Complete classification of trapping coins for quantum walks on the two-dimensional square lattice

One of the unique features of discrete-time quantum walks is called trapping, meaning the inability of the quantum walker to completely escape from its initial position, although the system is translationally invariant. The effect is dependent on the dimension and the explicit form of the local coin. A four-state discrete-time quantum walk on a square lattice is defined by its unitary coin operator, acting on the four-dimensional coin Hilbert space. The well-known example of the Grover coin leads to a partial trapping, i.e., there exists some escaping initial state for which the probability of staying at the initial position vanishes. On the other hand, some other coins are known to exhibit strong trapping, where such an escaping state does not exist. We present a systematic study of coins leading to trapping, explicitly construct all such coins for discrete-time quantum walks on the two-dimensional square lattice, and classify them according to the structure of the operator and the manifestation of the trapping effect. We distinguish three types of trapping coins exhibiting distinct dynamical properties, as exemplified by the existence or nonexistence of the escaping state and the area covered by the spreading wave packet

On analog quantum algorithms for the mixing of Markov chains

The problem of sampling from the stationary distribution of a Markov chain finds widespread applications in a variety of fields. The time required for a Markov chain to converge to its stationary distribution is known as the classical mixing time. In this article, we deal with analog quantum algorithms for mixing. First, we provide an analog quantum algorithm that given a Markov chain, allows us to sample from its stationary distribution in a time that scales as the sum of the square root of the classical mixing time and the square root of the classical hitting time. Our algorithm makes use of the framework of interpolated quantum walks and relies on Hamiltonian evolution in conjunction with von Neumann measurements. There also exists a different notion for quantum mixing: the problem of sampling from the limiting distribution of quantum walks, defined in a time-averaged sense. In this scenario, the quantum mixing time is defined as the time required to sample from a distribution that is close to this limiting distribution. Recently we provided an upper bound on the quantum mixing time for Erd\"os-Renyi random graphs [Phys. Rev. Lett. 124, 050501 (2020)]. Here, we also extend and expand upon our findings therein. Namely, we provide an intuitive understanding of the state-of-the-art random matrix theory tools used to derive our results. In particular, for our analysis we require information about macroscopic, mesoscopic and microscopic statistics of eigenvalues of random matrices which we highlight here. Furthermore, we provide numerical simulations that corroborate our analytical findings and extend this notion of mixing from simple graphs to any ergodic, reversible, Markov chain.Comment: The section concerning time-averaged mixing (Sec VIII) has been updated: Now contains numerical plots and an intuitive discussion on the random matrix theory results used to derive the results of arXiv:2001.0630

Quadratic speedup for finding marked vertices by quantum walks

A quantum walk algorithm can detect the presence of a marked vertex on a graph quadratically faster than the corresponding random walk algorithm (Szegedy, FOCS 2004). However, quantum algorithms that actually find a marked element quadratically faster than a classical random walk were only known for the special case when the marked set consists of just a single vertex, or in the case of some specific graphs. We present a new quantum algorithm for finding a marked vertex in any graph, with any set of marked vertices, that is (up to a log factor) quadratically faster than the corresponding classical random walk, resolving a question that had been open for 15 years

Quantum Singular Value Transformation & Its Algorithmic Applications

In this dissertation we study how efficiently quantum computers can solve various problems, and how large speedups can be achieved compared to classical computers. In particular we develop a generic quantum algorithmic framework that we call "quantum singular value transformation", capable of working with exponentially large matrices, that can apply polynomial transformations to the singular values of a block of a unitary. We show how quantum singular value transformation unifies a large number of prominent quantum algorithms, and show several problems where it leads to new quantum algorithms or improves earlier approaches. We develop an improved version of Jordan's quantum algorithm for gradient computation that can speed up the training of variational quantum optimization, and prove an essentially matching lower bound on quantum gradient computation. We also show that a quantum computer can very efficiently compute an approximate subgradient of a convex Lipschitz function. Combining this with some recent classical results we get improvements for black-box convex optimization problems. Then we take a new perspective on quantum SDP-solvers, introducing several new techniques, and improve on all prior quantum algorithms for SDP-solving. Finally we study the variable version of the Lovász Local Lemma (LLL) and its quantum generalization. We improve on the previous constructive quantum results by designing an algorithm that works efficiently for non-commuting terms as well, assuming that the system is "uniformly" gapped. For the variable version of the classical LLL we find optimal bounds for the "guaranteed-to-be-feasible" probabilities on cyclic dependency graphs

Quantum-inspired algorithms for solving low-rank linear equation systems with logarithmic dependence on the dimension

We present two efficient classical analogues of the quantum matrix inversion algorithm [16] for low-rank matrices. Inspired by recent work of Tang [27], assuming length-square sampling access to input data, we implement the pseudoinverse of a low-rank matrix allowing us to sample from the solution to the problem Ax = b using fast sampling techniques. We construct implicit descriptions of the pseudo-inverse by finding approximate singular value decomposition of A via subsampling, then inverting the singular values. In principle, our approaches can also be used to apply any desired “smooth” function to the singular values. Since many quantum algorithms can be expressed as a singular value transformation problem [15], our results indicate that more low-rank quantum algorithms can be effectively “dequantised” into classical length-square sampling algorithms