Jarrod R. McClean

Also known as Jarrod McClean

Jarrod McClean is a senior staff research scientist at Google Quantum AI, where he works on quantum algorithms and applications, including quantum models for machine learning. Raised in Arizona, he earned a chemistry degree at UC Berkeley in 2010 and a chemical physics PhD at Harvard in 2015 under Alán Aspuru-Guzik, then became the 2015 Luis Alvarez Fellow in Computing Science at Lawrence Berkeley National Laboratory. In 2016 he extended the theory of variational hybrid quantum-classical algorithms, and in 2018 his paper on barren plateaus showed that for a wide class of parameterized quantum circuits the chance of a non-zero gradient shrinks exponentially with the number of qubits. He was among the authors of Google's 2019 quantum supremacy experiment and of the 2022 Science paper "Quantum advantage in learning from experiments." In 2025 he coauthored a Nature Communications paper establishing an exponential quantum advantage for learning periodic neurons and helped lead a study reporting generative quantum advantage on a 68-qubit processor.

Now at: Google

Timeline

2010–2015

A Harvard PhD in chemical physics

Born in Texas and raised in Arizona, he earns a B.S. in chemistry at UC Berkeley in 2010, where he works in William Lester's group. He completes a Ph.D. in chemical physics at Harvard in 2015 under Alán Aspuru-Guzik, supported by a Department of Energy Computational Science Graduate Fellowship.
2015

Alvarez Fellow at Berkeley Lab

In 2015, he becomes the Luis Alvarez Fellow in Computing Science at Lawrence Berkeley National Laboratory. His work includes the design and analysis of new algorithms for quantum computing in quantum chemistry and close collaboration with groups developing new quantum hardware.
2016

Theory of variational hybrid algorithms

In 2016, he publishes "The theory of variational hybrid quantum-classical algorithms" in New Journal of Physics with Jonathan Romero, Ryan Babbush and Alán Aspuru-Guzik. The paper extends the theory of the quantum variational eigensolver, develops a variational adiabatic ansatz, and shows that modern derivative-free optimizers can save up to three orders of magnitude in computation.
2018

Barren plateaus in quantum circuits

In 2018, he publishes "Barren plateaus in quantum neural network training landscapes" in Nature Communications with Sergio Boixo, Vadim Smelyanskiy, Ryan Babbush and Hartmut Neven. The paper shows that for a wide class of parameterized quantum circuits, the probability of a non-zero gradient to fixed precision is exponentially small in the number of qubits.
2019

The quantum supremacy experiment

As a member of Google AI Quantum, he is one of the authors of the 2019 Nature paper "Quantum supremacy using a programmable superconducting processor." The Sycamore processor creates quantum states on 53 qubits and samples a circuit a million times in about 200 seconds, a task the authors estimate would take a state-of-the-art classical supercomputer approximately 10,000 years.
2022

Learning from fewer experiments

In 2022, he is one of eleven coauthors, with Hsin-Yuan Huang, Michael Broughton, John Preskill and others, of "Quantum advantage in learning from experiments" in Science. The paper proves that quantum machines can learn from exponentially fewer experiments than conventional setups, and demonstrates a quantum advantage with up to 40 superconducting qubits and 1300 quantum gates.
2025

Quantum advantage for neural networks

In March 2025, he, Laura Lewis and Dar Gilboa post a theory study of an efficient quantum algorithm for learning periodic neurons, which Nature Communications publishes in December 2025 as an exponential quantum advantage over classical gradient-based methods. In September 2025, a study that he co-leads with Hsin-Yuan Huang, Michael Broughton, Hartmut Neven and Ryan Babbush reports quantum models for generative learning, tested on a 68-qubit superconducting processor.
As of October 2026

Senior staff scientist at Quantum AI

He is a senior staff research scientist at the Google Quantum AI Laboratory, and his research interests include quantum computation, quantum chemistry, numerical methods and information sparsity. In April 2026, he is among the Google Quantum AI authors of a study with Caltech and MIT researchers, showing that small quantum computers could process massive datasets more efficiently than far larger classical systems.