Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 186 publications
Efficient Quantum Computation of Molecular Forces and Other Energy Gradients
Thomas E O'Brien
Michael Streif
Raffaele Santagati
Yuan Su
William J. Huggins
Joshua Goings
Nikolaj Moll
Elica Kyoseva
Matthias Degroote
Christofer Tautermann
Joonho Lee
Dominic Berry
Nathan Wiebe
Physical Review Research, 4 (2022), pp. 043210
Preview abstract
While most work on the quantum simulation of chemistry has focused on computing energy surfaces, a similarly important application requiring subtly different algorithms is the computation of energy derivatives. Almost all molecular properties can be expressed an energy derivative, including molecular forces, which are essential for applications such as molecular dynamics simulations. Here, we introduce new quantum algorithms for computing molecular energy derivatives with significantly lower complexity than prior methods. Under cost models appropriate for noisy-intermediate scale quantum devices we demonstrate how low rank factorizations and other tomography schemes can be optimized for energy derivative calculations. We perform numerics revealing that our techniques reduce the number of circuit repetitions required by many orders of magnitude for even modest systems. In the context of fault-tolerant algorithms, we develop new methods of estimating energy derivatives with Heisenberg limited scaling incorporating state-of-the-art techniques for block encoding fermionic operators. Our results suggest that the calculation of forces on a single nuclei may be of similar cost to estimating energies of chemical systems, but that further developments are needed for quantum computers to meaningfully assist with molecular dynamics simulations.
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Quantum Computation of Molecular Structure using Data from Challenging-to-Classically-Simulate Nuclear Magnetic Resonance Experiments
Thomas E O'Brien
Yuan Su
David Fushman
Vadim Smelyanskiy
PRX Quantum, 3 (2022)
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We propose a quantum algorithm for inferring the molecular nuclear spin Hamiltonian from time-resolved measurements of spin-spin correlators, which can be obtained via nuclear magnetic resonance (NMR). We focus on learning the anisotropic dipolar term of the Hamiltonian, which generates dynamics that are challenging to classically simulate in some contexts. We demonstrate the ability to directly estimate the Jacobian and Hessian of the corresponding learning problem on a quantum computer, allowing us to learn the Hamiltonian parameters. We develop algorithms for performing this computation on both noisy near-term and future fault-tolerant quantum computers. We argue that the former is promising as an early beyond-classical quantum application since it only requires evolution of a local spin Hamiltonian. We investigate the example of a protein (ubiquitin) confined on a membrane as a benchmark of our method. We isolate small spin clusters, demonstrate the convergence of our learning algorithm on one such example, and then investigate the learnability of these clusters as we cross the ergodic to nonergodic phase transition by suppressing the dipolar interaction. We see a clear correspondence between a drop in the multifractal dimension measured across many-body eigenstates of these clusters, and a transition in the structure of the Hessian of the learning cost function (from degenerate to learnable). Our hope is that such quantum computations might enable the interpretation and development of new NMR techniques for analyzing molecular structure.
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Nearly Optimal Quantum Algorithm for Estimating Multiple Expectation Values
William J. Huggins
Kianna Wan
Thomas E O'Brien
Nathan Wiebe
Physical Review Letters, 129 (2022), pp. 240501
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Many quantum algorithms involve the evaluation of expectation values. Optimal strategies for estimating a single expectation value are known, requiring a number of iterations that scales with the target error $\epsilon$ as $\mathcal{O}(\epsilon^{-1})$. In this paper we address the task of estimating the expectation values of \(M\) different observables, each to within an error \(\epsilon\), with the same \(\epsilon^{-1}\) dependence. We describe an approach that leverages Gily\'{e}n \emph{et al.}'s~quantum gradient estimation algorithm to achieve $\mathcal{O}\sqrt{M}\epsilon^{-1})$ scaling up to logarithmic factors, regardless of the commutation properties of the $M$ observables.
We prove that this scaling is optimal in the worst case, even when the operators are mutually commuting. We highlight the flexibility of our approach by presenting several generalizations, including a strategy for accelerating the estimation of a collection of dynamic correlation functions.
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Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway
Sharad Shriram
Nidhin Vaidhiyan
Gaurav Aggarwal
Jiangzhuo Chen
Srini Venkatramanan
Lijing Wang
Aniruddha Adiga
Adam Sadilek
Madhav Marathe
Rajesh Sundaresan
AMAAS 2021 (2021), pp. 1680
Preview abstract
The Mumbai Suburban Railways, \emph{locals}, are a key transit infrastructure of the city and is crucial for resuming normal economic activity. To reduce disease transmission, policymakers can enforce reduced crowding and mandate wearing of masks. \emph{Cohorting} -- forming groups of travelers that always travel together, is an additional policy to reduce disease transmission on \textit{locals} without severe restrictions. Cohorting allows us to: (i) form traveler bubbles, thereby decreasing the number of distinct interactions over time; (ii) potentially quarantine an entire cohort if a single case is detected, making contact tracing more efficient, and (iii) target cohorts for testing and early detection of symptomatic as well as asymptomatic cases. Studying impact of cohorts using compartmental models is challenging because of the ensuing representational complexity. Agent-based models provide a natural way to represent cohorts along with the representation of the cohort members with the larger social network. This paper describes a novel multi-scale agent-based model to study the impact of cohorting strategies on COVID-19 dynamics in Mumbai. We achieve this by modeling the Mumbai urban region using a detailed agent-based model comprising of 12.4 million agents. Individual cohorts and their inter-cohort interactions as they travel on locals are modeled using local mean field approximations. The resulting multi-scale model in conjunction with a detailed disease transmission and intervention simulator is used to assess various cohorting strategies. The results provide a quantitative trade-off between cohort size and its impact on disease dynamics and well being. The results show that cohorts can provide significant benefit in terms of reduced transmission without significantly impacting ridership and or economic \& social activity.
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What the foundations of quantum computer science teach us about chemistry
Joonho Lee
Thomas E O'Brien
William J. Huggins
Hsin-Yuan Huang
Journal of Chemical Physics, 155 (2021), pp. 150901
Preview abstract
With the rapid development of quantum technology, one of the leading applications that has been identified is the simulation of chemistry. Interestingly, even before full scale quantum computers are available, quantum computer science has exhibited a remarkable string of results that directly impact what is possible in chemical simulation, even with a quantum computer. Some of these results even impact our understanding of chemistry in the real world. In this perspective, we take the position that direct chemical simulation is best understood as a digital experiment. While on one hand this clarifies the power of quantum computers to extend our reach, it also shows us the limitations of taking such an approach too directly. Leveraging results that quantum computers cannot outpace the physical world, we build to the controversial stance that some chemical problems are best viewed as problems for which no algorithm can deliver their solution in general, known in computer science as undecidable problems. This has implications for the predictive power of thermodynamic models and topics like the ergodic hypothesis. However, we argue that this perspective is not defeatist, but rather helps shed light on the success of existing chemical models like transition state theory, molecular orbital theory, and thermodynamics as models that benefit from data. We contextualize recent results showing that data-augmented models are more powerful rote simulation. These results help us appreciate the success of traditional chemical theory and anticipate new models learned from experimental data. Not only can quantum computers provide data for such models, but they can extend the class and power of models that utilize data in fundamental ways. These discussions culminate in speculation on new ways for quantum computing and chemistry to interact and our perspective on the eventual roles of quantum computers in the future of chemistry.
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Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Joel Shor
Jordan R. Green
Interspeech, Interspeech 2021 (2021) (to appear)
Preview abstract
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of a speech impairment. Classification approaches can also help identify hard-to-recognize speech samples to teach ASR systems about the variable manifestations of impaired speech. Here, we develop and compare different deep learning techniques to classify the intelligibility of disordered speech on selected phrases. We collected samples from a diverse set of 661 speakers with a variety of self-reported disorders speaking 29 words or phrases, which were rated by speech-language pathologists for their overall intelligibility using a five-point Likert scale. We then evaluated classifiers developed using 3 approaches: (1) a convolutional neural network (CNN) trained for the task, (2) classifiers trained on non-semantic speech representations from CNNs that used an unsupervised objective [1], and (3) classifiers trained on the acoustic (encoder) embeddings from an ASR system trained on typical speech [2]. We find that the ASR encoder’s embeddings considerably outperform the other two on detecting and classifying disordered speech. Further analysis shows that the ASR embeddings cluster speech by the spoken phrase, while the non-semantic embeddings cluster speech by speaker. Also, longer phrases are more indicative of intelligibility deficits than single words.
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Disordered Speech Data Collection: Lessons Learned at 1 Million Utterances from Project Euphonia
Bob MacDonald
Rus Heywood
Richard Cave
Katie Seaver
Marilyn Ladewig
Jordan R. Green
Interspeech (2021) (to appear)
Preview abstract
Speech samples from over 1000 individuals with impaired speech have been submitted for Project Euphonia, aimed at improving automated speech recognition for atypical speech. We provide an update on the contents of the corpus, which recently passed 1 million utterances, and review key lessons learned from this project.
The reasoning behind decisions such as phrase set composition, prompted vs extemporaneous speech, metadata and data quality efforts are explained based on findings from both technical and user-facing research.
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Scanning Electron Microscopes (SEM) and Dual Beam Focused Ion Beam Microscopes (FIB-SEM) are essential tools used in the semiconductor industry and in relation to this work, for wafer inspection in the production of hard drives at Seagate. These microscopes provide essential metrology during the build and help determine process bias and control. However, these microscopes will naturally drift out of focus over time, and if not immediately detected the consequences of this include: incorrect measurements, scrap, wasted resources, tool down time and ultimately delays in production.
This paper presents an automated solution that uses deep learning to remove anomalous images and determine the degree of blurriness for SEM and FIB-SEM images. Since its first deployment, the first of its kind at Seagate, it has replaced the need for manual inspection on the covered processes and mitigated delays in production, realizing return on investment in the order of millions of US dollars annually in both cost savings and cost avoidance.
The proposed solution can be broken into two deep learning steps. First, we train a deep convolutional neural network, a RetinaNet object detector, to detect and locate a Region Of Interest (ROI) containing the main feature of the image. For the second step, we train another deep convolutional neural network using the ROI, to determine the sharpness of the image. The second model identifies focus level based on a training dataset consisting of synthetically degraded in- focus images, based on work by Google Research, achieving up to 99.3% test set accuracy.
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Exponential suppression of bit or phase flip errors with repetitive quantum error correction
Alan Derk
Alan Ho
Alex Opremcak
Alexander Korotkov
Alexandre Bourassa
Andre Gregory Petukhov
Andrew Dunsworth
Anthony Megrant
Bálint Pató
Benjamin Chiaro
Brooks Riley Foxen
Charles Neill
Cody Jones
Daniel Eppens
Dave Landhuis
Doug Strain
Edward Farhi
Eric Ostby
Fedor Kostritsa
Frank Carlton Arute
Igor Aleiner
Jamie Yao
Jeremy Patterson Hilton
Jimmy Chen
Josh Mutus
Juan Atalaya
Kostyantyn Kechedzhi
Kunal Arya
Marco Szalay
Masoud Mohseni
Matt Trevithick
Michael Broughton
Michael Newman
Nicholas Bushnell
Nicholas Redd
Orion Martin
Pavel Laptev
Ping Yeh
Rami Barends
Roberto Collins
Sean Harrington
Sergei Isakov
Thomas E O'Brien
Trent Huang
Trevor Mccourt
Vadim Smelyanskiy
Vladimir Shvarts
William Courtney
Wojtek Mruczkiewicz
Xiao Mi
Yu Chen
Nature (2021)
Preview abstract
Realizing the potential of quantum computing will require achieving sufficiently low logical error rates. Many applications call for error rates below 10^-15, but state-of-the-art quantum platforms typically have physical error rates near 10^-3. Quantum error correction (QEC) promises to bridge this divide by distributing quantum logical information across many physical qubits so that errors can be corrected. Logical errors are then exponentially suppressed as the number of physical qubits grows, provided that the physical error rates are below a certain threshold. QEC also requires that the errors are local, and that performance is maintained over many rounds of error correction, a major outstanding experimental challenge. Here, we implement 1D repetition codes embedded in a 2D grid of superconducting qubits which demonstrate exponential suppression of bit or phase-flip errors, reducing logical error per round by more than 100x when increasing the number of qubits from 5 to 21. Crucially, this error suppression is stable over 50 rounds of error correction. We also introduce a method for analyzing error correlations with high precision, and characterize the locality of errors in a device performing QEC for the first time. Finally, we perform error detection using a small 2D surface code logical qubit on the same device, and show that the results from both 1D and 2D codes agree with numerical simulations using a simple depolarizing error model. These findings demonstrate that superconducting qubits are on a viable path towards fault tolerant quantum computing.
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Quantum many-body systems involving bosonic modes or gauge fields have infinite-dimensional local Hilbert spaces which must be truncated to perform simulations of real-time dynamics on classical or quantum computers. To analyze errors resulting from truncation, we develop methods for bounding the rate of growth of local quantum numbers such as the occupation number of a mode at a lattice site, or the electric field at a lattice link. Our approach applies to various models of bosons interacting with spins or fermions such as the Hubbard-Holstein, Fr\"ohlich, and Dicke models, and also to both abelian and non-abelian gauge theories. We show that if states in these models are truncated by imposing an upper limit $\Lambda$ on each local quantum number, and if the initial state has low local quantum numbers, then a truncation error no worse than $\epsilon$ can be achieved by choosing $\Lambda$ to increase polylogarithmically with $\epsilon^{-1}$, an exponential improvement over previous bounds based on energy conservation. For the Hubbard-Holstein model, we numerically compute an upper bound on the value of $\Lambda$ that achieves accuracy $\epsilon$, finding significant improvement over previous estimates in various parameter regimes. We also establish a criterion for truncating the Hamiltonian with a provable guarantee on the accuracy of time evolution. Building on that result, we formulate quantum algorithms for dynamical simulation of lattice gauge theories and of models with bosonic modes; the gate complexity depends almost linearly on spacetime volume in the former case, and almost quadratically on time in the latter case. We establish a lower bound showing that there are systems involving bosons for which this quadratic scaling with time cannot be improved. By applying our results on the truncation error in time evolution, we also prove that spectrally isolated energy eigenstates can be approximated with error at most $\epsilon$ by truncating local quantum numbers at $\Lambda=\textrm{polylog}(\epsilon^{-1})$.
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Hamiltonian Monte Carlo is discussed in the context of a fusion plasma reconstruction. Ill conditioned covariance and multi-modality are discussed in depth.
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Tuning Quantum Information Scrambling on a 53-Qubit Processor
Alan Derk
Alan Ho
Alex Opremcak
Alexander Korotkov
Alexandre Bourassa
Andre Gregory Petukhov
Andrew Dunsworth
Anthony Megrant
Bálint Pató
Benjamin Chiaro
Brooks Riley Foxen
Charles Neill
Cody Jones
Daniel Eppens
Dave Landhuis
Doug Strain
Edward Farhi
Eric Ostby
Fedor Kostritsa
Frank Carlton Arute
Igor Aleiner
Jamie Yao
Jeffrey Marshall
Jeremy Patterson Hilton
Jimmy Chen
Josh Mutus
Juan Atalaya
Kostyantyn Kechedzhi
Kunal Arya
Marco Szalay
Masoud Mohseni
Matt Trevithick
Michael Blythe Broughton
Michael Newman
Nicholas Bushnell
Nicholas Redd
Orion Martin
Pavel Laptev
Ping Yeh
Rami Barends
Roberto Collins
Salvatore Mandra
Sean Harrington
Sergei Isakov
Thomas E O'Brien
Trent Huang
Trevor Mccourt
Vadim Smelyanskiy
Vladimir Shvarts
William Courtney
Wojtek Mruczkiewicz
Xiao Mi
Yu Chen
arXiv (2021)
Preview abstract
As entanglement in a quantum system grows, initially localized quantum information is spread into the exponentially many degrees of freedom of the entire system. This process, known as quantum scrambling, is computationally intensive to study classically and lies at the heart of several modern physics conundrums. Here, we characterize scrambling of different quantum circuits on a 53-qubit programmable quantum processor by measuring their out-of-time-order correlators (OTOCs). We observe that the spatiotemporal spread of OTOCs, as well as their circuit-to-circuit fluctuation, unravel in detail the time-scale and extent of quantum scrambling. Comparison with numerical results indicates a high OTOC measurement accuracy despite the large size of the quantum system. Our work establishes OTOC as an experimental tool to diagnose quantum scrambling at the threshold of being classically inaccessible.
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Engineers: You Can Disrupt Climate Change
Illustrates the scale and nature of challenges, and opportunities for engineers to tackle them.
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Deep diversification of an AAV capsid protein by machine learning
Ali Bashir
Sam Sinai
Nina K. Jain
Pierce J. Ogden
Patrick F. Riley
George M. Church
Eric D. Kelsic
Nature Biotechnology (2021)
Preview abstract
Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one
route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload. Focusing on a 28-amino acid segment, we generated 201,426 variants of the AAV2 wild-type (WT) sequence yielding 110,689 viable engineered capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences, with 12–29 mutations across this region. Even when trained on limited data, deep neural network models accurately predict capsid viability across diverse variants. This approach unlocks vast areas of functional but previously unreachable sequence space, with many potential applications for the generation of improved
viral vectors and protein therapeutics.
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On Ensembles, I-Optimality, and Active Learning
William D Heavlin
Journal of Statistical Theory and Practice (2021)
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We consider the active learning problem for a supervised learning
model: That is, after training a black box model on a given dataset, we determine which (large batch of) unlabeled candidates to label in order to improve
the model further.
We concentrate on the large-batch case, because this is most aligned with
most machine learning applications, and because it is more theoretically rich.
Our approach blends two key ideas: (1) We quantify model uncertainty with
jackknife-like 50-percent sub-samples (“half-samples”). (2) To select which n of
C candidates to label, we consider (a rank-(M −1) estimate of) the associated
C × C prediction covariance matrix, which has good properties.
We illustrate by fitting a deep neural networks to about 20 percent of the
CIFAR-10 image dataset. The statistical efficiency we achieve is better than
3× random selection.
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