Estimating the gradient and higher-order derivatives on quantum hardware

By Andrea Mari, Thomas R. Bromley, Nathan Killoran. Physical Review A, 2020

163 citations recorded by the source corpus.

The authors show how to evaluate, with near-term quantum computers, high-order derivatives of expectation values with respect to the variational parameters of quantum circuits. The authors also study how such derivatives are affected by statistical noise. Their theoretical results are then tested with both numerical simulations and experiments.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1d568e892337198a8cbd7a1f7879c6ff4886ec0a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lennart Bittel
2. Martin Kliesch
3. David Wierichs
4. Cedric Yen-Yu Lin
5. Cody Wang
6. Josh A. Izaac

In Computational Theory and Mathematics

1. Martin Kliesch
2. Lennart Bittel
3. Josh A. Izaac

Citations in the corpus

• Quantum variational algorithms are swamped with traps (2022, unknown): “[41, 56] both showed that, in an overparameterized regime, these models experience good local minima, though this transition to trainability typically occurs at an intractable number of parameters.”
• Training Variational Quantum Algorithms Is NP-Hard. (2021, background): “Second order methods require significant overhead in the number of measurements but can yield better accuracy [9].”
• Variational quantum algorithms (2020, unknown): “Explicit formulas can be found in [67, 68].”
• General parameter-shift rules for quantum gradients (2021, methodology): “[46].”