The quantum dense output problem is the process of evaluating
time-accum...
This paper addresses the problem of solving nonlinear systems in the con...
Coupled cluster theory is considered to be the “gold standard” ansatz of...
A fundamental problem in quantum physics is to encode functions that are...
We analyze stochastic gradient descent (SGD) type algorithms on a
high-d...
We propose a simple method for simulating a general class of non-unitary...
Pricing based on individual customer characteristics is widely used to
m...
We provide the first rigorous study of the finite-size error in the simp...
Controlling False Discovery Rate (FDR) while leveraging the side informa...
Quantum signal processing (QSP) represents a real scalar polynomial of d...
The time-marching strategy, which propagates the solution from one time ...
Explicit antisymmetrization of a two-layer neural network is a potential...
Under suitable assumptions, the algorithms in [Lin, Tong, Quantum 2020] ...
Variational quantum algorithms stand at the forefront of simulations on
...
Many standard linear algebra problems can be solved on a quantum compute...
Depression is a global mental health problem, the worst case of which ca...
This is a set of lecture notes used in a graduate topic class in applied...
The combination of neural networks and quantum Monte Carlo methods has a...
We propose a simple quantum algorithm for simulating highly oscillatory
...
Learning multi-view data is an emerging problem in machine learning rese...
Quantum subspace diagonalization methods are an exciting new class of
al...
Symmetric quantum signal processing provides a parameterized representat...
We propose a staggered mesh method for correlation energy calculations o...
Deep neural network (DNN) models have achieved phenomenal success for
ap...
Despite decades of practice, finite-size errors in many widely used
elec...
Direct simulation of the von Neumann dynamics for a general (pure or mix...
The calculation of the MP2 correlation energy for extended systems can b...
Controlling false discovery rate (FDR) while leveraging the side informa...
The accuracy of quantum dynamics simulation is usually measured by the e...
Variational quantum eigensolvers have recently received increased attent...
The extended Lagrangian molecular dynamics (XLMD) method provides a usef...
The efficient treatment of long-range interactions for point clouds is a...
The Quantum Approximate Optimization Ansatz (QAOA) is a prominent exampl...
Preconditioning is the most widely used and effective way for treating
i...
The LINPACK benchmark reports the performance of a computer for solving ...
In many scientific problems such as video surveillance, modern genomic
a...
We present the GPU version of DeePMD-kit, which, upon training a deep ne...
Preparing the ground state of a given Hamiltonian and estimating its gro...
The task of using machine learning to approximate the mapping
x∑_i=1^d x...
The polarizability operator plays a central role in density functional
p...
The quantum approximate optimization algorithm (QAOA), as a hybrid
quant...
The Hartree-Fock-Bogoliubov (HFB) theory is the starting point for treat...
The architectures of deep neural networks (DNN) rely heavily on the
unde...
We consider universal approximations of symmetric and anti-symmetric
fun...
The recently developed Deep Potential [Phys. Rev. Lett. 120, 143001, 201...
We present a simple algorithm to solve the quantum linear system problem...
Quantum embedding theories are powerful tools for approximately solving
...
We demonstrate that with an optimally tuned scheduling function, adiabat...
Tensor network operators, such as the matrix product operator (MPO) and ...
Understanding and interpreting how machine learning (ML) models make
dec...