29 citations · 54 across the 18 of their papers we have counts for
18 papers
Quantum score matching with applications to learning thermal states
Yulong Dong, Jiaqi Leng
Score matching has driven major advances in classical generative learning by enabling models to learn from data without evaluating intractable normalization constants, or partition…
Accelerating quantum Gibbs sampling without quantum walks
Jiaqi Leng, Jiaqing Jiang, Lin Lin
Szegedy's quantum walk quadratically accelerates reversible classical Markov chains, but extending this mechanism to efficiently implementable quantum Gibbs samplers has remained c…
Quantum Estimation of Non-Hermitian Pseudospectra
Gengzhi Yang, Jiaqi Leng, Xiaodi Wu +1
Non-Hermitian many-body systems can be spectrally unstable, so small perturbations may induce large eigenvalue shifts. The pseudospectrum quantifies this instability and provides a…
Predicting properties of quantum thermal states from a single trajectory
Jiaqing Jiang, Jiaqi Leng, Lin Lin
Estimating thermal expectation values of observables is a fundamental task in quantum physics, quantum chemistry, and materials science. While recent quantum algorithms have enable…
Resource-efficient quantum simulation of transport phenomena via Hamiltonian embedding
Joseph Li, Gengzhi Yang, Jiaqi Leng +1
Transport phenomena play a key role in a variety of application domains, and efficient simulation of these dynamics remains an outstanding challenge. While quantum computers offer…
Continuous Unitary Designs for Universally Robust Quantum Control
Xiaodong Yang, Jiaqing Leng, Jun Li
Unitary designs are unitary ensembles that emulate Haar-random unitary statistics. They provide a vital tool for studying quantum randomness and have found broad applications in qu…
(Sub)Exponential Quantum Speedup for Optimization
Jiaqi Leng, Kewen Wu, Xiaodi Wu +1
We demonstrate provable (sub)exponential quantum speedups in both discrete and continuous optimization, achieved through simple and natural quantum optimization algorithms, namely…
Quantum Hamiltonian Descent for Non-smooth Optimization
Jiaqi Leng, Yufan Zheng, Zhiyuan Jia +4
Non-smooth optimization models play a fundamental role in various disciplines, including engineering, science, management, and finance. However, classical algorithms for solving su…
On the Computational Complexity of Schrödinger Operators
Yufan Zheng, Jiaqi Leng, Yizhou Liu +1
We study computational problems related to the Schrödinger operator in the real space under the condition that (i) the potential function is smooth and has its valu…
Differentiable Quantum Computing for Large-scale Linear Control
Connor Clayton, Jiaqi Leng, Gengzhi Yang +3
As industrial models and designs grow increasingly complex, the demand for optimal control of large-scale dynamical systems has significantly increased. However, traditional method…
QHDOPT: A Software for Nonlinear Optimization with Quantum Hamiltonian Descent
Samuel Kushnir, Jiaqi Leng, Yuxiang Peng +2
We develop an open-source, end-to-end software (named QHDOPT), which can solve nonlinear optimization problems using the quantum Hamiltonian descent (QHD) algorithm. QHDOPT offers…
Expanding Hardware-Efficiently Manipulable Hilbert Space via Hamiltonian Embedding
Jiaqi Leng, Joseph Li, Yuxiang Peng +1
Many promising quantum applications depend on the efficient quantum simulation of an exponentially large sparse Hamiltonian, a task known as sparse Hamiltonian simulation, which is…
A quantum central path algorithm for linear optimization
Brandon Augustino, Jiaqi Leng, Giacomo Nannicini +2
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…
A quantum-classical performance separation in nonconvex optimization
Jiaqi Leng, Yufan Zheng, Xiaodi Wu
In this paper, we identify a family of nonconvex continuous optimization instances, each -dimensional instance with local minima, to demonstrate a quantum-classical perfor…
Quantum Hamiltonian Descent
Jiaqi Leng, Ethan Hickman, Joseph Li +1
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…
Differentiable Analog Quantum Computing for Optimization and Control
Jiaqi Leng, Yuxiang Peng, Yi-Ling Qiao +2
We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quan…
Quantum simulation of real-space dynamics
Andrew M. Childs, Jiaqi Leng, Tongyang Li +2
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 alg…
Quantum algorithms for escaping from saddle points
Chenyi Zhang, Jiaqi Leng, Tongyang Li
We initiate the study of quantum algorithms for escaping from saddle points with provable guarantee. Given a function , our quantum algorithm ou…