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 …

At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …

Quantum computing promises to offer substantial speed-ups over its classical counterpart for
certain problems. However, the greatest impediment to realizing its full potential is noise that …

We report an accurate and efficient classical simulation of a kicked Ising quantum system on
the heavy hexagon lattice. A simulation of this system was recently performed on a 127-qubit …

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 …

For the first time in history, we are seeing a branching point in computing paradigms with the
emergence of quantum processing units (QPUs). Extracting the full potential of computation …

The holographic principle, theorized to be a property of quantum gravity, postulates that the
description of a volume of space can be encoded on a lower-dimensional boundary. The …

Noise in quantum computers can result in biased estimates of physical observables.
Accurate bias-free estimates can be obtained using probabilistic error cancellation, an error …

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 …

Quantum simulation traditionally relies on unitary dynamics, inherently imposing efficiency
constraints on the generation of intricate entangled states. In principle, these limitations can …