For quantum computers to successfully solve real-world problems, it is necessary to tackle
the challenge of noise: the errors that occur in elementary physical components due to …

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 …

We present a method for mitigating measurement errors on quantum computing platforms
that does not form the full assignment matrix, or its inverse, and works in a subspace defined …

Quantum error mitigation has been proposed as a means to combat unwanted and
unavoidable errors in near-term quantum computing without the heavy resource overheads …

The vacuum of the lattice Schwinger model is prepared on up to 100 qubits of IBM's Eagle-
processor quantum computers. A new algorithm to prepare the ground state of a gapped …

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 …

Data representation is crucial for the success of machine-learning models. In the context of
quantum machine learning with near-term quantum computers, equally important …

During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …

We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …

The inevitable accumulation of errors in near-future quantum devices represents a key
obstacle in delivering practical quantum advantages, motivating the development of various …