Ryan Babbush
Ryan is the director of the Quantum Algorithm & Applications Team at Google. The mandate of this research team is to develop new and more efficient quantum algorithms, discovery and analyze new applications of quantum computers, build and open source tools for accelerating quantum algorithms research, and to design algorithms experiments to demonstrate on existing quantum devices.
Authored Publications
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Fast electronic structure quantum simulation by spectrum amplification
Qiushi Han
Dominic Berry
Alec White
Albert Eugene DePrince III
Guang Hao Low
Robbie King
Rolando Somma
arXiv:2502.15882 (2025)
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The most advanced techniques using fault-tolerant quantum computers to estimate the ground-state energy of a chemical Hamiltonian involve compression of the Coulomb operator through tensor factorizations, enabling efficient block-encodings of the Hamiltonian. A natural challenge of these methods is the degree to which block-encoding costs can be reduced. We address this challenge through the technique of spectrum amplification, which magnifies the spectrum of the low-energy states of Hamiltonians that can be expressed as sums of squares. Spectrum amplification enables estimating ground-state energies with significantly improved cost scaling in the block encoding normalization factor $\Lambda$ to just $\sqrt{2\Lambda E_{\text{gap}}}$, where $E_{\text{gap}} \ll \Lambda$ is the lowest energy of the sum-of-squares Hamiltonian. To achieve this, we show that sum-of-squares representations of the electronic structure Hamiltonian are efficiently computable by a family of classical simulation techniques that approximate the ground-state energy from below. In order to further optimize, we also develop a novel factorization that provides a trade-off between the two leading Coulomb integral factorization schemes-- namely, double factorization and tensor hypercontraction-- that when combined with spectrum amplification yields a factor of 4 to 195 speedup over the state of the art in ground-state energy estimation for models of Iron-Sulfur complexes and a CO$_{2}$-fixation catalyst.
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We present shadow Hamiltonian simulation, a framework for simulating quantum dynamics
using a compressed quantum state that we call the “shadow state”. The amplitudes of this
shadow state are proportional to the expectations of a set of operators of interest. The shadow
state evolves according to its own Schrodinger equation, and under broad conditions can be
simulated on a quantum computer. We analyze a number of applications of this framework to quantum simulation problems. This includes simulating the dynamics of exponentially large systems of free fermions, or exponentially large systems of free bosons, the latter example recovering a recent algorithm for simulating exponentially many classical harmonic oscillators. Shadow Hamiltonian simulation can be extended to simulate expectations of more complex operators such as two-time correlators or Green’s functions, and to study the evolution of operators themselves in the Heisenberg picture
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Stable quantum-correlated many-body states through engineered dissipation
Sara Shabani
Dripto Debroy
Jerome Lloyd
Alexios Michailidis
Andrew Dunsworth
Bill Huggins
Markus Hoffmann
Alexis Morvan
Josh Cogan
Ben Curtin
Guifre Vidal
Bob Buckley
Tom O'Brien
John Mark Kreikebaum
Rajeev Acharya
Joonho Lee
Ningfeng Zhu
Shirin Montazeri
Sergei Isakov
Jamie Yao
Clarke Smith
Rebecca Potter
Sean Harrington
Jeremy Hilton
Paula Heu
Alexei Kitaev
Alex Crook
Fedor Kostritsa
Kim Ming Lau
Dmitry Abanin
Trent Huang
Aaron Shorter
Steve Habegger
Gina Bortoli
Charles Rocque
Vladimir Shvarts
Alfredo Torres
Anthony Megrant
Charles Neill
Michael Hamilton
Dar Gilboa
Lily Laws
Nicholas Bushnell
Ramis Movassagh
Mike Shearn
Wojtek Mruczkiewicz
Desmond Chik
Leonid Pryadko
Xiao Mi
Brooks Foxen
Frank Arute
Alejo Grajales Dau
Yaxing Zhang
Lara Faoro
Alexander Lill
JiunHow Ng
Justin Iveland
Marco Szalay
Orion Martin
Juhwan Yoo
Michael Newman
William Giang
Alex Opremcak
Amanda Mieszala
William Courtney
Andrey Klots
Wayne Liu
Pavel Laptev
Charina Chou
Paul Conner
Rolando Somma
Vadim Smelyanskiy
Benjamin Chiaro
Grayson Young
Tim Burger
ILYA Drozdov
Agustin Di Paolo
Jimmy Chen
Marika Kieferova
Michael Broughton
Negar Saei
Juan Atalaya
Markus Ansmann
Pavol Juhas
Murray Ich Nguyen
Yuri Lensky
Roberto Collins
Élie Genois
Jindra Skruzny
Igor Aleiner
Yu Chen
Reza Fatemi
Leon Brill
Ashley Huff
Doug Strain
Monica Hansen
Noah Shutty
Ebrahim Forati
Dave Landhuis
Kenny Lee
Ping Yeh
Kunal Arya
Henry Schurkus
Cheng Xing
Cody Jones
Edward Farhi
Raja Gosula
Andre Petukhov
Alexander Korotkov
Ani Nersisyan
Christopher Schuster
George Sterling
Kostyantyn Kechedzhi
Trond Andersen
Alexandre Bourassa
Kannan Sankaragomathi
Vinicius Ferreira
Science, 383 (2024), pp. 1332-1337
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Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Dynamics of magnetization at infinite temperature in a Heisenberg spin chain
Tomaž Prosen
Vedika Khemani
Rhine Samajdar
Jesse Hoke
Sarang Gopalakrishnan
Andrew Dunsworth
Bill Huggins
Markus Hoffmann
Alexis Morvan
Josh Cogan
Ben Curtin
Guifre Vidal
Bob Buckley
Tom O'Brien
John Mark Kreikebaum
Rajeev Acharya
Joonho Lee
Ningfeng Zhu
Shirin Montazeri
Sergei Isakov
Jamie Yao
Clarke Smith
Rebecca Potter
Sean Harrington
Jeremy Hilton
Paula Heu
Alexei Kitaev
Alex Crook
Fedor Kostritsa
Kim Ming Lau
Dmitry Abanin
Trent Huang
Aaron Shorter
Steve Habegger
Steven Martin
Gina Bortoli
Seun Omonije
Richard Ross Allen
Charles Rocque
Vladimir Shvarts
Alfredo Torres
Anthony Megrant
Charles Neill
Michael Hamilton
Dar Gilboa
Lily Laws
Nicholas Bushnell
Kyle Anderson
Ramis Movassagh
David Rhodes
Mike Shearn
Wojtek Mruczkiewicz
Desmond Chik
Leonid Pryadko
Xiao Mi
Brooks Foxen
Frank Arute
Alejo Grajales Dau
Yaxing Zhang
Lara Faoro
Alexander Lill
Gordon Hill
JiunHow Ng
Justin Iveland
Marco Szalay
Orion Martin
Juan Campero
Juhwan Yoo
Michael Newman
William Giang
Gonzalo Garcia
Alex Opremcak
Amanda Mieszala
William Courtney
Andrey Klots
Wayne Liu
Pavel Laptev
Paul Conner
Rolando Somma
Vadim Smelyanskiy
Benjamin Chiaro
Grayson Young
Tim Burger
ILYA Drozdov
Agustin Di Paolo
Jimmy Chen
Marika Kieferova
Hung-Shen Chang
Michael Broughton
Negar Saei
Juan Atalaya
Markus Ansmann
Pavol Juhas
Murray Ich Nguyen
Yuri Lensky
Roberto Collins
Élie Genois
Jindra Skruzny
Yu Chen
Reza Fatemi
Leon Brill
Seneca Meeks
Ashley Huff
Doug Strain
Monica Hansen
Noah Shutty
Ebrahim Forati
Doug Thor
Dave Landhuis
Kenny Lee
Ping Yeh
Kunal Arya
Henry Schurkus
Cheng Xing
Cody Jones
Edward Farhi
Vlad Sivak
Raja Gosula
Andre Petukhov
Clint Earle
Alexander Korotkov
Ani Nersisyan
Christopher Schuster
George Sterling
Trond Andersen
Alexandre Bourassa
Salvatore Mandra
Kannan Sankaragomathi
Vinicius Ferreira
Science, 384 (2024), pp. 48-53
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Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain’s center, P(M). The first two moments of P(M) show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Expressing and Analyzing Quantum Algorithms with Qualtran
Anurudh Peduri
Charles Yuan
arXiv::2409.04643 (2024)
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Quantum computing's transition from theory to reality has spurred the need for novel software tools to manage the increasing complexity, sophistication, toil, and chance for error of quantum algorithm development. We present Qualtran, an open-source library for representing and analyzing quantum algorithms. Using carefully chosen abstractions and data structures, we can simulate and test algorithms, automatically generate information-rich diagrams, and tabulate resource requirements. Qualtran offers a \emph{standard library} of algorithmic building blocks that are essential for modern cost-minimizing compilations. Its capabilities are showcased through the re-analysis of key algorithms in Hamiltonian simulation, chemistry, and cryptography. The resulting architecture-independent resource counts can be forwarded to our implementation of cost models to estimate physical costs like wall-clock time and number of physical qubits assuming a surface-code architecture. Qualtran provides a foundation for explicit constructions and reproducible analysis, fostering greater collaboration within the quantum algorithm development community. We believe tools like Qualtran will accelerate progress in the field.
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Analyzing Prospects for Quantum Advantage in Topological Data Analysis
Dominic W. Berry
Abhishek Rajput
Nathan Wiebe
Robbie King
Vedran Djunko
Casper Gyurik
Joao Basso
Yuan Su
PRX Quantum, 5 (2024), pp. 010319
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Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers in persistent homology (a way of characterizing topological features of data sets). Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples for which super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve some seemingly classically intractable instances.
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Drug Design on Quantum Computers
Leticia Gonzalez
Christofer Tautermann
Horst Weiss
Michael Streif
Raffaele Santagati
Clemens Utschig-Utschig
Robert Parrish
Markus Oppel
Alán Aspuru-Guzik
Matthias Degroote
Elica Kyoseva
Nathan Wiebe
Nikolaj Moll
Nature Physics (2024)
Preview abstract
The promised industrial applications of quantum computers often rest on their anticipated ability to perform accurate, efficient quantum chemical calculations. Computational drug discovery relies on accurate predictions of how candidate drugs interact with their targets in a cellular environment involving several thousands of atoms at finite temperatures. Although quantum computers are still far from being used as daily tools in the pharmaceutical industry, here we explore the challenges and opportunities of applying quantum computers to drug design. We discuss where these could transform industrial research and identify the substantial further developments needed to reach this goal.
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Given copies of a quantum state $\rho$, a shadow tomography protocol aims to learn all expectation values from a fixed set of observables, to within a given precision $\epsilon$. We say that a shadow tomography protocol is \textit{triply efficient} if it is sample- and time-efficient, and only employs measurements that entangle a constant number of copies of $\rho$ at a time. The classical shadows protocol based on random single-copy measurements is triply efficient for the set of local Pauli observables. This and other protocols based on random single-copy Clifford measurements can be understood as arising from fractional colorings of a graph $G$ that encodes the commutation structure of the set of observables. Here we describe a framework for two-copy shadow tomography that uses an initial round of Bell measurements to reduce to a fractional coloring problem in an induced subgraph of $G$ with bounded clique number. This coloring problem can be addressed using techniques from graph theory known as \textit{chi-boundedness}. Using this framework we give the first triply efficient shadow tomography scheme for the set of local fermionic observables, which arise in a broad class of interacting fermionic systems in physics and chemistry. We also give a triply efficient scheme for the set of all $n$-qubit Pauli observables. Our protocols for these tasks use two-copy measurements, which is necessary: sample-efficient schemes are provably impossible using only single-copy measurements. Finally, we give a shadow tomography protocol that compresses an $n$-qubit quantum state into a $\poly(n)$-sized classical representation, from which one can extract the expected value of any of the $4^n$ Pauli observables in $\poly(n)$ time, up to a small constant error.
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Quantum Computation of Stopping power for Inertial Fusion Target Design
Andrew Baczewski
Alec White
Dominic Berry
Alina Kononov
Joonho Lee
Proceedings of the National Academy of Sciences, 121 (2024), e2317772121
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Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it - one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. We also work out the constant factors associated with a novel implementation of a high order Trotter approach to simulating a grid representation of these systems. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with
roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.
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We describe a quantum algorithm for the Planted Noisy kXOR problem (also known as sparse Learning Parity with Noise) that achieves a nearly quartic (4th power) speedup over the best known classical algorithm while also only using logarithmically many qubits. Our work generalizes and simplifies prior work of Hastings, by building on his quantum algorithm for the Tensor Principal Component Analysis (PCA) problem. We achieve our quantum speedup using a general framework based on the Kikuchi Method (recovering the quartic speedup for Tensor PCA), and we anticipate it will yield similar speedups for further planted inference problems. These speedups rely on the fact that planted inference problems naturally instantiate the Guided Sparse Hamiltonian problem. Since the Planted Noisy kXOR problem has been used as a component of certain cryptographic constructions, our work suggests that some of these are susceptible to super-quadratic quantum attacks.
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