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This chapter introduces topological quantum computation (TQC), a model using non-Abelian anyons, specifically Fibonacci anyons, for information processing via braiding operations. The braid group and fusion rules are central to TQC, enabling operations that remain robust against certain environmental errors. TQC provides inherent fault tolerance, reducing susceptibility to local disturbances. The chapter concludes by examining the challenges and future potential of topological models, marking TQC as a promising, albeit complex, path toward scalable and robust quantum computing solutions.
This chapter delves into topological order, a phase of matter with implications for quantum computation. The ℤ2 toric code model is introduced, using lattice arrangements of qubits to demonstrate topological protection against errors. Anyons, particles exhibiting unique exchange statistics, are utilized for encoding information through braiding operations. Surface codes are discussed as practical implementations of topological error correction, leveraging topological entanglement entropy to protect quantum information. This approach provides a highly resilient framework for quantum error correction, essential for developing fault-tolerant quantum computers with intrinsic stability against certain types of errors.
This chapter examines quantum decoherence, a process by which quantum information is lost due to environmental interactions. Various noise channels, such as bit-flip, phase-flip, and depolarizing channels, are discussed to illustrate common errors in qubit states. The Kraus representation and Lindblad equation offer frameworks for modeling these interactions. Metrics such as T1 (relaxation time) and T2 (decoherence time) are introduced to measure qubit stability. Understanding decoherence mechanisms is critical for developing strategies to preserve quantum information, laying the groundwork for quantum error correction techniques and highlighting the challenges in creating reliable quantum systems.
This chapter covers quantum error correction, essential for preserving quantum information in the presence of noise. It introduces the bit-flip and phase-flip codes as foundational error-correction methods, building toward Shor’s code, which corrects general single-qubit errors. Logical qubits are formed by encoding physical qubits to maintain stability. Stabilizer codes are presented as a systematic framework for error correction, enabling fault-tolerant quantum computing. These principles are crucial for creating scalable quantum systems that can perform reliable computations, even in noisy environments, addressing a central challenge in quantum computing’s practical implementation.
This chapter explores classical computation fundamentals, starting with Turing machines as a foundation for defining computability. The universal Turing machine is introduced, emphasizing the theoretical basis for all computable functions. Computational complexity is discussed, differentiating between tractable and intractable problems and explaining complexity classes as a framework for problem-solving. The chapter also covers the circuit model, providing a bridge between theoretical constructs and modern computer architecture. Finally, the concept of reversible computation is introduced, which has implications for energy-efficient processing. Through these topics, the chapter delineates classical computation’s limitations, setting up the motivation to transition into quantum approaches in subsequent chapters.
This chapter introduces quantum computation by comparing classical and quantum computers. Core concepts including qubits, superposition, and entanglement are introduced, setting the stage for deeper exploration. Various quantum computing models are summarized, with a focus on the circuit and topological models. The chapter explains why quantum computing is necessary, especially for tasks beyond classical computing’s limits. It discusses existing quantum platforms and provides an overview of their capabilities and limitations. The chapter also offers a brief historical perspective, touches on computational energy efficiency, and forecasts a quantum future where quantum and classical computing work in tandem. This groundwork provides essential insights into quantum computation’s potential and upcoming chapters’ explorations of algorithmic and theoretical principles.
Over the past few decades, graph theory has developed into one of the central areas of modern mathematics, with close (and growing) connections to areas of pure mathematics such as number theory, probability theory, algebra and geometry, as well as to applied areas such as the theory of networks, machine learning, statistical physics, and biology. It is a young and vibrant area, with several major breakthroughs having occurred in just the past few years. This book offers the reader a gentle introduction to the fundamental concepts and techniques of graph theory, covering classical topics such as matchings, colourings and connectivity, alongside the modern and vibrant areas of extremal graph theory, Ramsey theory, and random graphs. The focus throughout is on beautiful questions, ideas and proofs, and on illustrating simple but powerful techniques, such as the probabilistic method, that should be part of every young mathematician's toolkit.
A trace of a sequence is generated by deleting each bit of the sequence independently with a fixed probability. The well-studied trace reconstruction problem asks how many traces are required to reconstruct an unknown binary sequence with high probability. In this paper, we study the multidimensional version of this problem for matrices and hypermatrices, where a trace is generated by deleting each row/column of the matrix or each slice of the hypermatrix independently with a constant probability. Previously, Krishnamurthy, Mazumdar, McGregor and Pal showed that $\exp (\widetilde {O}(n^{d/(d+2)}))$ traces suffice to reconstruct any unknown $n\times n$ matrix (for $d=2$) and any unknown $n^{\times d}$ hypermatrix. By developing a dimension reduction procedure and establishing a multivariate version of the Littlewood-type result that lower bounds sparse complex polynomials around $1$, we improve this upper bound by showing that $\exp (\widetilde {O}(n^{3/7}))$ traces suffice to reconstruct any unknown $n\times n$ matrix, and $\exp (\widetilde {O}(n^{3/5}))$ traces suffice to reconstruct any unknown $n^{\times d}$ hypermatrix. In contrast to the earlier bound, our new exponent is bounded away from $1$ even as $d$ becomes very large.
We identify the size of the largest connected component in a subcritical inhomogeneous random graph with a kernel of preferential attachment type. The component is polynomial in the graph size with an explicitly given exponent, which is strictly larger than the exponent for the largest degree in the graph. This is in stark contrast to the behaviour of inhomogeneous random graphs with a kernel of rank one. Our proof uses local approximation by branching random walks going well beyond the weak local limit and novel results on subcritical killed branching random walks.
Aimed at advanced undergraduate and graduate-level students, this textbook covers the core topics of quantum computing in a format designed for a single-semester course. It will be accessible to learners from a range of disciplines, with an understanding of linear algebra being the primary prerequisite. The textbook introduces central concepts such as quantum mechanics, the quantum circuit model, and quantum algorithms, and covers advanced subjects such as the surface code and topological quantum computation. These topics are essential for understanding the role of symmetries in error correction and the stability of quantum architectures, which situate quantum computation within the wider realm of theoretical physics. Graphical representations and exercises are included throughout the book and optional expanded materials are summarized within boxed 'Remarks'. Lecture notes have been made freely available for download from the textbook's webpage, with instructors having additional online access to selected exercise solutions.
Balister, the second author, Groenland, Johnston, and Scott recently showed that there are asymptotically $C4^n/n^{3/4}$ many unordered sequences that occur as degree sequences of graphs with $n$ vertices. Combining limit theory for infinitely divisible distributions with a new connection between a class of random walk trajectories and a subset counting formula from additive number theory, we describe $C$ in terms of Walkup’s number of rooted plane trees. The bijection is related to an instance of the Lévy–Khintchine formula. Our main result complements a result of Stanley, that ordered graphical sequences are related to quasi-forests.
We study time-inhomogeneous random walks on finite groups in the case where each random walk step need not be supported on a generating set of the group. When the supports of the random walk steps satisfy a natural condition involving normal subgroups of quotients of the group, we show that the random walk converges to the uniform distribution on the group and give bounds for the convergence rate using spectral properties of the random walk steps. As an application, we use the moment method of Wood to prove a universality theorem for cokernels of random integer matrices allowing some dependence between entries.
A well-known theorem of Nikiforov asserts that any graph with a positive $K_{r}$-density contains a logarithmic blowup of $K_r$. In this paper, we explore variants of Nikiforov’s result in the following form. Given $r,t\in \mathbb{N}$, when a positive $K_{r}$-density implies the existence of a significantly larger (with almost linear size) blowup of $K_t$? Our results include:
• For an $n$-vertex ordered graph $G$ with no induced monotone path $P_{6}$, if its complement $\overline {G}$ has positive triangle density, then $\overline {G}$ contains a biclique of size $\Omega ({n \over {\log n}})$. This strengthens a recent result of Pach and Tomon. For general $k$, let $g(k)$ be the minimum $r\in \mathbb{N}$ such that for any $n$-vertex ordered graph $G$ with no induced monotone $P_{2k}$, if $\overline {G}$ has positive $K_r$-density, then $\overline {G}$ contains a biclique of size $\Omega ({n \over {\log n}})$. Using concentration of measure and the isodiametric inequality on high dimensional spheres, we provide constructions showing that, surprisingly, $g(k)$ grows quadratically. On the other hand, we relate the problem of upper bounding $g(k)$ to a certain Ramsey problem and determine $g(k)$ up to a factor of 2.
• Any incomparability graph with positive $K_{r}$-density contains a blowup of $K_r$ of size $\Omega ({n \over {\log n}}).$ This confirms a conjecture of Tomon in a stronger form. In doing so, we obtain a strong regularity type lemma for incomparability graphs with no large blowups of a clique, which is of independent interest. We also prove that any $r$-comparability graph with positive $K_{(2h-2)^{r}+1}$-density contains a blowup of $K_h$ of size $\Omega (n)$, where the constant $(2h-2)^{r}+1$ is optimal.
The ${n \over {\log n}}$ size of the blowups in all our results are optimal up to a constant factor.
This introduction to quantum computing from a classical programmer's perspective is meant for students and practitioners alike. More than 50 quantum techniques and algorithms are explained with mathematical derivations and code for simulation, using an open-source code base in Python and C++. New material throughout this fully revised and expanded second edition includes new chapters on Quantum Machine Learning, State Preparation, and Similarity Tests. Coverage includes algorithms exploiting entanglement, black-box algorithms, the quantum Fourier transform, phase estimation, quantum walks, and foundational QML algorithms. Readers will find detailed, easy-to-follow derivations and implementations of Shor's algorithm, Grover's algorithm, SAT3, graph coloring, the Solovay-Kitaev algorithm, Moettoenen's algorithm, quantum mean, median, and minimum finding, Deutsch's algorithm, Bernstein-Vazirani, quantum teleportation and superdense coding, the CHSH game, and, from QML, the HHL algorithm, Euclidean distance, and PCA. The book also discusses productivity issues like quantum noise, error correction, quantum programming languages, compilers, and techniques for transpilation.