The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

Predicting the properties of complex, large-scale quantum systems is essential for
develo** quantum technologies. We present an efficient method for constructing an …

We study the performance of classical and quantum machine learning (ML) models in
predicting outcomes of physical experiments. The experiments depend on an input …

Advances in isolating, controlling and entangling quantum systems are transforming what
was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and …

Quantum computing crucially relies on the ability to efficiently characterize the quantum
states output by quantum hardware. Conventional methods which probe these states …

We propose a tomographic protocol for estimating any k-body reduced density matrix (k-
RDM) of an n-mode fermionic state, a ubiquitous step in near-term quantum algorithms for …

The Poisson equation has wide applications in many areas of science and engineering.
Although there are some quantum algorithms that can efficiently solve the Poisson equation …

Hamiltonian formulation of lattice gauge theories (LGTs) is the most natural framework for
the purpose of quantum simulation, an area of research that is growing with advances in …

Given copies of a quantum state ρ, a shadow tomography protocol aims to learn all
expectation values from a fixed set of observables, to within a given precision ε. We say that …