# Publications

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# Publications

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1 - 15 of 139 publications

Analyzing Prospects for Quantum Advantage in Topological Data Analysis

Dominic W. Berry

Yuan Su

Casper Gyurik

Robbie King

Joao Basso

Abhishek Rajput

Nathan Wiebe

Vedran Djunko

PRX Quantum, 5(2024), pp. 010319

Preview abstract
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

Raffaele Santagati

Alán Aspuru-Guzik

Matthias Degroote

Leticia Gonzalez

Elica Kyoseva

Nikolaj Moll

Markus Oppel

Robert Parrish

Michael Streif

Christofer Tautermann

Horst Weiss

Nathan Wiebe

Clemens Utschig-Utschig

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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Quantum Computation of Stopping power for Inertial Fusion Target Design

Dominic Berry

Alina Kononov

Alec White

Joonho Lee

Andrew Baczewski

Proceedings of the National Academy of Sciences, 121(2024), e2317772121

Preview abstract
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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Model-based Optimization of Superconducting Qubit Readout

Alex Opremcak

Mostafa Khezri

Alexandre Bourassa

Alexander Korotkov

Jimmy Chen

Physical Review Letters, 132(2024), pp. 100603

Preview abstract
Measurement is one of the essential components of quantum algorithms, and for superconducting qubits it is often the most error prone. Here, we demonstrate a model-based readout optimization achieving low measurement errors while avoiding detrimental side-effects. For simultaneous and mid-circuit measurements across 17 qubits we observe 1.5% error per qubit with a duration of 500 ns end-to-end and minimal excess reset error from residual resonator photons. We also suppress measurement-induced state transitions and achieve a qubit leakage rate limited by natural heating.This technique can scale to hundreds of qubits, and be used to enhance performance of error-correcting codes as well as near-term applications
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Optimizing quantum gates towards the scale of logical qubits

Alexandre Bourassa

Andrew Dunsworth

Will Livingston

Vlad Sivak

Trond Andersen

Yaxing Zhang

Desmond Chik

Jimmy Chen

Charles Neill

Alejo Grajales Dau

Anthony Megrant

Alexander Korotkov

Vadim Smelyanskiy

Yu Chen

Nature Communications, 15(2024), pp. 2442

Preview abstract
A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major challenges that could become fundamental roadblocks are manufacturing high-performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dynamic control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by ~3.7× compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to a variety of quantum operations, algorithms, and computing architectures.
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Dynamics of magnetization at infinite temperature in a Heisenberg spin chain

Trond Andersen

Rhine Samajdar

Andre Petukhov

Jesse Hoke

Dmitry Abanin

ILYA Drozdov

Xiao Mi

Alexis Morvan

Charles Neill

Rajeev Acharya

Richard Ross Allen

Kyle Anderson

Markus Ansmann

Frank Arute

Kunal Arya

Juan Atalaya

Gina Bortoli

Alexandre Bourassa

Leon Brill

Michael Broughton

Bob Buckley

Tim Burger

Nicholas Bushnell

Juan Campero

Hung-Shen Chang

Jimmy Chen

Benjamin Chiaro

Desmond Chik

Josh Cogan

Roberto Collins

Paul Conner

William Courtney

Alex Crook

Ben Curtin

Agustin Di Paolo

Andrew Dunsworth

Clint Earle

Lara Faoro

Edward Farhi

Reza Fatemi

Vinicius Ferreira

Ebrahim Forati

Austin Fowler

Brooks Foxen

Gonzalo Garcia

Élie Genois

William Giang

Dar Gilboa

Raja Gosula

Alejo Grajales Dau

Steve Habegger

Michael Hamilton

Monica Hansen

Sean Harrington

Paula Heu

Gordon Hill

Trent Huang

Ashley Huff

Bill Huggins

Sergei Isakov

Justin Iveland

Zhang Jiang

Cody Jones

Pavol Juhas

Mostafa Khezri

Marika Kieferova

Alexei Kitaev

Andrey Klots

Alexander Korotkov

Fedor Kostritsa

John Mark Kreikebaum

Dave Landhuis

Pavel Laptev

Kim Ming Lau

Lily Laws

Joonho Lee

Kenny Lee

Yuri Lensky

Alexander Lill

Wayne Liu

Salvatore Mandra

Orion Martin

Steven Martin

Seneca Meeks

Amanda Mieszala

Shirin Montazeri

Ramis Movassagh

Wojtek Mruczkiewicz

Ani Nersisyan

Michael Newman

JiunHow Ng

Murray Ich Nguyen

Tom O'Brien

Seun Omonije

Alex Opremcak

Rebecca Potter

Leonid Pryadko

David Rhodes

Charles Rocque

Negar Saei

Kannan Sankaragomathi

Henry Schurkus

Christopher Schuster

Mike Shearn

Aaron Shorter

Noah Shutty

Vladimir Shvarts

Vlad Sivak

Jindra Skruzny

Clarke Smith

Rolando Somma

George Sterling

Doug Strain

Marco Szalay

Doug Thor

Alfredo Torres

Guifre Vidal

Benjamin Villalonga

Cheng Xing

Jamie Yao

Ping Yeh

Juhwan Yoo

Grayson Young

Yaxing Zhang

Ningfeng Zhu

Jeremy Hilton

Anthony Megrant

Yu Chen

Vadim Smelyanskiy

Vedika Khemani

Sarang Gopalakrishnan

Tomaž Prosen

Science, 384(2024), pp. 48-53

Preview abstract
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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Stable quantum-correlated many-body states through engineered dissipation

Xiao Mi

Alexios Michailidis

Sara Shabani

Jerome Lloyd

Rajeev Acharya

Igor Aleiner

Trond Andersen

Markus Ansmann

Frank Arute

Kunal Arya

Juan Atalaya

Gina Bortoli

Alexandre Bourassa

Leon Brill

Michael Broughton

Bob Buckley

Tim Burger

Nicholas Bushnell

Jimmy Chen

Benjamin Chiaro

Desmond Chik

Charina Chou

Josh Cogan

Roberto Collins

Paul Conner

William Courtney

Alex Crook

Ben Curtin

Alejo Grajales Dau

Dripto Debroy

Agustin Di Paolo

ILYA Drozdov

Andrew Dunsworth

Lara Faoro

Edward Farhi

Reza Fatemi

Vinicius Ferreira

Ebrahim Forati

Austin Fowler

Brooks Foxen

Élie Genois

William Giang

Dar Gilboa

Raja Gosula

Steve Habegger

Michael Hamilton

Monica Hansen

Sean Harrington

Paula Heu

Trent Huang

Ashley Huff

Bill Huggins

Sergei Isakov

Justin Iveland

Zhang Jiang

Cody Jones

Pavol Juhas

Kostyantyn Kechedzhi

Mostafa Khezri

Marika Kieferova

Alexei Kitaev

Andrey Klots

Alexander Korotkov

Fedor Kostritsa

John Mark Kreikebaum

Dave Landhuis

Pavel Laptev

Kim Ming Lau

Lily Laws

Joonho Lee

Kenny Lee

Yuri Lensky

Alexander Lill

Wayne Liu

Orion Martin

Amanda Mieszala

Shirin Montazeri

Alexis Morvan

Ramis Movassagh

Wojtek Mruczkiewicz

Charles Neill

Ani Nersisyan

Michael Newman

JiunHow Ng

Murray Ich Nguyen

Tom O'Brien

Alex Opremcak

Andre Petukhov

Rebecca Potter

Leonid Pryadko

Charles Rocque

Negar Saei

Kannan Sankaragomathi

Henry Schurkus

Christopher Schuster

Mike Shearn

Aaron Shorter

Noah Shutty

Vladimir Shvarts

Jindra Skruzny

Clarke Smith

Rolando Somma

George Sterling

Doug Strain

Marco Szalay

Alfredo Torres

Guifre Vidal

Benjamin Villalonga

Cheng Xing

Jamie Yao

Ping Yeh

Juhwan Yoo

Grayson Young

Yaxing Zhang

Ningfeng Zhu

Jeremy Hilton

Anthony Megrant

Yu Chen

Vadim Smelyanskiy

Dmitry Abanin

Science, 383(2024), pp. 1332-1337

Preview abstract
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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Quantum Error Mitigation

Zhenyu Cai

Simon Benjamin

Suguru Endo

William J. Huggins

Ying Li

Thomas E O'Brien

Reviews of Modern Physics, 95(2023), pp. 045005

Preview abstract
For quantum computers to successfully solve real-world problems, it is necessary to tackle the challenge of noise: the errors that occur in elementary physical components due to unwanted or imperfect interactions. The theory of quantum fault tolerance can provide an answer in the long term, but in the coming era of noisy intermediate-scale quantum machines one must seek to mitigate errors rather than completely eliminate them. This review surveys the diverse methods that have been proposed for quantum error mitigation, assesses their in-principle efficacy, and describes the hardware demonstrations achieved to date. Commonalities and limitations among the methods are identified, while mention is made of how mitigation methods can be chosen according to the primary type of noise present, including algorithmic errors. Open problems in the field are identified, and the prospects for realizing mitigation-based devices that can deliver a quantum advantage with an impact on science and business are discussed.
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Josephson parametric amplifier with Chebyshev gain profile and high saturation

Ryan Kaufman

Mark Dykman

Andrea Iorio

George Sterling

Alex Opremcak

Lara Faoro

Tim Burger

Robert Gasca

Physical Review Applied, 20(2023), pp. 054058

Preview abstract
We demonstrate a Josephson parametric amplifier design with a band-pass impedance matching network based on a third-order Chebyshev prototype. We measured eight amplifiers operating at 4.6~GHz that exhibit gains of 20~dB with less than 1~dB gain ripple and up to 500~MHz bandwidth. The amplifiers further achieve high input saturation powers around $-93$~dBm based on the use of rf-SQUID arrays as their nonlinear element. We characterize the amplifiers' readout efficiency and their signal-to-noise ratio near saturation using a Sycamore processor. In addition, we measure the amplifiers intermodulation distortion in two-tone experiments as a function of input power and inter-tone detuning, and observe excess distortion at small detuning with a pronounced dip as a function of signal power, which we interpret in terms of power-dependent dielectric losses.
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Exponential Quantum Speedup in Simulating Coupled Classical Oscillators

Dominic Berry

Rolando Somma

Nathan Wiebe

Physical Review X, 13(2023), pp. 041041

Preview abstract
We present a quantum algorithm for simulating the classical dynamics of 2^n coupled oscillators (e.g., 2^n masses coupled by springs). Our approach leverages a mapping between the Schrodinger equation and Newton's equations for harmonic potentials such that the amplitudes of the evolved quantum state encode the momenta and displacements of the classical oscillators. When individual masses and spring constants can be efficiently queried, and when the initial state can be efficiently prepared, the complexity of our quantum algorithm is polynomial in n, almost linear in the evolution time, and sublinear in the sparsity. As an example application, we apply our quantum algorithm to efficiently estimate the kinetic energy of an oscillator at any time, for a specification of the problem that we prove is \BQP-complete. Thus, our approach solves a potentially practical application with an exponential speedup over classical computers. Finally, we show that under similar conditions our approach can efficiently simulate more general classical harmonic systems with 2^n modes.
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Evaluating the Evidence for Exponential Quantum Advantage in Ground-State Quantum Chemistry

Seunghoon Lee

Joonho Lee

Huanchen Zhai

Yu Tong

Alexander Dalzell

Ashutosh Kumar

Phillip Helms

Johnnie Gray

Zhi-Hao Cui

Michael Kastoryano

John Preskill

David Reichman

Earl Campbell

Edward Valeev

Lin Lin

Garnet Chan

Nature Communications, 14(2023)

Preview abstract
Due to intense interest in the potential applications of quantum computing, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in quantum chemistry, namely, ground-state energy estimation, for generic chemical problems where heuristic quantum state preparation might be assumed to be efficient. The availability of exponential quantum advantage then centers on whether features of the physical problem that enable efficient heuristic quantum state preparation also enable efficient solution by classical heuristics. Through numerical studies of quantum state preparation and empirical complexity analysis (including the error scaling) of classical heuristics, in both ab initio and model Hamiltonian settings, we conclude that evidence for such an exponential advantage across chemical space has yet to be found. While quantum computers may still prove useful for ground-state quantum chemistry through polynomial speedups, it may be prudent to assume exponential speedups are not generically available for this problem.
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