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arXiv:2311.07372 · Quantum

Multidimensional Electrical Networks and their Application to Exponential Speedups for Graph Problems

Jianqiang Li, Sebastian Zur

Published May 6, 2025• 2 citations
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Abstract

Recently, Apers and Piddock [TQC '23] strengthened the connection between quantum walks and electrical networks via Kirchhoff's Law and Ohm's Law. In this work, we develop a new multidimensional electrical network by defining Alternative Kirchhoff's Law and Alternative Ohm's Law based on the multidimensional quantum walk framework by Jeffery and Zur [STOC '23]. In analogy to the connection between the incidence matrix of a graph and Kirchhoff's Law and Ohm's Law in an electrical network, we rebuild the connection between the alternative incidence matrix and Alternative Kirchhoff's Law and Alternative Ohm's Law. This new framework enables generating an alternative electrical flow over the edges on graphs, which has the potential to be applied to a broader range of graph problems, benefiting both quantum and classical algorithm design. We first use this framework to generate quantum alternative electrical flow states and use it to find a marked vertex in one-dimensional random hierarchical graphs as defined by Balasubramanian, Li, and Harrow [arXiv '23]. In this work, they generalised the exponential quantum-classical separation of the welded tree graph by Childs, Cleve, Deotto, Farhi, Gutmann, and Spielman [STOC '03] to random hierarchical graphs. Our result partially recovers their results with an arguably simpler analysis. Furthermore, this framework also allows us to demonstrate an exponential quantum speedup for the pathfinding problem in a type of regular graph, which we name the welded tree circuit graph. The exponential quantum advantage is obtained by efficiently generating quantum alternative electrical flow states and then sampling from them to find an s-t path in the welded tree circuit graph. By comparison, Li [arXiv '23] constructed a non-regular graph based on welded trees and used the degree information to achieve a similar speedup.

DOI
10.22331/q-2025-05-06-1733

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Recently, Apers and Piddock [TQC '23] strengthened the connection between quantum walks and electrical networks via Kirchhoff's Law and Ohm's Law. In this work, we develop a new multidimensional electrical network by defining Alternative Kirchhoff's Law and Alternative Ohm's Law based on the multidimensional quantum walk framework by Jeffery and Zur [STOC '23]. In analogy to the connection between the incidence matrix of a graph and Kirchhoff's Law and Ohm's Law in an electrical network, we rebuild the connection between the alternative incidence matrix and Alternative Kirchhoff's Law and Alternative Ohm's Law. This new framework enables generating an alternative electrical flow over the edges on graphs, which has the potential to be applied to a broader range of graph problems, benefiting both quantum and classical algorithm design. We first use this framework to generate quantum alternative electrical flow states and use it to find a marked vertex in one-dimensional random hierarchical graphs as defined by Balasubramanian, Li, and Harrow [arXiv '23]. In this work, they generalised the exponential quantum-classical separation of the welded tree graph by Childs, Cleve, Deotto, Farhi, Gutmann, and Spielman [STOC '03] to random hierarchical graphs. Our result partially recovers their results with an arguably simpler analysis. Furthermore, this framework also allows us to demonstrate an exponential quantum speedup for the pathfinding problem in a type of regular graph, which we name the welded tree circuit graph. The exponential quantum advantage is obtained by efficiently generating quantum alternative electrical flow states and then sampling from them to find an s-t path in the welded tree circuit graph. By comparison, Li [arXiv '23] constructed a non-regular graph based on welded trees and used the degree information to achieve a similar speedup.
Up to now, relatively few exponential quantum speed-ups have been achieved. Out of them, the welded tree problem (Childs, Cleve, Deotto, Farhi, Gutmann, and Spielman'2003) is one of the unusual examples, as the exponential speed-up is attained by a quantum walk. In this paper, we give a very short proof of the optimal linear hitting time for this problem by a discrete-time quantum walk, which is based on a simple modification of the electric quantum walk framework. The same technique can be applied to other 1-dimensional hierarchical graphs, yielding results similar to (Balasubramanian, Li, and Harrow'2023).
Abstract There are few known exponential speedups for quantum algorithms and these tend to fall into even fewer families. One speedup that has mostly resisted generalization is the use of quantum walks to traverse the welded-tree graph, due to Childs, Cleve, Deotto, Farhi, Gutmann, and Spielman. We show how to generalize this to a large class of hierarchical graphs in which the vertices are grouped into “supervertices” which are arranged according to a d-dimensional lattice. Supervertices can have different sizes, and edges between supervertices correspond to random connections between their constituent vertices. The hitting times of quantum walks on these graphs are related to the localization properties of zero modes in certain disordered tight binding Hamiltonians. The speedups range from superpolynomial to exponential, depending on the underlying dimension and the random graph model. We also provide concrete realizations of these hierarchical graphs, and introduce a general method for constructing graphs with efficient quantum traversal times using graph sparsification.
We investigate how much quantum distributed algorithms can outperform classical distributed algorithms with respect to the message complexity (the overall amount of communication used by the algorithm). Recently, Dufoulon, Magniez and Pandurangan (PODC 2025) have shown a polynomial quantum advantage for several tasks such as leader election and agreement. In this paper, we show an exponential quantum advantage for a fundamental task: routing information between two specified nodes of a network. We prove that for the family of “welded trees” introduced in the seminal work by Childs, Cleve, Deotto, Farhi, Gutmann and Spielman (STOC 2003), there exists a quantum distributed algorithm that transfers messages from the entrance of the graph to the exit with message complexity exponentially smaller than any classical algorithm. Our quantum algorithm is based on the recent “succinct” implementation of quantum walks over the welded trees by Li, Li and Luo (SODA 2024). Our classical lower bound is obtained by “lifting” the lower bound from Childs, Cleve, Deotto, Farhi, Gutmann and Spielman (STOC 2003) from query complexity to message complexity. A full version of this paper can be found on arXiv [21].
Welded Trees are a class of graphs designed to exhibit exponential algorithmic speedup for certain search problems via quantum walks, outperforming any classical algorithm. A Welded Tree of depth d consists of two complete binary trees, each of depth d, whose leaves are interconnected by an additional cycle or permutation alternating between the two trees. Access to this graph is provided only through queries to an oracle, which, given a vertex, returns its neighbors. In 2021, Aaronson suggested the FINDING-PATH-TO-EXIT problem, which consists of finding a path to the root of the opposite tree given the root of one tree. This can be seen as an extension of the FINDING-EXIT problem, where the task is simply to find the root of the opposite tree, for which Childs et al. demonstrated an exponential speed-up in 2003. We provide a quantum algorithm for solving the Finding Path To Exit problem that performs Õ(2d/3) queries and also formalize the folklore lower bound that any classical algorithm requires at least Ω̃(2d/2 ) queries. This is the first proof of a polynomial speed-up for this problem over any classical algorithm. In addition, we present and analyze several other algorithms, focusing on the trade-offs between queries and classical or quantum space.
Quantum computing offers the potential to solve problems more efficiently than classical methods, yet understanding its capabilities requires new conceptual frameworks. This dissertation explores quantum algorithms with a focus on maintaining connections to classical intuition, clarifying both their strengths and limitations. In Part I, we explore the capabilities of a class of quantum algorithms known as quantum walks. These quantum walks, the quantum analogs of classical random walks, serve as powerful yet accessible tools for solving a variety of computational problems. Our key contribution is the development of a novel way to construct quantum walks, resulting in multidimensional quantum walks. By applying these walks to the kkk-distinctness problem, we achieve a time-efficient algorithm matching the best-known query upper bound up to polylogarithmic factors. Additionally, applying them to the welded tree problem results in exponential speedups, marking the first instance of a discrete quantum walk to do so. Furthermore, we extend the well-known link between quantum walks and electrical networks to the multidimensional quantum walks. In Part II, we turn to the limitations of quantum algorithms by examining techniques for establishing quantum query lower bounds, representing the minimum computational cost required for any quantum algorithm to solve a given computational problem, regardless of the quantum algorithm used. To this goal, we introduce the multiplicative ladder adversary method, a simplified version of the multiplicative adversary method which unifies the compressed oracle technique within the broader adversary framework while extending its applicability, offering a more intuitive approach to establishing quantum lower bounds.
Over the last decade, a large number of quantum algorithms have been discovered that outperform their classical counterparts. However, depending on the main techniques used, most of them fall into only three categories. The first category uses quantum Fourier transform to achieve super-polynomial speedup in group-theoretical problems. The second category uses amplitude amplification to achieve polynomial speedup in search problems. The third category simulates quantum many-body systems. Finding a new class of quantum algorithms has proven a challenging task. This dissertation explores several new approaches to developing quantum algorithms. These approaches include span programs, electrical flows and nonsparse Hamiltonian simulation. We demonstrate their power by successfully applying them to some useful problems, including tree detection, effective resistance estimation and curve fitting. All of these algorithms are time-efficient, and some of them are proven to be (nearly) optimal.Span program is a linear-algebraic computation model originally proposed to prove circuit lower bounds. Recently, it is found to be closely related to quantum query complexity. We develop a span-program-based quantum algorithm for the following variant of the tree containment problem. Let TTT be a fixed tree. Given the ntimesnn times nntimesn adjacency matrix of a graph GGG, we need to decide whether GGG contains TTT as a subgraph, or GGG does not contain TTT as a minor, under the promise that one of these cases holds. We call this problem is the subgraph/not-a-minor problem for TTT. We show that this problem can be solved by a quantum algorithm with O(n)O(n)O(n) query complexity and tildeO(n)tilde{O}(n)tildeO(n) time complexity. This query complexity is optimal, and this time complexity is tight up to poly-logarithmic factors. Electrical network theory has many applications to the design and analysis of classical algorithms. Its connection to quantum computation, however, remains mostly unclear. We give two quantum algorithms for approximating the effective resistance between two given vertices in an electrical network. Both of them have time complexity polynomial in lognlog{n}logn, ddd, ccc, 1/phi1/phi1/phi and 1/epsilon1/epsilon1/epsilon, where nnn is the number of vertices, ddd is the maximum degree of the vertices, ccc is the ratio of the largest to the smallest edge resistance, phiphiphi is the conductance of the network, and epsilonepsilonepsilon is the relative error. Our algorithms have exponentially better dependence on nnn than classical algorithms. Furthermore, we prove that the polynomial dependence on the inverse conductance is necessary. As a consequence, our algorithms cannot be greatly improved. Finally, as a by-product, our second algorithm also produces a quantum state encoding the electrical flow between two given vertices in a network, which might be of independent interest. Our algorithms are developed by using quantum tools to analyze the algebraic properties of graph-related matrices. While the first one relies on inverting the Laplacian matrix, the second one relies on projecting onto the kernel of the weighted incidence matrix. It is hopeful that more quantum algorithms could be designed in similar way. Curve fitting is the process of constructing a (simple continuous) curve that has the best fit to a series of data points. It is a common practice in many fields of science and engineering, because it can help us to understand the relationships among two or more variables, and to infer the values of a function where no data are available. We propose efficient quantum algorithms for estimating the best-fit parameters and the quality of least-square curve fitting. The running times of our algorithms are polynomial in lognlog{n}logn, ddd, kappakappakappa, nununu, chichichi, 1/Phi1/Phi1/Phi and 1/epsilon1/epsilon1/epsilon, where nnn is the number of data points to be fitted, ddd is the dimension of the feature vectors, kappakappakappa is the condition number of the design matrix, nununu and chichichi are some parameters reflecting the variances of the design matrix and response vector, PhiPhiPhi is the fit quality, and epsilonepsilonepsilon is the tolerable error. Our algorithms have exponentially better dependence on nnn than classical algorithms. They are designed by combining the techniques of phase estimation and density matrix exponentiation for nonsparse Hamiltonian simulation.