This thesis presents two main projects focused on hybrid quantum search algorithms. The first, Particle-Guided Grover's Search, applies Bayesian inference to quantum search when the number of solutions is a priori unknown. It begins with a prior distribution that encodes our initial "beliefs" about the true number of solutions in the search space, and recursively updates these beliefs using Bayesian inference. Measurement outcomes of the quantum search that do not yield a solution are interpreted as "evidence", guiding the Bayesian update to determine the optimal number of Grover iterations for subsequent rounds. This approach leverages Particle Filtering -- a Bayesian filtering technique -- to avoid explicitly storing the entire prior distribution at each step. Instead of requiring O(N) memory, the method draws a set of "particles" from the prior and updates their associated weights as quantum search failures accumulate. Particle filtering also offers the advantage of incorporating information from all past failures, rather than only the most recent one. The algorithm's performance is benchmarked against a prior work. The second project, Runtime--Coherence Trade-offs for Hybrid SAT Solvers, is focused on a class of hybrid k-SAT solvers. These solvers are based on a well-known random walk technique called Schoening's algorithm, which uses two sources of randomness to perform a local search over the space of all assignments for a k-SAT problem. In this project, we assume that these sources of randomness are encoded into a long bitstring, allowing the random walk to be interpreted as a deterministic function mapping each bitstring to either 0 or 1, depending on whether the walk successfully finds a satisfying assignment. This functional perspective enables us to pose the question: Given a bound on the coherence time of a quantum computer, which portion of the bitstring should we search coherently, and which portion should be left to classical search? We address this question by introducing a family of hybrid algorithms called "partial Groverizations", which strategically combine quantum and classical resources. We analyze the runtime performance of this family as a function of the available quantum coherence time. The results of this work have been published in IEEE Transactions on Quantum Engineering.
This thesis presents two main projects focused on hybrid quantum search algorithms. The first, Particle-Guided Grover's Search, applies Bayesian inference to quantum search when the number of solutions is a priori unknown. It begins with a prior distribution that encodes our initial "beliefs" about the true number of solutions in the search space, and recursively updates these beliefs using Bayesian inference. Measurement outcomes of the quantum search that do not yield a solution are interpreted as "evidence", guiding the Bayesian update to determine the optimal number of Grover iterations for subsequent rounds. This approach leverages Particle Filtering -- a Bayesian filtering technique -- to avoid explicitly storing the entire prior distribution at each step. Instead of requiring O(N) memory, the method draws a set of "particles" from the prior and updates their associated weights as quantum search failures accumulate. Particle filtering also offers the advantage of incorporating information from all past failures, rather than only the most recent one. The algorithm's performance is benchmarked against a prior work. The second project, Runtime--Coherence Trade-offs for Hybrid SAT Solvers, is focused on a class of hybrid k-SAT solvers. These solvers are based on a well-known random walk technique called Schoening's algorithm, which uses two sources of randomness to perform a local search over the space of all assignments for a k-SAT problem. In this project, we assume that these sources of randomness are encoded into a long bitstring, allowing the random walk to be interpreted as a deterministic function mapping each bitstring to either 0 or 1, depending on whether the walk successfully finds a satisfying assignment. This functional perspective enables us to pose the question: Given a bound on the coherence time of a quantum computer, which portion of the bitstring should we search coherently, and which portion should be left to classical search? We address this question by introducing a family of hybrid algorithms called "partial Groverizations", which strategically combine quantum and classical resources. We analyze the runtime performance of this family as a function of the available quantum coherence time. The results of this work have been published in IEEE Transactions on Quantum Engineering.
Many search-based quantum algorithms that achieve a theoretical speedup are not practically relevant since they require extraordinarily long coherence times, or lack the parallelizability of their classical counterparts.This raises the question of how to divide computational tasks into a collection of parallelizable sub-problems, each of which can be solved by a quantum computer with limited coherence time. Here, we approach this question via hybrid algorithms for the k-SAT problem. Our analysis is based on Schöning's algorithm, which solves instances of k-SAT by performing random walks in the space of potential assignments. The search space of the walk allows for "natural" partitions, where we subject only one part of the partition to a Grover search, while the rest is sampled classically, thus resulting in a hybrid scheme. In this setting, we argue that there exists a simple trade-off relation between the total runtime and the coherence-time, which no such partition based hybrid-scheme can surpass. For several concrete choices of partitions, we explicitly determine the specific runtime coherence-time relations, and show saturation of the ideal trade-off. Finally, we present numerical simulations which suggest additional flexibility in implementing hybrid algorithms with optimal trade-off.
Many search-based quantum algorithms that achieve a theoretical speed-up are not practically relevant since they require extraordinarily long coherence times, or lack the parallelizability of their classical counterparts. This raises the question of how to divide computational tasks into a collection of parallelizable subproblems, each of which can be solved by a quantum computer with limited coherence time. Here, we approach this question via hybrid algorithms for thek-SAT problem. Our analysis is based on Schöning's algorithm, which solves instances ofk-SAT by performing random walks in the space of potential assignments. The search space of the walk allows for ‘natural’ partitions, where we subject only one part of the partition to a Grover search, while the rest is sampled classically, thus resulting in a hybrid scheme. In this setting, we argue that there exists a simple trade-off relation between the total runtime and the coherence-time, which no such partition based hybrid-scheme can surpass. For several concrete choices of partitions, we explicitly determine the specific runtime coherence-time relations, and show saturation of the ideal trade-off. Finally, we present numerical simulations which suggest additional flexibility in implementing hybrid algorithms with optimal trade-off.
The field of Quantum Information Theory provides the theoretical foundation for the pursuit of quantum computers. The ongoing questions of how quantum computers will be realized and what they will achieve, are both very uncertain. However, worldwide efforts are beginning to converge on some answers, and the future of quantum computers is looking brighter than ever. In contribution to the grand goal that is quantum computing, this thesis serves as a demonstration to the usefulness of quantum over classical computing. The central theme of my work, and my collaborators, is the exploration of using quantum systems as a tool for path finding and search algorithms. First, we explore a specific problem of sequential measurements in Quantum Information Theory, namely quantum retrodiction. In experiments involving quantum systems with numerous intermediate interactions, often times the only thing that matters to the experimenters is the final measurement. However, quantum retrodiction is the problem of extrapolating information about the past of a quantum system, by studying the present state. The case studied in this dissertation is the retrodiction of a series of two-outcome measurements, the double and triple qubit-interferometers. In both cases, we are presented with the problem of inferring the results of the two-outcome measurements, using only the final state of the system. Specifically, all of the possible outcomes from the measurements result in a different final state, and we are tasked with distinguishing which final state we have. One version of this problem can be seen as light traversing a series of half-silver mirrors, and we would like to know the path the light took. To distinguish which final state we are given, we employ the use of POVMs (Positive-Operator Valued Measurements) that reflect the symmetry of the system. The effectiveness of these POVMs in solving the retrodiction problem are then presented, along with a few comments as to other types of information that can be extracted. Second, we examine a different type of path finding via a quantum system. For a given graph G that can be expressed by connections and nodes, the same graph can be mapped onto a quantum system with a Hilbert space H. All of the connections in the graph can be represented as states in H, and the nodes act as sites for local unitary operations. The sum of all these local operations give rise to U, which is the unitary operator that acts on H, and describes the behavior of the system. Using this formalism, we explore a particular type of U called a scattering quantum random walk. The quantum random walk is the quantum mechanical analogy to the classical random walk, and serves as a powerful tool for searching on a graph. In this dissertation, we present the case of searching for a marked final node, on a graph that consists of a series of stars. The initial state of the system has an equal probability distribution among all states, reflecting no a priori knowledge about the location of the final node. But by applying the quantum random walk, the final state of the system reaches a superposition where nearly all of the probability is concentrated along the path leading to the marked final node. Lastly, we extend this idea of using a quantum random walk to reveal a path even further. We examine a specific type of graph G, called an “N-tree” graph, and apply our quantum random walk scheme. An N-tree graph can be described as a maze where the solver is presented with a junction consisting of N choices. Beyond each choice, is an identical junction of N choices, and so on. The depth of the maze is categorized by a parameter M, thus, to correctly solve the maze one must make M correct choices in succession. Labeling the marked final nodes as F, we present a quantum algorithm that uses a quantum random walk to locate F faster than any classical algorithm. Specifically, the quantum random walk results in a final state of the system with a high probability concentration on all the states leading to F. Two methods for searching for F are then presented. The first approach mimics the ideology of a Grover Search, relying on repetitive searches until F is found. The second approach presents a novel use of past measurements to dictates how the algorithm proceeds next. Specifically, we take advantage of the fact that states along the path leading to F hold most of the probability in the system. In the end, the second quantum algorithm approach provides an speedup over the classical algorithm of the order O().
Combinatorial optimisation is a main topic for real-world applications, where fast solutions are required. Optimisation algorithms are incredibly powerful, but still face bottlenecks for certain problems. In recent years, quantum algorithms have been proposed as a potential candidate for solving such problems. The question that naturally arises is whether they could provide meaningful speed-ups over classical approaches. The study of asymptotic complexity has become central in the development of quantum algorithms. Nevertheless, having a promising asymptotic improvement does not directly translate into good practical performance. This is already well known for classical algorithms, which can be executed and evaluated directly. When it comes to quantum algorithms, the situation is more nuanced, as solving practically relevant instances would require a fully fault-tolerant quantum computer. In this thesis, we address the challenge of assessing the practical performance of a quantum algorithm without access to quantum hardware. Our strategy is to evaluate how these algorithms would perform under idealised fault-tolerant assumptions and to identify parameter regimes in which quantum approaches could potentially become useful. Our goal is to guide algorithmic development long before implementation becomes possible. We first give a general overview of the quantum (sub)routines that serve as building blocks for more advanced algorithms, as well as of the three hybrid benchmarking techniques used throughout this thesis. Then, we elaborate on each method: we start with the algorithmic descriptions, proceed to the methodology, and conclude with their hybrid evaluation on practically relevant datasets. Within the hybrid benchmarking method 1 (gate count analysis), we analytically derive the minimum number of gates that a quantum version of the classical simplex algorithm would require to solve realistic instances. By executing classical counterparts of the quantum subroutines, we infer the execution speed required per gate for the quantum simplex to outperform its classical counterpart. Within the hybrid benchmarking method 2 (query count analysis), we benchmark functional quantum linear solvers by analytically deriving a precise number of query calls made to the same oracles as a function of problem parameters. This provides insight into which algorithm might be more suitable in a given parameter regime. It allows for a direct comparison only between different quantum algorithms and is independent of classical runtime measurements. Within the hybrid benchmarking method 3 (cycle count analysis), we develop a runtime estimation framework tailored to the quantum tree generator–based search for solving 0-1-knapsack problems. We evaluate its performance on large scale instances beyond the reach of general simulation. We compare it with state-of-the-art classical linear programming solvers in terms of processor cycle counts. Hybrid benchmarking provides a fair, systematic benchmarking methodology, ensuring reproducible and relevant comparisons not only between classical and quantum approaches, but also among different quantum algorithms.
The k-SAT problem is a canonical NP-complete problem for which no efficient algorithm is known. This paper proposes a hybrid quantum–classical algorithm that employs Grover's algorithm as the inner local search of a randomized outer loop. Each outer iteration measures a random assignment, prepares a problem-dependent superposition over the Hamming ball of radius r around it, and applies amplitude amplification to locate a satisfying assignment. Because the ball center is measured, the outer loop remains a classical repetition over independently sampled balls and contributes the inverse success probability rather than its square root; balancing this against the inner quadratic speedup shifts the optimal radius from n/(k+1) to n/(1+k) and yields the running time (2−2/(1+k))n, up to polynomial factors—1.268n for k=3, below the best known classical 1.307n. The contribution is not an asymptotic record—the best known quantum exponents for k-SAT, obtained by amplifying stronger classical solvers as black boxes, remain smaller—but a fully explicit, gate-level construction: the problem-dependent state over the Hamming ball and the k-SAT oracle are decomposed into elementary quantum gates, and the polynomial gate and qubit overheads are accounted for in full.
Satisfiability (SAT) is a central problem in computer science, and advances in SAT-solving algorithms have a far-reaching impact across many fields. Recent works have proposed quantum SAT solvers based on Grover's algorithm, a quantum search technique. However, Grover-based approaches face a key limitation: they typically require prior knowledge of the number of satisfying assignments of the target Boolean formula. This information is unavailable in most practical settings. Quantum counting can be used to estimate this quantity, but it incurs a computational overhead that is several orders of magnitude higher than Grover search. In this paper, we propose a novel quantum SAT solver based on entanglement and equivalence checking. Our method does not assume prior knowledge of the number of solutions and is computationally more efficient than quantum counting. Although the worst case time complexity is inevitably exponential, we prove that the expected time complexity of our approach is only constant time O(1) over random Boolean functions. Experimental results also support our theoretical claim.