Quantum algorithms offer significant speed-ups over their classical counterparts for a variety
of problems. The strongest arguments for this advantage are borne by algorithms for …

Quantum state preparation is an important subroutine for quantum computing. We show that
any n-qubit quantum state can be prepared with a Θ (n)-depth circuit using only single-and …

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 …

Quantum Random Access Memory (QRAM) has the potential to revolutionize the area of
quantum computing. QRAM uses quantum computing principles to store and modify …

Large machine learning models are revolutionary technologies of artificial intelligence
whose bottlenecks include huge computational expenses, power, and time used both in the …

In this paper we propose the Quantum Data Center (QDC), an architecture combining
Quantum Random Access Memory (QRAM) and quantum networks. We give a precise …

Quantum random-access memory (QRAM) is a mechanism to access data (quantum or
classical) based on addresses which are themselves a quantum state. QRAM has a long …

How to efficiently encode classical data is a fundamental task in quantum computing. While
many existing works treat classical data encoding as a black box in oracle-based quantum …

Quantum computing promises to provide the next step up in computational power for diverse
application areas. In this review, we examine the science behind the quantum hype and …

We provide a modular circuit-level implementation and resource estimates for several
methods of block-encoding a dense matrix of classical data to precision; the minimal-depth …