14 research outputs found
Quantum Algorithms for Scientific Computing and Approximate Optimization
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
Design Automation and Design Space Exploration for Quantum Computers
A major hurdle to the deployment of quantum linear systems algorithms and
recent quantum simulation algorithms lies in the difficulty to find inexpensive
reversible circuits for arithmetic using existing hand coded methods. Motivated
by recent advances in reversible logic synthesis, we synthesize arithmetic
circuits using classical design automation flows and tools. The combination of
classical and reversible logic synthesis enables the automatic design of large
components in reversible logic starting from well-known hardware description
languages such as Verilog. As a prototype example for our approach we
automatically generate high quality networks for the reciprocal , which is
necessary for quantum linear systems algorithms.Comment: 6 pages, 1 figure, in 2017 Design, Automation & Test in Europe
Conference & Exhibition, DATE 2017, Lausanne, Switzerland, March 27-31, 201
Black-Box Quantum State Preparation with Inverse Coefficients
Black-box quantum state preparation is a fundamental building block for many
higher-level quantum algorithms, which is applied to transduce the data from
computational basis into amplitude. Here we present a new algorithm for
performing black-box state preparation with inverse coefficients based on the
technique of inequality test. This algorithm can be used as a subroutine to
perform the controlled rotation stage of the Harrow-Hassidim-Lloyd (HHL)
algorithm and the associated matrix inversion algorithms with exceedingly low
cost. Furthermore, we extend this approach to address the general black-box
state preparation problem where the transduced coefficient is a general
non-linear function. The present algorithm greatly relieves the need to do
arithmetic and the error is only resulted from the truncated error of binary
string. It is expected that our algorithm will find wide usage both in the NISQ
and fault-tolerant quantum algorithms.Comment: 11 pages, 3 figure
A quantum circuit simulator and its applications on Sunway TaihuLight supercomputer
Classical simulation of quantum computation is vital for verifying quantum
devices and assessing quantum algorithms. We present a new quantum circuit
simulator developed on the Sunway TaihuLight supercomputer. Compared with other
simulators, the present one is distinguished in two aspects. First, our
simulator is more versatile. The simulator consists of three mutually
independent parts to compute the full, partial and single amplitudes of a
quantum state with different methods. It has the function of emulating the
effect of noise and support more kinds of quantum operations. Second, our
simulator is of high efficiency. The simulator is designed in a two-level
parallel structure to be implemented efficiently on the distributed many-core
Sunway TaihuLight supercomputer. Random quantum circuits can be simulated with
40, 75 and 200 qubits on the full, partial and single amplitude, respectively.
As illustrative applications of the simulator, we present a quantum fast
Poisson solver and an algorithm for quantum arithmetic of evaluating
transcendental functions. Our simulator is expected to have broader
applications in developing quantum algorithms in various fields.Comment: 21 pages, 9 figure