A review on quantum approximate optimization algorithm and its variants
Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization problems that …
variational quantum algorithm that aims to solve combinatorial optimization problems that …
Quantum-centric supercomputing for materials science: A perspective on challenges and future directions
Computational models are an essential tool for the design, characterization, and discovery
of novel materials. Computationally hard tasks in materials science stretch the limits of …
of novel materials. Computationally hard tasks in materials science stretch the limits of …
Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware
Quantum computers may provide good solutions to combinatorial optimization problems by
leveraging the Quantum Approximate Optimization Algorithm (QAOA). The QAOA is often …
leveraging the Quantum Approximate Optimization Algorithm (QAOA). The QAOA is often …
Benchmarking quantum logic operations relative to thresholds for fault tolerance
Contemporary methods for benchmarking noisy quantum processors typically measure
average error rates or process infidelities. However, thresholds for fault-tolerant quantum …
average error rates or process infidelities. However, thresholds for fault-tolerant quantum …
Design and execution of quantum circuits using tens of superconducting qubits and thousands of gates for dense Ising optimization problems
We develop a hardware-efficient ansatz for variational optimization, derived from existing
ansatzes in the literature, that parametrizes subsets of all interactions in the cost Hamiltonian …
ansatzes in the literature, that parametrizes subsets of all interactions in the cost Hamiltonian …
Pauli noise learning for mid-circuit measurements
Current benchmarks for midcircuit measurements (MCMs) are limited in scalability or the
types of error they can quantify, necessitating new techniques for quantifying MCM …
types of error they can quantify, necessitating new techniques for quantifying MCM …
Solving non-native combinatorial optimization problems using hybrid quantum-classical algorithms
Combinatorial optimization is a challenging problem applicable in a wide range of fields
from logistics to finance. Recently, quantum computing has been used to attempt to solve …
from logistics to finance. Recently, quantum computing has been used to attempt to solve …
Superstaq: Deep optimization of quantum programs
C Campbell, FT Chong, D Dahl… - 2023 IEEE …, 2023 - ieeexplore.ieee.org
We describe Superstaq, a quantum software platform that optimizes the execution of
quantum programs by tailoring to underlying hardware primitives. For benchmarks such as …
quantum programs by tailoring to underlying hardware primitives. For benchmarks such as …
A practical introduction to benchmarking and characterization of quantum computers
Rapid progress in quantum technology has transformed quantum computing and quantum
information science from theoretical possibilities into tangible engineering challenges …
information science from theoretical possibilities into tangible engineering challenges …
Efficient generation of multi-partite entanglement between non-local superconducting qubits using classical feedback
Quantum entanglement is one of the primary features which distinguishes quantum
computers from classical computers. In gate-based quantum computing, the creation of …
computers from classical computers. In gate-based quantum computing, the creation of …