Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …

The difficulty of simulating quantum systems, well known to quantum chemists, prompted the
idea of quantum computation. One can avoid the steep scaling associated with the exact …

The success of modern deep learning hinges on the ability to train neural networks at scale.
Through clever reuse of intermediate information, backpropagation facilitates training …

The parameter-shift rule is an approach to measuring gradients of quantum circuits with
respect to their parameters, which does not require ancilla qubits or controlled operations …

We consider a generic framework of optimization algorithms based on gradient descent. We
develop a quantum algorithm that computes the gradient of a multi-variate realvalued …

Quantum machine learning and optimization are exciting new areas that have been brought
forward by the breakthrough quantum algorithm of Harrow, Hassidim, and Lloyd for solving …

Large language models (LLMs) have demonstrated exceptional performance across a wide
range of tasks. These models, powered by advanced deep learning techniques, have …

Many quantum algorithms involve the evaluation of expectation values. Optimal strategies
for estimating a single expectation value are known, requiring a number of state …

We describe algorithms to obtain an approximate classical description of ad-dimensional
quantum state when given access to a unitary (and its inverse) that prepares it. For pure …

Within a natural black-box setting, we exhibit a simple optimization problem for which a
quantum variational algorithm that measures analytic gradients of the objective function with …