Microsoft Azure Quantum
If you eventually thought Microsoft would not play in the Quantum sandbox and let IBM and Google have fun alone, Satya Nadella, Microsoft CEO made the show during the Vision Keynote at Ignite. Microsoft provides
Quantum optimization with a Novel Gibbs objective function and Ansatz Architecture Search
The Quantum Approximate Optimization Algorithm (QAOA) is a standard method for combinatorial optimization with a gate-based quantum computer. The QAOA consists of a particular ansatz for the quantum circuit architecture, together with a prescription for
Xanadu PennyLane now has support for Google AI Cirq
PennyLane, Xanadu’s software for quantum machine learning & optimization of hybrid quantum-classical computations, now has support for Google AI Cirq via the new PennyLane-Cirq plugin! Cirq is an open-source framework for Noisy Intermediate Scale Quantum (NISQ) computers. Combine
Learning adiabatic quantum algorithms for solving optimization problems
An adiabatic quantum algorithm is essentially given by three elements: An initial Hamiltonian with known ground state, a problem Hamiltonian whose ground state corresponds to the solution of the given problem and an evolution schedule
Case Study: Quantum Approximate Optimization Algorithm (QAOA)
Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising quantum algorithms for the Noisy Intermediate-Scale Quantum (NISQ) era. Quantifying the performance of QAOA in the near-term regime is of utmost importance. A team
Near-term quantum algorithms for linear systems of equations
Solving linear systems of equations is an essential component in science and technology, including in many machine learning algorithms. Existing quantum algorithms have demonstrated large speedups in solving linear systems, but the required quantum resources
Calculation of the Green’s function on near-term quantum computers
The Green’s function plays a crucial role to study the nature of quantum many-body systems, especially strongly-correlated systems. Although the development of quantum computers in near term is expected to enable us to compute energy
Improved Boltzmann machines with error corrected quantum annealing
Boltzmann machines are the basis of several deep learning methods that have been successfully applied to both supervised and unsupervised machine learning tasks. These models assume that a dataset is generated according to a Boltzmann
Quantum error detection improves accuracy of chemical calculations on a quantum computer
The ultimate goal of quantum error correction is to achieve the fault-tolerance threshold beyond which quantum computers can be made arbitrarily accurate. This requires extraordinary resources and engineering efforts. The researchers at Lawrence Berkeley National
Barclays demonstrates proof-of-concept quantum clearing algorithm
A team of researchers from Barclays’ chief technology and innovation office, in collaboration with IBM, have published a paper describing a proof-of-concept quantum optimised application. The paper, Quantum algorithms for mixed binary optimization, published earlier in October,