[HTML][HTML] The variational quantum eigensolver: a review of methods and best practices
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 …
received significant attention from the research community in recent years. It uses the …
Noisy intermediate-scale quantum algorithms
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 …
integer factorization and unstructured database search requires millions of qubits with low …
Exploiting symmetry in variational quantum machine learning
Variational quantum machine learning is an extensively studied application of near-term
quantum computers. The success of variational quantum learning models crucially depends …
quantum computers. The success of variational quantum learning models crucially depends …
Emerging quantum computing algorithms for quantum chemistry
Digital quantum computers provide a computational framework for solving the Schrödinger
equation for a variety of many‐particle systems. Quantum computing algorithms for the …
equation for a variety of many‐particle systems. Quantum computing algorithms for the …
Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation
Quantum computing is a game-changing technology for global academia, research centers
and industries including computational science, mathematics, finance, pharmaceutical …
and industries including computational science, mathematics, finance, pharmaceutical …
Local, expressive, quantum-number-preserving VQE ansätze for fermionic systems
We propose VQE circuit fabrics with advantageous properties for the simulation of strongly
correlated ground and excited states of molecules and materials under the Jordan–Wigner …
correlated ground and excited states of molecules and materials under the Jordan–Wigner …
Matrix-model simulations using quantum computing, deep learning, and lattice monte carlo
Matrix quantum mechanics plays various important roles in theoretical physics, such as a
holographic description of quantum black holes, and it underpins the only practical …
holographic description of quantum black holes, and it underpins the only practical …
Progress toward larger molecular simulation on a quantum computer: Simulating a system with up to 28 qubits accelerated by point-group symmetry
The exact evaluation of the molecular ground state in quantum chemistry requires an
exponentially increasing computational cost. Quantum computation is a promising way to …
exponentially increasing computational cost. Quantum computation is a promising way to …
Deep variational quantum eigensolver: a divide-and-conquer method for solving a larger problem with smaller size quantum computers
We propose a divide-and-conquer method for the quantum-classical hybrid algorithm to
solve larger problems with small-scale quantum computers. Specifically, we concatenate a …
solve larger problems with small-scale quantum computers. Specifically, we concatenate a …
Quantum computation of dominant products in lithium–sulfur batteries
Quantum chemistry simulations of some industrially relevant molecules are reported,
employing variational quantum algorithms for near-term quantum devices. The energies and …
employing variational quantum algorithms for near-term quantum devices. The energies and …