_et_al:LIPIcs.FSTTCS.2024.19,
  author =	{Cornelissen, Arjan and Mande, Nikhil S. and Patro, Subhasree},
  title =	{{Quantum Sabotage Complexity}},
  booktitle =	{44th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2024)},
  pages =	{19:1--19:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-355-3},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{323},
  editor =	{Barman, Siddharth and Lasota, S{\l}awomir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2024.19},
  URN =		{urn:nbn:de:0030-drops-222082},
  doi =		{10.4230/LIPIcs.FSTTCS.2024.19},
  annote =	{Keywords: Sabotage complexity, quantum query complexity, Boolean functions, fractional block sensitivity}
}
Document
DOI: 10.4230/LIPIcs.TQC.2023.13
Quantum Policy Gradient Algorithms

Authors: Sofiene Jerbi, Arjan Cornelissen, Maris Ozols, and Vedran Dunjko

Published in: LIPIcs, Volume 266, 18th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2023)

Abstract
Understanding the power and limitations of quantum access to data in machine learning tasks is primordial to assess the potential of quantum computing in artificial intelligence. Previous works have already shown that speed-ups in learning are possible when given quantum access to reinforcement learning environments. Yet, the applicability of quantum algorithms in this setting remains very limited, notably in environments with large state and action spaces. In this work, we design quantum algorithms to train state-of-the-art reinforcement learning policies by exploiting quantum interactions with an environment. However, these algorithms only offer full quadratic speed-ups in sample complexity over their classical analogs when the trained policies satisfy some regularity conditions. Interestingly, we find that reinforcement learning policies derived from parametrized quantum circuits are well-behaved with respect to these conditions, which showcases the benefit of a fully-quantum reinforcement learning framework.
Cite as

Sofiene Jerbi, Arjan Cornelissen, Maris Ozols, and Vedran Dunjko. Quantum Policy Gradient Algorithms. In 18th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 266, pp. 13:1-13:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{jerbi_et_al:LIPIcs.TQC.2023.13,
  author =	{Jerbi, Sofiene and Cornelissen, Arjan and Ozols, Maris and Dunjko, Vedran},
  title =	{{Quantum Policy Gradient Algorithms}},
  booktitle =	{18th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2023)},
  pages =	{13:1--13:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-283-9},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{266},
  editor =	{Fawzi, Omar and Walter, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TQC.2023.13},
  URN =		{urn:nbn:de:0030-drops-183230},
  doi =		{10.4230/LIPIcs.TQC.2023.13},
  annote =	{Keywords: quantum reinforcement learning, policy gradient methods, parametrized quantum circuits}
}
Document
DOI: 10.4230/LIPIcs.FSTTCS.2022.15
Improved Quantum Query Upper Bounds Based on Classical Decision Trees

Authors: Arjan Cornelissen, Nikhil S. Mande, and Subhasree Patro

Published in: LIPIcs, Volume 250, 42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2022)

Abstract
We consider the following question in query complexity: Given a classical query algorithm in the form of a decision tree, when does there exist a quantum query algorithm with a speed-up (i.e., that makes fewer queries) over the classical one? We provide a general construction based on the structure of the underlying decision tree, and prove that this can give us an up-to-quadratic quantum speed-up in the number of queries. In particular, our results give a bounded-error quantum query algorithm of cost O(√s) to compute a Boolean function (more generally, a relation) that can be computed by a classical (even randomized) decision tree of size s. This recovers an O(√n) algorithm for the Search problem, for example. Lin and Lin [Theory of Computing'16] and Beigi and Taghavi [Quantum'20] showed results of a similar flavor. Their upper bounds are in terms of a quantity which we call the "guessing complexity" of a decision tree. We identify that the guessing complexity of a decision tree equals its rank, a notion introduced by Ehrenfeucht and Haussler [Information and Computation'89] in the context of learning theory. This answers a question posed by Lin and Lin, who asked whether the guessing complexity of a decision tree is related to any measure studied in classical complexity theory. We also show a polynomial separation between rank and its natural randomized analog for the complete binary AND-OR tree. Beigi and Taghavi constructed span programs and dual adversary solutions for Boolean functions given classical decision trees computing them and an assignment of non-negative weights to edges of the tree. We explore the effect of changing these weights on the resulting span program complexity and objective value of the dual adversary bound, and capture the best possible weighting scheme by an optimization program. We exhibit a solution to this program and argue its optimality from first principles. We also exhibit decision trees for which our bounds are strictly stronger than those of Lin and Lin, and Beigi and Taghavi. This answers a question of Beigi and Taghavi, who asked whether different weighting schemes in their construction could yield better upper bounds.
Cite as

Arjan Cornelissen, Nikhil S. Mande, and Subhasree Patro. Improved Quantum Query Upper Bounds Based on Classical Decision Trees. In 42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 250, pp. 15:1-15:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{cornelissen_et_al:LIPIcs.FSTTCS.2022.15,
  author =	{Cornelissen, Arjan and Mande, Nikhil S. and Patro, Subhasree},
  title =	{{Improved Quantum Query Upper Bounds Based on Classical Decision Trees}},
  booktitle =	{42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2022)},
  pages =	{15:1--15:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-261-7},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{250},
  editor =	{Dawar, Anuj and Guruswami, Venkatesan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2022.15},
  URN =		{urn:nbn:de:0030-drops-174071},
  doi =		{10.4230/LIPIcs.FSTTCS.2022.15},
  annote =	{Keywords: Quantum Query Complexity, Decision Trees, Decision Tree Rank}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2020.26
Span Programs and Quantum Time Complexity

Authors: Arjan Cornelissen, Stacey Jeffery, Maris Ozols, and Alvaro Piedrafita

Published in: LIPIcs, Volume 170, 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020)

Abstract
Span programs are an important model of quantum computation due to their correspondence with quantum query and space complexity. While the query complexity of quantum algorithms obtained from span programs is well-understood, it is not generally clear how to implement certain query-independent operations in a time-efficient manner. In this work, we prove an analogous connection for quantum time complexity. In particular, we show how to convert a sufficiently-structured quantum algorithm for f with time complexity T into a span program for f such that it compiles back into a quantum algorithm for f with time complexity 𝒪̃(T). This shows that for span programs derived from algorithms with a time-efficient implementation, we can preserve the time efficiency when implementing the span program, which means that span programs capture time, query and space complexities and are a complete model of quantum algorithms. One practical advantage of being able to convert quantum algorithms to span programs in a way that preserves time complexity is that span programs compose very nicely. We demonstrate this by improving Ambainis’s variable-time quantum search result using our construction through a span program composition for the OR function.
Cite as

Arjan Cornelissen, Stacey Jeffery, Maris Ozols, and Alvaro Piedrafita. Span Programs and Quantum Time Complexity. In 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 170, pp. 26:1-26:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{cornelissen_et_al:LIPIcs.MFCS.2020.26,
  author =	{Cornelissen, Arjan and Jeffery, Stacey and Ozols, Maris and Piedrafita, Alvaro},
  title =	{{Span Programs and Quantum Time Complexity}},
  booktitle =	{45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020)},
  pages =	{26:1--26:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-159-7},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{170},
  editor =	{Esparza, Javier and Kr\'{a}l', Daniel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2020.26},
  URN =		{urn:nbn:de:0030-drops-126947},
  doi =		{10.4230/LIPIcs.MFCS.2020.26},
  annote =	{Keywords: quantum query algorithms, span programs, variable-time quantum search}
}

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