Noise is the central obstacle to building large-scale quantum computers. Quantum systems with sufficiently uncorrelated and weak noise could be used to solve computational problems that are intractable with current digital computers. There has been substantial progress towards engineering such systems. ...
Deep reinforcement learning is an emerging machine-learning approach that can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate
generating realistic charge conductance signals from medium, more than 10 quantum dot arrays. By levering the polytope finding algorithm fromO. Krause, A. Chatterjee, F. Kuemmeth and E. van Nieuwenburg, Learning coulomb diamonds in large quantum dot arrays, SciPost Physics 13(4), 084 (2022)...
Through a collaborative approach, we will bring together experts in quantum computing, machine learning, and interdisciplinary fields. The initiative will foster research and development to push the boundaries of what's possible in AI, unlocking new realms of computation and problem-solving....
Federated learning refers to the task of performing machine learning with decentralized data from multiple clients while protecting data security and privacy. Works have been done to incorporate quantum advantage in such scenarios. However, when the clients’ data are not independent and identically dist...
We propose a surrogate-based method for optimizing parameterized quantum circuits which is designed to operate with few calls to a quantum computer. We employ a computationally inexpensive classical surrogate to approximate the cost function of a variational quantum algorithm. An initial surrogate is fit...
We study the problem of learning a real-valued function of correlated variables. Solving this problem is of interest since many classical learning results apply only in the case of learning functions of random variables that are independent. We show how to recover a high-dimensional, sparse monomi...
Reinforcement learning models, the backbone of decision-making AI, often falter when faced with minor task variations. For instance, a model managing city traffic might struggle with intersections differing in speed limits or lane numbers. To address this, researchers at MIT have developed a groundbr...
We address the question of efficient implementation of quantum protocols, with small communication and entanglement, and short depth circuit for encoding or decoding. We introduce two methods for this; the first constructs a resource-efficient convex-split lemma and the second adapts the technique of...
I present a method for estimating the fidelity F(μ, τ) between a preparable quantum state μ and a classically specified pure target state τ=|τ⟩⟨τ|, using simple quantum circuits and on-the-fly classical calculation (or lookup) of selected amplitudes of |τ⟩. The method is...