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Quantum Information and Computation

Quantum algorithms and circuits for scientific computing

Mihir K. Bhaskar, Stuart Hadfield, Anargyros Papageorgiou, Iasonas Petras

Published Mar 1, 2016• 53 citations
Publisher

Abstract

Quantum algorithms for scientific computing require modules implementing fundamental functions, such as the square root, the logarithm, and others. We require algorithms that have a well-controlled numerical error, that are uniformly scalable and reversible (unitary), and that can be implemented efficiently. We present quantum algorithms and circuits for computing the square root, the natural logarithm, and arbitrary fractional powers. We provide performance guarantees in terms of their worst-case accuracy and cost. We further illustrate their performance by providing tests comparing them to the respective floating point implementations found in widely used numerical software.

DOI
10.26421/qic16.3-4-2

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Quantum algorithms for scientific computing require modules implementing fundamental functions, such as the square root, the logarithm, and others. We require algorithms that have a well-controlled numerical error, that are uniformly scalable and reversible (unitary), and that can be implemented efficiently. We present quantum algorithms and circuits for computing the square root, the natural logarithm, and arbitrary fractional powers. We provide performance guarantees in terms of their worst-case accuracy and cost. We further illustrate their performance by providing tests comparing them to the respective floating point implementations found in widely used numerical software.
Quantum algorithms for scientific computing require modules implementing fundamental functions, such as the square root, the logarithm, and others. We require algorithms that have a well-controlled numerical error, that are uniformly scalable and reversible (unitary), and that can be implemented efficiently. We present quantum algorithms and circuits for computing the square root, the natural logarithm, and arbitrary fractional powers. We provide performance guarantees in terms of their worst-case accuracy and cost. We further illustrate their performance by providing tests comparing them to the respective floating point implementations found in widely used numerical software.
Quantum computation appears to offer significant advantages over classical computation and this has generated a tremendous interest in the field. In this thesis we study the application of quantum computers to computational problems in science and engineering, and to combinatorial optimization problems. We outline the results below. Algorithms for scientific computing require modules, i.e., building blocks, implementing elementary numerical functions that have well-controlled numerical error, are uniformly scalable and reversible, and that can be implemented efficiently. We derive quantum algorithms and circuits for computing square roots, logarithms, and arbitrary fractional powers, and derive worst-case error and cost bounds. We describe a modular approach to quantum algorithm design as a first step towards numerical standards and mathematical libraries for quantum scientific computing. A fundamental but computationally hard problem in physics is to solve the time-independent Schrödinger equation. This is accomplished by computing the eigenvalues of the corresponding Hamiltonian operator. The eigenvalues describe the different energy levels of a system. The cost of classical deterministic algorithms computing these eigenvalues grows exponentially with the number of system degrees of freedom. The number of degrees of freedom is typically proportional to the number of particles in a physical system. We show an efficient quantum algorithm for approximating a constant number of low-order eigenvalues of a Hamiltonian using a perturbation approach. We apply this algorithm to a special case of the Schrödinger equation and show that our algorithm succeeds with high probability, and has cost that scales polynomially with the number of degrees of freedom and the reciprocal of the desired accuracy. This improves and extends earlier results on quantum algorithms for estimating the ground state energy. We consider the simulation of quantum mechanical systems on a quantum computer. We show a novel divide and conquer approach for Hamiltonian simulation. Using the Hamiltonian structure, we can obtain faster simulation algorithms. Considering a sum of Hamiltonians we split them into groups, simulate each group separately, and combine the partial results. Simulation is customized to take advantage of the properties of each group, and hence yield refined bounds to the overall simulation cost. We illustrate our results using the electronic structure problem of quantum chemistry, where we obtain significantly improved cost estimates under mild assumptions. We turn to combinatorial optimization problems. An important open question is whether quantum computers provide advantages for the approximation of classically hard combinatorial problems. A promising recently proposed approach of Farhi et al. is the Quantum Approximate Optimization Algorithm (QAOA). We study the application of QAOA to the Maximum Cut problem, and derive analytic performance bounds for the lowest circuit-depth realization, for both general and special classes of graphs. Along the way, we develop a general procedure for analyzing the performance of QAOA for other problems, and show an example demonstrating the difficulty of obtaining similar results for greater depth. We show a generalization of QAOA and its application to wider classes of combinatorial optimization problems, in particular, problems with feasibility constraints. We introduce the Quantum Alternating Operator Ansatz, which utilizes more general unitary operators than the original QAOA proposal. Our framework facilitates low-resource implementations for many applications which may be particularly suitable for early quantum computers. We specify design criteria, and develop a set of results and tools for mapping diverse problems to explicit quantum circuits. We derive constructions for several important prototypical problems including Maximum Independent Set, Graph Coloring, and the Traveling Salesman problem, and show appealing resource cost estimates for their implementations.
In recent years, rapid advancements in quantum technologies have resulted in heightened research activity, particularly in the domain of efficient implementation of computationally intensive kernels from prominent quantum algorithms. This is partly addressed by increasingly sophisticated synthesis techniques for quantum circuits. In this paper, we study the efficient Clifford +T circuit realization of two kernels, namely square root and inverse square root for different number formats. Quantum algorithms for solving Toeplitz system of linear equations, numerical methods for solving Poisson equation and in general for scientific computing require such kernels with customizable trade-off points for accuracy and performance. We present an efficient circuit design, obtained via detailed exploration of design choices in terms of accuracy and quantum costs. The manual circuits designed provide upto $50 \times $ reduction in qubit count for high precision circuits. Furthermore, a detailed benchmarking of state-of-the-art synthesis tools is undertaken for these kernels along with detailed comparison against manually-implemented designs.
Quantum algorithms promise up to a superpolynomial factor speedup in computational tasks compared to their classical counterparts. Quantum computers are now approaching sizes where quantum algorithms can be demonstrated. As a result, the implementation of algorithms as quantum circuits has become an important research topic. In this work, we provide an overview of quantum computation and the design of quantum circuits. Properties unique to quantum computation are outlined. We introduce how a quantum algorithm is translated into a circuit and show measures used to assess the performance of quantum circuits.
Quantum square root operation is a useful building block in implementing important quantum algorithms such as the Harrow–Hassidim–Lloyd (HHL) algorithm, algorithms in physics and quantum chemistry, and also used in computing roots of quadratic equation, evaluating quadratic congruence, solving Hamiltonian and Poisson equations, trigonometric functions, computing logarithm and fractional exponent, and in quantum chemistry [1]–[5]. Quantum circuits could be made fault-tolerant by using error correcting codes and fault-tolerant quantum gates (such as the Clifford + T-gates). However, The T-gates are expensive to implement than other fault-tolerant gates (such as Clifford+T gates) [6] [7]. Therefore, quantum circuits with low T-count and qubit cost is preferred. In this paper, we present a novel quantum square root architecture optimized for T-count and Qubit cost that produces no garbage outputs. The proposed square root circuit accepts integer and fractional inputs. We use the Babylonian method to calculate the square root. To reduce the number of iteration, the initial guess required in the Babylonian method is calculated from the integer square root circuit in [8] which reduces the cost measures. To further save the cost, we use the resource efficient non-restoring division circuit shown in [9] and quantum adder shown in [6]. The proposed quantum square root circuit sees an asymptotic reduction of 95.84% in T-count and 88.89% in Qubit cost with respect to Bhaskar et al. [1]. With respect to Dutta et al. [10] the design sees an asymptotic reduction of 96% in T-count, and 88.89% in Qubit cost.