@article {3411, title = {A quantum central path algorithm for linear optimization}, year = {2023}, month = {11/7/2023}, abstract = {

We propose a novel quantum algorithm for solving linear optimization problems by quantum-mechanical simulation of the central path. While interior point methods follow the central path with an iterative algorithm that works with successive linearizations of the perturbed KKT conditions, we perform a single simulation working directly with the nonlinear complementarity equations. Combining our approach with iterative refinement techniques, we obtain an exact solution to a linear optimization problem involving m constraints and n variables using at most O((m+n)nnz(A)κ(M)L\⋅polylog(m,n,κ(M))) elementary gates and O(nnz(A)L) classical arithmetic operations, where nnz(A) is the total number of non-zero elements found in the constraint matrix, L denotes binary input length of the problem data, and κ(M) is a condition number that depends only on the problem data.

}, url = {https://arxiv.org/abs/2311.03977}, author = {Brandon Augustino and Jiaqi Leng and Giacomo Nannicini and Tam{\'a}s Terlaky and Xiaodi Wu} } @article {3250, title = {Quantum Hamiltonian Descent}, year = {2023}, month = {3/2/2023}, abstract = {

Gradient descent is a fundamental algorithm in both theory and practice for continuous optimization. Identifying its quantum counterpart would be appealing to both theoretical and practical quantum applications. A conventional approach to quantum speedups in optimization relies on the quantum acceleration of intermediate steps of classical algorithms, while keeping the overall algorithmic trajectory and solution quality unchanged. We propose Quantum Hamiltonian Descent (QHD), which is derived from the path integral of dynamical systems referring to the continuous-time limit of classical gradient descent algorithms, as a truly quantum counterpart of classical gradient methods where the contribution from classically-prohibited trajectories can significantly boost QHD\&$\#$39;s performance for non-convex optimization. Moreover, QHD is described as a Hamiltonian evolution efficiently simulatable on both digital and analog quantum computers. By embedding the dynamics of QHD into the evolution of the so-called Quantum Ising Machine (including D-Wave and others), we empirically observe that the D-Wave-implemented QHD outperforms a selection of state-of-the-art gradient-based classical solvers and the standard quantum adiabatic algorithm, based on the time-to-solution metric, on non-convex constrained quadratic programming instances up to 75 dimensions. Finally, we propose a \"three-phase picture\" to explain the behavior of QHD, especially its difference from the quantum adiabatic algorithm.

}, url = {https://arxiv.org/abs/2303.01471}, author = {Jiaqi Leng and Ethan Hickman and Joseph Li and Xiaodi Wu} } @article {3412, title = {A quantum-classical performance separation in nonconvex optimization}, year = {2023}, month = {11/1/2023}, abstract = {

In this paper, we identify a family of nonconvex continuous optimization instances, each d-dimensional instance with 2d local minima, to demonstrate a quantum-classical performance separation. Specifically, we prove that the recently proposed Quantum Hamiltonian Descent (QHD) algorithm [Leng et al., arXiv:2303.01471] is able to solve any d-dimensional instance from this family using O\˜(d3) quantum queries to the function value and O\˜(d4) additional 1-qubit and 2-qubit elementary quantum gates. On the other side, a comprehensive empirical study suggests that representative state-of-the-art classical optimization algorithms/solvers (including Gurobi) would require a super-polynomial time to solve such optimization instances.

}, url = {https://arxiv.org/abs/2311.00811}, author = {Jiaqi Leng and Yufan Zheng and Xiaodi Wu} } @article {3187, title = {Quantum simulation of real-space dynamics}, journal = {Quantum}, volume = {6}, year = {2022}, month = {11/8/2022}, pages = {860}, abstract = {

Quantum simulation is a prominent application of quantum computers. While there is extensive previous work on simulating finite-dimensional systems, less is known about quantum algorithms for real-space dynamics. We conduct a systematic study of such algorithms. In particular, we show that the dynamics of a d-dimensional Schr{\"o}dinger equation with η particles can be simulated with gate complexity O~(ηdFpoly(log(g\′/ϵ))), where ϵ is the discretization error, g\′ controls the higher-order derivatives of the wave function, and F measures the time-integrated strength of the potential. Compared to the best previous results, this exponentially improves the dependence on ϵ and g\′ from poly(g\′/ϵ) to poly(log(g\′/ϵ)) and polynomially improves the dependence on T and d, while maintaining best known performance with respect to η. For the case of Coulomb interactions, we give an algorithm using η3(d+η)Tpoly(log(ηdTg\′/(Δϵ)))/Δ one- and two-qubit gates, and another using η3(4d)d/2Tpoly(log(ηdTg\′/(Δϵ)))/Δ one- and two-qubit gates and QRAM operations, where T is the evolution time and the parameter Δ regulates the unbounded Coulomb interaction. We give applications to several computational problems, including faster real-space simulation of quantum chemistry, rigorous analysis of discretization error for simulation of a uniform electron gas, and a quadratic improvement to a quantum algorithm for escaping saddle points in nonconvex optimization.

}, doi = {https://doi.org/10.22331/q-2022-11-17-860}, url = {https://doi.org/10.22331\%2Fq-2022-11-17-860}, author = {Andrew M. Childs and Jiaqi Leng and Tongyang Li and Jin-Peng Liu and Chenyi Zhang} } @article {2645, title = {Quantum Algorithms for Escaping from Saddle Points}, journal = {Quantum}, volume = {5}, year = {2021}, month = {8/19/2021}, abstract = {

We initiate the study of quantum algorithms for escaping from saddle points with provable guarantee. Given a function f:Rn\→R, our quantum algorithm outputs an ϵ-approximate second-order stationary point using O~(log2n/ϵ1.75) queries to the quantum evaluation oracle (i.e., the zeroth-order oracle). Compared to the classical state-of-the-art algorithm by Jin et al. with O~(log6n/ϵ1.75) queries to the gradient oracle (i.e., the first-order oracle), our quantum algorithm is polynomially better in terms of n and matches its complexity in terms of 1/ϵ. Our quantum algorithm is built upon two techniques: First, we replace the classical perturbations in gradient descent methods by simulating quantum wave equations, which constitutes the polynomial speedup in n for escaping from saddle points. Second, we show how to use a quantum gradient computation algorithm due to Jordan to replace the classical gradient queries by quantum evaluation queries with the same complexity. Finally, we also perform numerical experiments that support our quantum speedup.

}, doi = {https://doi.org/10.22331/q-2021-08-20-529}, url = {https://arxiv.org/abs/2007.10253}, author = {Chenyi Zhang and Jiaqi Leng and Tongyang Li} }