About

I'm Schlumberger Centennial Chair of Computer Science at The University of Texas at Austin, and director of its Quantum Information Center. My research interests center around the capabilities and limits of quantum computers, and computational complexity theory more generally.

Research topics

• Computer Science
• Artificial Intelligence
• Programming language
• Mathematical economics
• Quantum mechanics
• Mathematics
• Physics
• Theoretical computer science

Selected publications

• The Effectiveness of Transcranial Magnetic Stimulation for Major Depressive Disorder: Associations With Age and Biological Sex

  Transcranial magnetic stimulation . · 2026-05-18

  article
  PublisherDOI

• Future of quantum computing

  Quantum Machine Intelligence · 2026-01-20

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Real-World Antidepressant Outcomes of Transcranial Magnetic Stimulation in Adolescents Aged 12–14

  Transcranial magnetic stimulation . · 2026-05-18

  article
  PublisherDOI

• The impact of treatment gaps on effectiveness of Transcranial Magnetic Stimulation in Major Depressive Disorder

  Brain stimulation · 2025-01-01

  articleOpen accessSenior author

  Psychiatric symptoms such as apathy, depression, anxiety, and impulsivecompulsive behaviors (ICBs) are prevalent in Parkinson's disease (PD) and greatly affect patients' quality of life.While deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective for PD motor symptoms and shows efficacy in treating obsessive-compulsive disorder (OCD), the pathophysiological mechanisms underlying psychiatric symptoms in PD, particularly following DBS, remain unclear.This study aims to identify resting-state electrophysiological biomarkers within the STN that are linked to these psychiatric symptoms in PD patients.We conducted perioperative recordings of resting-state STN local field potentials (LFPs) and frontal electroencephalograms (EEGs) in PD patients undergoing DBS surgery (n 47, eye-closed; n 50, eye-open).Psychiatric symptoms were evaluated using the Apathy Evaluation Scale (AES), Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), UPPS-P Impulsive Behavior Scale, Questionnaire for Impulsive-Compulsive Disorders in PD (QUIPS), and Obsessive-Compulsive Inventory-Revised (OCI-R).Motor symptoms were assessed with the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) in both off and on-medication states.Fiveminute recordings were taken with eyes closed and open during the onmedication state.Electrophysiological analyses encompassed spectral features, coherence, and cortical-STN phase-amplitude coupling (PAC).Results showed positive correlations between BDI-II scores and a band power.AES scores were positively correlated with high b power and negatively correlated with frontal-STN q and b coherence and q-b PAC.Total UPPS scores were negatively correlated with low g power, while OCI-R scores were negatively correlated with d and low g power.Additionally, motor severity correlated with peak low b activity.These associations remained significant after adjusting for UPDRS-III scores, levodopa equivalent daily dose (LEED), and other variables.These findings highlight distinct resting-state electrophysiological signatures associated with psychiatric symptoms in PD, offering potential biomarkers for neuromodulation in psychiatric disorders.

  PublisherOA PDFDOI

• Certified randomness using a trapped-ion quantum processor

  Nature · 2025-03-26 · 23 citations

  articleOpen access

  Abstract Although quantum computers can perform a wide range of practically important tasks beyond the abilities of classical computers 1,2 , realizing this potential remains a challenge. An example is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy 3 . Certified randomness has many applications but is impossible to achieve solely by classical computation. Here we demonstrate the generation of certifiably random bits using the 56-qubit Quantinuum H2-1 trapped-ion quantum computer accessed over the Internet. Our protocol leverages the classical hardness of recent random circuit sampling demonstrations 4,5 : a client generates quantum ‘challenge’ circuits using a small randomness seed, sends them to an untrusted quantum server to execute and verifies the results of the server. We analyse the security of our protocol against a restricted class of realistic near-term adversaries. Using classical verification with measured combined sustained performance of 1.1 × 10 18 floating-point operations per second across multiple supercomputers, we certify 71,313 bits of entropy under this restricted adversary and additional assumptions. Our results demonstrate a step towards the practical applicability of present-day quantum computers.

  PublisherOA PDFDOI

• Wide Neural Networks as a Baseline for the Computational No-Coincidence Conjecture

  ArXiv.org · 2025-10-08

  preprintOpen accessSenior author

  We establish that randomly initialized neural networks, with large width and a natural choice of hyperparameters, have nearly independent outputs exactly when their activation function is nonlinear with zero mean under the Gaussian measure: $\mathbb{E}_{z \sim \mathcal{N}(0,1)}[σ(z)]=0$. For example, this includes ReLU and GeLU with an additive shift, as well as tanh, but not ReLU or GeLU by themselves. Because of their nearly independent outputs, we propose neural networks with zero-mean activation functions as a promising candidate for the Alignment Research Center's computational no-coincidence conjecture -- a conjecture that aims to measure the limits of AI interpretability.

  PublisherOA PDFDOI

• Demonstrating an unconditional separation between quantum and classical information resources

  ArXiv.org · 2025-09-08

  preprintOpen accessSenior author

  A longstanding goal in quantum information science is to demonstrate quantum computations that cannot be feasibly reproduced on a classical computer. Such demonstrations mark major milestones: they showcase fine control over quantum systems and are prerequisites for useful quantum computation. To date, quantum advantage has been demonstrated, for example, through violations of Bell inequalities and sampling-based quantum supremacy experiments. However, both forms of advantage come with important caveats: Bell tests are not computationally difficult tasks, and the classical hardness of sampling experiments relies on unproven complexity-theoretic assumptions. Here we demonstrate an unconditional quantum advantage in information resources required for a computational task, realized on Quantinuum's H1-1 trapped-ion quantum computer operating at a median two-qubit partial-entangler fidelity of 99.941(7)%. We construct a task for which the most space-efficient classical algorithm provably requires between 62 and 382 bits of memory, and solve it using only 12 qubits. Our result provides the most direct evidence yet that currently existing quantum processors can generate and manipulate entangled states of sufficient complexity to access the exponentiality of Hilbert space. This form of quantum advantage -- which we call quantum information supremacy -- represents a new benchmark in quantum computing, one that does not rely on unproven conjectures.

  PublisherOA PDFDOI

• Comparison of the PHQ-9 and QIDS-SR in assessing the antidepressant effects of Transcranial Magnetic Stimulation: sensitivity to change

  Brain stimulation · 2025-01-01

  articleOpen access

  In Attention Deficit Hyperactivity Disorder (ADHD), it is well known that cognitive functions such as attention and working memory are disrupted.There are contradictory results in the literature regarding the non-invasive neuromodulation of ADHD subjects to improve their disrupted attention and related cognitive functions.Based on earlier studies that has demonstrated the efficacy of individualized theta transcranial alternating current stimulation in enhancing working memory capacity in healthy young adults, here, we aimed to investigate the effectiveness of individualized theta tACS on cognitive functions and related-EEG features in adult ADHD subjects.27 adults who met the clinical criteria for ADHD were included in the study and randomly assigned to two stimulation groups: active stimulation (ITF-1 Hz tACS) and sham.Neuropsychological tests that assess mainly attention and memory and EEG recording in resting-state and during a visual memory task were obtained before and after the tACS session.Over the left fronto-parietal network, each participant received a 20-min tACS session at 1 Hz slower than their individual theta frequency (ITF-1 Hz).In the post-EEG measurement; while there is a statistically significant decrease in fronto-central theta power in sham group (p<0.05),theta power remained the same in the active stimulation group (p>0.05).The number of remembered items in the visual memory task was significantly increased in the active stimulation group after tACS, in contrast, no statistically significant increase was seen in sham group.The results of the current study indicate that individualized theta tACS protocol might be a promising non-invasive neuromodulation technique in the treatment of ADHD by improving the attention and related cognitive functions.

  PublisherOA PDFDOI

• How much to give and when to give again: Dosing the acute TMS course and retreatment following relapse

  Brain stimulation · 2025-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• The impact of TMS on symptomatology in Major Depressive Disorder: What is being changed?

  Brain stimulation · 2025-01-01

  articleOpen access
  PublisherOA PDFDOI

Recent grants

• 2012 Waterman Award

  NSF · $1.0M · 2012–2017

• CAREER: Efficient Computation in the Physical World

  NSF · $579k · 2009–2014

Frequent coauthors

• A. G. White

  University of Queensland

  21 shared

• Alessandro Fedrizzi

  Heriot-Watt University

  20 shared

• Matthew A. Broome

  20 shared

• Andrew Drucker

  19 shared

• Elad Hazan

  Google (United States)

  18 shared

• Satyen Kale

  17 shared

• Andris Ambainis

  17 shared

• Xinyi Chen

  Shenzhen Research Institute of Big Data

  17 shared

Labs

• Quantum Information Center - UT AustinPI

Education

• Ph.D., Theoretical Computer Science

  Massachusetts Institute of Technology (MIT)

  2006

• B.S., Computer Science

  University of Texas at Austin

Awards & honors

• 2018 - Tomassoni-Chisesi Award
• 2016 - Vannevar Bush Faculty Fellowship
• 2015 - IT from Qubit: Simons Collaboration on Quantum Fields…
• 2012 - Alan T. Waterman Award of the National Science Founda…
• 2011 - Best Paper, International Computer Science Symposium…