# Sergio Boixo

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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

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

Cody Jones

Pavol Juhas

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

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, vol. 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

Alfredo Torres

Guifre Vidal

Cheng Xing

Jamie Yao

Ping Yeh

Juhwan Yoo

Grayson Young

Yaxing Zhang

Ningfeng Zhu

Jeremy Hilton

Anthony Megrant

Yu Chen

Vadim Smelyanskiy

Dmitry Abanin

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

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

Cody Jones

Pavol Juhas

Kostyantyn Kechedzhi

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

Science, vol. 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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Purification-Based Quantum Error Mitigation of Pair-Correlated Electron Simulations

Thomas E O'Brien

Gian-Luca R. Anselmetti

Fotios Gkritsis

Vincent Elfving

Stefano Polla

William J. Huggins

Oumarou Oumarou

Kostyantyn Kechedzhi

Dmitry Abanin

Rajeev Acharya

Igor Aleiner

Richard Ross Allen

Trond Ikdahl Andersen

Kyle Anderson

Markus Ansmann

Frank Carlton Arute

Kunal Arya

Juan Atalaya

Michael Blythe Broughton

Bob Benjamin Buckley

Alexandre Bourassa

Leon Brill

Tim Burger

Nicholas Bushnell

Jimmy Chen

Yu Chen

Benjamin Chiaro

Desmond Chun Fung Chik

Josh Godfrey Cogan

Roberto Collins

Paul Conner

William Courtney

Alex Crook

Ben Curtin

Ilya Drozdov

Andrew Dunsworth

Daniel Eppens

Lara Faoro

Edward Farhi

Reza Fatemi

Ebrahim Forati

Brooks Riley Foxen

William Giang

Dar Gilboa

Alejandro Grajales Dau

Steve Habegger

Michael C. Hamilton

Sean Harrington

Jeremy Patterson Hilton

Trent Huang

Ashley Anne Huff

Sergei Isakov

Justin Thomas Iveland

Cody Jones

Pavol Juhas

Marika Kieferova

Andrey Klots

Alexander Korotkov

Fedor Kostritsa

John Mark Kreikebaum

Dave Landhuis

Pavel Laptev

Kim Ming Lau

Lily MeeKit Laws

Joonho Lee

Kenny Lee

Alexander T. Lill

Wayne Liu

Orion Martin

Trevor Johnathan Mccourt

Anthony Megrant

Xiao Mi

Masoud Mohseni

Shirin Montazeri

Alexis Morvan

Ramis Movassagh

Wojtek Mruczkiewicz

Charles Neill

Ani Nersisyan

Michael Newman

Jiun How Ng

Murray Nguyen

Alex Opremcak

Andre Gregory Petukhov

Rebecca Potter

Kannan Aryaperumal Sankaragomathi

Christopher Schuster

Mike Shearn

Aaron Shorter

Vladimir Shvarts

Jindra Skruzny

Vadim Smelyanskiy

Clarke Smith

Rolando Diego Somma

Doug Strain

Marco Szalay

Alfredo Torres

Guifre Vidal

Jamie Yao

Ping Yeh

Juhwan Yoo

Grayson Robert Young

Yaxing Zhang

Ningfeng Zhu

Christian Gogolin

Nature Physics (2023)

Preview abstract
An important measure of the development of quantum computing platforms has been the simulation of increasingly complex physical systems. Prior to fault-tolerant quantum computing, robust error mitigation strategies are necessary to continue this growth. Here, we study physical simulation within the seniority-zero electron pairing subspace, which affords both a computational stepping stone to a fully correlated model, and an opportunity to validate recently introduced ``purification-based'' error-mitigation strategies. We compare the performance of error mitigation based on doubling quantum resources in time (echo verification) or in space (virtual distillation), on up to 20 qubits of a superconducting qubit quantum processor. We observe a reduction of error by one to two orders of magnitude below less sophisticated techniques (e.g. post-selection); the gain from error mitigation is seen to increase with the system size. Employing these error mitigation strategies enables the implementation of the largest variational algorithm for a correlated chemistry system to-date. Extrapolating performance from these results allows us to estimate minimum requirements for a beyond-classical simulation of electronic structure. We find that, despite the impressive gains from purification-based error mitigation, significant hardware improvements will be required for classically intractable variational chemistry simulations.
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Suppressing quantum errors by scaling a surface code logical qubit

Anthony Megrant

Cody Jones

Jeremy Hilton

Jimmy Chen

Juan Atalaya

Kenny Lee

Michael Newman

Vadim Smelyanskiy

Yu Chen

Nature (2023)

Preview abstract
Practical quantum computing will require error rates that are well below what is achievable with
physical qubits. Quantum error correction [1, 2] offers a path to algorithmically-relevant error rates
by encoding logical qubits within many physical qubits, where increasing the number of physical
qubits enhances protection against physical errors. However, introducing more qubits also increases
the number of error sources, so the density of errors must be sufficiently low in order for logical
performance to improve with increasing code size. Here, we report the measurement of logical qubit
performance scaling across multiple code sizes, and demonstrate that our system of superconducting
qubits has sufficient performance to overcome the additional errors from increasing qubit number.
We find our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3
logical qubits on average, both in terms of logical error probability over 25 cycles and logical error
per cycle (2.914%±0.016% compared to 3.028%±0.023%). To investigate damaging, low-probability
error sources, we run a distance-25 repetition code and observe a 1.7 × 10−6 logical error per round
floor set by a single high-energy event (1.6 × 10−7 when excluding this event). We are able to
accurately model our experiment, and from this model we can extract error budgets that highlight
the biggest challenges for future systems. These results mark the first experimental demonstration
where quantum error correction begins to improve performance with increasing qubit number, and
illuminate the path to reaching the logical error rates required for computation.
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Measurement-induced entanglement and teleportation on a noisy quantum processor

Jesse Hoke

Matteo Ippoliti

Dmitry Abanin

Rajeev Acharya

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

Josh Cogan

Roberto Collins

Paul Conner

William Courtney

Alex Crook

Ben Curtin

Alejo Grajales Dau

Agustin Di Paolo

ILYA Drozdov

Andrew Dunsworth

Daniel Eppens

Edward Farhi

Reza Fatemi

Vinicius Ferreira

Ebrahim Forati

Brooks Foxen

William Giang

Dar Gilboa

Raja Gosula

Steve Habegger

Michael Hamilton

Monica Hansen

Paula Heu

Trent Huang

Ashley Huff

Bill Huggins

Sergei Isakov

Justin Iveland

Cody Jones

Pavol Juhas

Kostyantyn Kechedzhi

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

Seun Omonije

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

Cheng Xing

Jamie Yao

Ping Yeh

Juhwan Yoo

Grayson Young

Yaxing Zhang

Ningfeng Zhu

Jeremy Hilton

Anthony Megrant

Yu Chen

Vadim Smelyanskiy

Xiao Mi

Vedika Khemani

Nature, vol. 622 (2023), 481–486

Preview abstract
Measurement has a special role in quantum theory: by collapsing the wavefunction, it can enable phenomena such as teleportation and thereby alter the ‘arrow of time’ that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space–time that go beyond the established paradigms for characterizing phases, either in or out of equilibrium. For present-day noisy intermediate-scale quantum (NISQ) processors, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling to measurement-induced teleportation. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
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Direct Measurement of Nonlocal Interactions in the Many-Body Localized Phase

Amit Vainsencher

Andrew Dunsworth

Anthony Megrant

Ben Chiaro

Brooks Foxen

Charles Neill

Dave Landhuis

Fedor Kostritsa

Frank Carlton Arute

Jimmy Chen

John Martinis

Josh Mutus

Kostyantyn Kechedzhi

Kunal Arya

Rami Barends

Roberto Collins

Trent Huang

Vadim Smelyanskiy

Yu Chen

Physical Review Research, vol. 4 (2022), pp. 013148

Preview abstract
The interplay of interactions and strong disorder can lead to an exotic quantum many-body localized (MBL) phase of matter. Beyond the absence of transport, the MBL phase has distinctive signatures, such as slow dephasing and logarithmic entanglement growth; they commonly result in slow and subtle modifications of the dynamics, rendering their measurement challenging. Here, we experimentally characterize these properties of the MBL phase in a system of coupled superconducting qubits. By implementing phase sensitive techniques, we map out the structure of local integrals of motion in the MBL phase. Tomographic reconstruction of single and two-qubit density matrices allows us to determine the spatial and temporal entanglement growth between the localized sites. In addition, we study the preservation of entanglement in the MBL phase. The interferometric protocols implemented here detect affirmative quantum correlations and exclude artifacts due to the imperfect isolation of the system. By measuring elusive MBL quantities, our work highlights the advantages of phase sensitive measurements in studying novel phases of matter.
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Noise-resilient Majorana Edge Modes on a Chain of Superconducting Qubits

Alejandro Grajales Dau

Alex Crook

Alex Opremcak

Alexa Rubinov

Alexander Korotkov

Alexandre Bourassa

Alexei Kitaev

Alexis Morvan

Andre Gregory Petukhov

Andrew Dunsworth

Andrey Klots

Anthony Megrant

Ashley Anne Huff

Benjamin Chiaro

Bernardo Meurer Costa

Bob Benjamin Buckley

Brooks Foxen

Charles Neill

Christopher Schuster

Cody Jones

Daniel Eppens

Dar Gilboa

Dave Landhuis

Dmitry Abanin

Doug Strain

Ebrahim Forati

Edward Farhi

Emily Mount

Fedor Kostritsa

Frank Carlton Arute

Guifre Vidal

Igor Aleiner

Jamie Yao

Jeremy Patterson Hilton

Joao Basso

John Mark Kreikebaum

Joonho Lee

Juan Atalaya

Juhwan Yoo

Justin Thomas Iveland

Kannan Aryaperumal Sankaragomathi

Kenny Lee

Kim Ming Lau

Kostyantyn Kechedzhi

Kunal Arya

Lara Faoro

Leon Brill

Marco Szalay

Masoud Mohseni

Michael Blythe Broughton

Michael Newman

Michel Henri Devoret

Mike Shearn

Nicholas Bushnell

Orion Martin

Paul Conner

Pavel Laptev

Ping Yeh

Rajeev Acharya

Rebecca Potter

Reza Fatemi

Roberto Collins

Sergei Isakov

Shirin Montazeri

Steve Habegger

Thomas E O'Brien

Trent Huang

Trond Ikdahl Andersen

Vadim Smelyanskiy

Vladimir Shvarts

Wayne Liu

William Courtney

William Giang

William J. Huggins

Wojtek Mruczkiewicz

Xiao Mi

Yaxing Zhang

Yu Chen

Yuan Su

Zijun Chen

Science (2022) (to appear)

Preview abstract
Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the kicked Ising model which exhibits Majorana edge modes (MEMs) protected by a $\mathbb{Z}_2$-symmetry. Remarkably, we find that any multi-qubit Pauli operator overlapping with the MEMs exhibits a uniform decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This finding allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Spectroscopic measurements further indicate exponentially suppressed hybridization between the MEMs over larger system sizes, which manifests as a strong resilience against low-frequency noise. Our work elucidates the noise sensitivity of symmetry-protected edge modes in a solid-state environment.
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Low-Depth Mechanisms for Quantum Optimization

Masoud Mohseni

Vadim Smelyanskiy

PRX Quantum, vol. 3 (2021), pp. 030312

Preview abstract
One of the major application areas of interest for both near-term and fault-tolerant quantum computers is the optimization of classical objective functions. In this work, we develop intuitive constructions for a large class of these algorithms based on connections to simple dynamics of quantum systems, quantum walks, and classical continuous relaxations. We focus on developing a language and tools connected with kinetic energy on a graph for understanding the physical mechanisms of success and failure to guide algorithmic improvement. This physical language, in combination with uniqueness results related to unitarity, allow us to identify some potential pitfalls from kinetic energy fundamentally opposing the goal of optimization. This is connected to effects from wavefunction confinement, phase randomization, and shadow defects lurking in the objective far away from the ideal solution. As an example, we explore the surprising deficiency of many quantum methods in solving uncoupled spin problems and how this is both predictive of performance on some more complex systems while immediately suggesting simple resolutions. Further examination of canonical problems like the Hamming ramp or bush of implications show that entanglement can be strictly detrimental to performance results from the underlying mechanism of solution in approaches like QAOA. Kinetic energy and graph Laplacian perspectives provide new insights to common initialization and optimal solutions in QAOA as well as new methods for more effective layerwise training. Connections to classical methods of continuous extensions, homotopy methods, and iterated rounding suggest new directions for research in quantum optimization. Throughout, we unveil many pitfalls and mechanisms in quantum optimization using a physical perspective, which aim to spur the development of novel quantum optimization algorithms and refinements.
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Focus Beyond Quadratic Speedups for Error-Corrected Quantum Advantage

Michael Newman

PRX Quantum, vol. 2 (2021), pp. 010103

Preview abstract
In this perspective we discuss conditions under which it would be possible for a modest fault-tolerant quantum computer to realize a runtime advantage by executing a quantum algorithm with only a small polynomial speedup over the best classical alternative. The challenge is that the computation must finish within a reasonable amount of time while being difficult enough that the small quantum scaling advantage would compensate for the large constant factor overheads associated with error correction. We compute several examples of such runtimes using state-of-the-art surface code constructions under a variety of assumptions. We conclude that quadratic speedups will not enable quantum advantage on early generations of such fault-tolerant devices unless there is a significant improvement in how we realize quantum error correction. While this conclusion persists even if we were to increase the rate of logical gates in the surface code by more than an order of magnitude, we also repeat this analysis for speedups by other polynomial degrees and find that quartic speedups look significantly more practical.
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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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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 Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor

Kevin Jeffery Sung

Frank Carlton Arute

Kunal Arya

Juan Atalaya

Rami Barends

Michael Blythe Broughton

Bob Benjamin Buckley

Nicholas Bushnell

Jimmy Chen

Yu Chen

Ben Chiaro

Roberto Collins

William Courtney

Andrew Dunsworth

Brooks Riley Foxen

Rob Graff

Steve Habegger

Sergei Isakov

Cody Jones

Kostyantyn Kechedzhi

Alexander Korotkov

Fedor Kostritsa

Dave Landhuis

Pavel Laptev

Martin Leib

Mike Lindmark

Orion Martin

John Martinis

Anthony Megrant

Xiao Mi

Masoud Mohseni

Wojtek Mruczkiewicz

Josh Mutus

Charles Neill

Florian Neukart

Thomas E O'Brien

Bryan O'Gorman

A.G. Petukhov

Harry Putterman

Andrea Skolik

Vadim Smelyanskiy

Doug Strain

Michael Streif

Marco Szalay

Amit Vainsencher

Jamie Yao

Leo Zhou

Edward Farhi

Nature Physics (2021)

Preview abstract
Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning. Accordingly, the possibility of quantum enhanced optimization has driven much interest in quantum technologies. Here we demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA). Like past QAOA experiments, we study performance for problems defined on the planar connectivity graph native to our hardware; however, we also apply the QAOA to the Sherrington–Kirkpatrick model and MaxCut, non-native problems that require extensive compilation to implement. For hardware-native problems, which are classically efficient to solve on average, we obtain an approximation ratio that is independent of problem size and observe that performance increases with circuit depth. For problems requiring compilation, performance decreases with problem size. Circuits involving several thousand gates still present an advantage over random guessing but not over some efficient classical algorithms. Our results suggest that it will be challenging to scale near-term implementations of the QAOA for problems on non-native graphs. As these graphs are closer to real-world instances, we suggest more emphasis should be placed on such problems when using the QAOA to benchmark quantum processors.
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Realizing topologically ordered states on a quantum processor

Y.-J. Liu

A. Smith

C. Knapp

M. Newman

N. C. Jones

Z. Chen

X. Mi

A. Dunsworth

I. Aleiner

F. Arute

K. Arya

J. Atalaya

R. Barends

J. Basso

M. Broughton

B. B. Buckley

N. Bushnell

B. Chiaro

R. Collins

W. Courtney

A. R Derk

D. Eppens

L. Faoro

E. Farhi

B. Foxen

A. Greene

S. D. Harrington

J. Hilton

T. Huang

W. J. Huggins

S. V. Isakov

K. Kechedzhi

A. N. Korotkov

F. Kostritsa

D. Landhuis

P. Laptev

O. Martin

M. Mohseni

S. Montazeri

W. Mruczkiewicz

J. Mutus

C. Neill

T. E. O'Brien

A. Opremcak

B. Pato

A. Petukhov

V. Shvarts

D. Strain

M. Szalay

Z. Yao

P. Yeh

J. Yoo

A. Megrant

Y. Chen

V. Smelyanskiy

A. Kitaev

M. Knap

F. Pollmann

Science, vol. 374 (2021), pp. 1237-1241

Preview abstract
The discovery of topological order has revolutionized the understanding of quantum matter in modern physics and provided the theoretical foundation for many quantum error correcting codes. Realizing topologically ordered states has proven to be extremely challenging in both condensed matter and synthetic quantum systems. Here, we prepare the ground state of the emblematic toric code Hamiltonian using an efficient quantum circuit on a superconducting quantum processor. We measure a topological entanglement entropy of Stopo ≈ −0.95 × ln 2 and simulate anyon interferometry to extract the braiding statistics of the emergent excitations. Furthermore, we investigate key aspects of the surface code, including logical state injection and the decay of the non-local order parameter. Our results illustrate the topological nature of these states and demonstrate their potential for implementing the surface code.
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Power of data in quantum machine learning

Hsin-Yuan (Robert) Huang

Michael Blythe Broughton

Masoud Mohseni

Nature Communications, vol. 12 (2021), pp. 2631

Preview abstract
The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. Using rigorous prediction error bounds as a foundation, we develop a methodology for assessing potential quantum advantage in learning tasks. The bounds are tight asymptotically and empirically predictive for a wide range of learning models. These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems. We then propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime. For near-term implementations, we demonstrate a significant prediction advantage over some classical models on engineered data sets designed to demonstrate a maximal quantum advantage in one of the largest numerical tests for gate-based quantum machine learning to date, up to 30 qubits.
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Virtual Distillation for Quantum Error Mitigation

William J. Huggins

Sam Connor McArdle

Thomas E O'Brien

Joonho Lee

Birgitta Whaley

Physical Review X, vol. 11 (2021), pp. 041036

Preview abstract
Contemporary quantum computers have relatively high levels of noise, making it difficult to use them to perform useful calculations, even with a large number of qubits. Quantum error correction is expected to eventually enable fault-tolerant quantum computation at large scales, but until then it will be necessary to use alternative strategies to mitigate the impact of errors. We propose a near-term friendly strategy to mitigate errors by entangling and measuring \(M\) copies of a noisy state \(\rho\). This enables us to estimate expectation values with respect to a state with dramatically reduced error, \(\rho^M/ \tr(\rho^M)\), without explicitly preparing it, hence the name ``virtual distillation''. As \(M\) increases, this state approaches the closest pure state to \(\rho\), exponentially quickly. We analyze the effectiveness of virtual distillation and find that it is governed in many regimes by the behaviour of this pure state (corresponding to the dominant eigenvector of \(\rho\)). We numerically demonstrate that virtual distillation is capable of suppressing errors by multiple orders of magnitude and explain how this effect is enhanced as the system size grows. Finally, we show that this technique can improve the convergence of randomized quantum algorithms, even in the absence of device noise.
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Error Mitigation via Verified Phase Estimation

Thomas E O'Brien

Stefano Polla

Bill Huggins

Sam Connor McArdle

PRX Quantum, vol. 2 (2021)

Preview abstract
We present a novel error mitigation technique for quantum phase estimation. By post-selecting the system register to be in the starting state, we convert all single errors prior to final measurement to a time-dependent decay (up to on average exponentially small corrections), which may be accurately corrected for at the cost of additional measurement. This error migitation can be built into phase estimation techniques that do not require control qubits. By separating the observable of interest into a linear combination of fast-forwardable Hamiltonians and measuring those components individually, we can convert this decay into a constant offset. Using this technique, we demonstrate the estimation of expectation values on numerical simulations of moderately-sized quantum circuits with multiple orders of magnitude improvement over unmitigated estimation at near-term error rates. We further combine verified phase estimation with the optimization step in a variational algorithm to provide additional mitigation of control error. In many cases, our results demonstrate a clear signature that the verification technique can mitigate against any single error occurring in an instance of a quantum computation: the error $\epsilon$ in the expectation value estimation scales with $p^2$, where $p$ is the probability of an error occurring at any point in the circuit. Further numerics indicate that our scheme remains robust in the presence of sampling noise, though different classical post-processing methods may lead to up to an order of magnitude error increase in the final energy estimates.
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Learnability and Complexity of Quantum Samples

Li Li

Augustus Odena

Zhengli Zhao

Vadim Smelyanskiy

arxiv (2020)

Preview abstract
Given a quantum circuit, a quantum computer can sample the output distribution exponentially
faster in the number of bits than classical computers. A similar exponential separation has yet
to be established in generative models through quantum sample learning: given samples from
an n-qubit computation, can we learn the underlying quantum distribution using models with
training parameters that scale polynomial in n under a fixed training time? We study four kinds of
generative models: Deep Boltzmann machine (DBM), Generative Adversarial Networks (GANs),
Long Short-Term Memory (LSTM) and Autoregressive GAN, on learning quantum data set generated
by deep random circuits. We demonstrate the leading performance of LSTM in learning quantum
samples, and thus the autoregressive structure present in the underlying quantum distribution from
random quantum circuits. Both numerical experiments and a theoretical proof in the case of the
DBM show exponentially growing complexity of learning-agent parameters required for achieving
a fixed accuracy as n increases. Finally, we establish a connection between learnability and the
complexity of generative models by benchmarking learnability against different sets of samples drawn
from probability distributions of variable degrees of complexities in their quantum and classical
representations.
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TensorFlow Quantum: A Software Framework for Quantum Machine Learning

Michael Broughton

Guillaume Verdon

Trevor McCourt

Antonio J. Martinez

Jae Hyeon Yoo

Sergei V. Isakov

Philip Massey

Ramin Halavati

Alexander Zlokapa

Evan Peters

Owen Lockwood

Andrea Skolik

Sofiene Jerbi

Vedran Djunko

Martin Leib

Michael Streif

David Von Dollen

Hongxiang Chen

Chuxiang Cao

Roeland Wiersema

Hsin-Yuan Huang

Alan K. Ho

Masoud Mohseni

(2020)

Preview abstract
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage.
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Hartree-Fock on a Superconducting Qubit Quantum Computer

Frank Carlton Arute

Kunal Arya

Rami Barends

Michael Blythe Broughton

Bob Benjamin Buckley

Nicholas Bushnell

Yu Chen

Jimmy Chen

Benjamin Chiaro

Roberto Collins

William Courtney

Andrew Dunsworth

Edward Farhi

Brooks Riley Foxen

Rob Graff

Steve Habegger

Alan Ho

Trent Huang

William J. Huggins

Sergei Isakov

Cody Jones

Kostyantyn Kechedzhi

Alexander Korotkov

Fedor Kostritsa

Dave Landhuis

Pavel Laptev

Mike Lindmark

Orion Martin

John Martinis

Anthony Megrant

Xiao Mi

Masoud Mohseni

Wojtek Mruczkiewicz

Josh Mutus

Charles Neill

Thomas E O'Brien

Eric Ostby

Andre Gregory Petukhov

Harry Putterman

Vadim Smelyanskiy

Doug Strain

Kevin Jeffery Sung

Marco Szalay

Tyler Y. Takeshita

Amit Vainsencher

Nathan Wiebe

Jamie Yao

Ping Yeh

Science, vol. 369 (2020), pp. 6507

Preview abstract
As the search continues for useful applications of noisy intermediate scale quantum devices, variational simulations of fermionic systems remain one of the most promising directions. Here, we perform a series of quantum simulations of chemistry which involve twice the number of qubits and more than ten times the number of gates as the largest prior experiments. We model the binding energy of ${\rm H}_6$, ${\rm H}_8$, ${\rm H}_{10}$ and ${\rm H}_{12}$ chains as well as the isomerization of diazene. We also demonstrate error-mitigation strategies based on $N$-representability which dramatically improve the effective fidelity of our experiments. Our parameterized ansatz circuits realize the Givens rotation approach to free fermion evolution, which we variationally optimize to prepare the Hartree-Fock wavefunction. This ubiquitous algorithmic primitive corresponds to a rotation of the orbital basis and is required by many proposals for correlated simulations of molecules and Hubbard models. Because free fermion evolutions are classically tractable to simulate, yet still generate highly entangled states over the computational basis, we use these experiments to benchmark the performance of our hardware while establishing a foundation for scaling up more complex correlated quantum simulations of chemistry.
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Accurately computing electronic properties of materials using eigenenergies

Alan Derk

Alan Ho

Alex Opremcak

Alexander Korotkov

Andre Gregory Petukhov

Andrew Dunsworth

Anthony Megrant

Bálint Pató

Benjamin Chiaro

Bob Benjamin Buckley

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

Juan Campero

Kostyantyn Kechedzhi

Kunal Arya

Marco Szalay

Masoud Mohseni

Matt Jacob-Mitos

Matt Trevithick

Michael Blythe 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

William J. Huggins

Wojtek Mruczkiewicz

Xiao Mi

Yu Chen

arXiv preprint arXiv:2012.00921 (2020)

Preview abstract
A promising approach to study quantum materials is to simulate them on an engineered quantum platform. However, achieving the accuracy needed to outperform classical methods has been an outstanding challenge. Here, using superconducting qubits, we provide an experimental blueprint for a programmable and accurate quantum matter simulator and demonstrate how to probe fundamental electronic properties. We illustrate the underlying method by reconstructing the single-particle band-structure of a one-dimensional wire. We demonstrate nearly complete mitigation of decoherence and readout errors and arrive at an accuracy in measuring energy eigenvalues of this wire with an error of ~0.01 radians, whereas typical energy scales are of order 1 radian. Insight into this unprecedented algorithm fidelity is gained by highlighting robust properties of a Fourier transform, including the ability to resolve eigenenergies with a statistical uncertainty of 1e-4 radians. Furthermore, we synthesize magnetic flux and disordered local potentials, two key tenets of a condensed-matter system. When sweeping the magnetic flux, we observe avoided level crossings in the spectrum, a detailed fingerprint of the spatial distribution of local disorder. Combining these methods, we reconstruct electronic properties of the eigenstates where we observe persistent currents and a strong suppression of conductance with added disorder. Our work describes an accurate method for quantum simulation and paves the way to study novel quantum materials with superconducting qubits.
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Demonstrating a Continuous Set of Two-qubit Gates for Near-term Quantum Algorithms

Brooks Riley Foxen

Charles Neill

Andrew Dunsworth

Ben Chiaro

Anthony Megrant

Jimmy Chen

Rami Barends

Frank Carlton Arute

Kunal Arya

Yu Chen

Roberto Collins

Edward Farhi

Rob Graff

Trent Huang

Sergei Isakov

Kostyantyn Kechedzhi

Alexander Korotkov

Fedor Kostritsa

Dave Landhuis

Xiao Mi

Masoud Mohseni

Josh Mutus

Vadim Smelyanskiy

Amit Vainsencher

Jamie Yao

John Martinis

arXiv:2001.08343 (2020)

Preview abstract
Quantum algorithms offer a dramatic speedup for computational problems in machine learning, material science, and chemistry. However, any near-term realizations of these algorithms will need to be heavily optimized to fit within the finite resources offered by existing noisy quantum hardware. Here, taking advantage of the strong adjustable coupling of gmon qubits, we demonstrate a continuous two qubit gate set that can provide a 5x reduction in circuit depth. We implement two gate families: an iSWAP-like gate to attain an arbitrary swap angle, $\theta$, and a CPHASE gate that generates an arbitrary conditional phase, $\phi$. Using one of each of these gates, we can perform an arbitrary two qubit gate within the excitation-preserving subspace allowing for a complete implementation of the so-called Fermionic Simulation, or fSim, gate set. We benchmark the fidelity of the iSWAP-like and CPHASE gate families as well as 525 other fSim gates spread evenly across the entire fSim($\theta$, $\phi$) parameter space achieving purity-limited average two qubit Pauli error of $3.8 \times 10^{-3}$ per fSim gate.
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Quantum Supremacy using a Programmable Superconducting Processor

Frank Arute

Kunal Arya

Rami Barends

Rupak Biswas

Fernando Brandao

David Buell

Yu Chen

Jimmy Chen

Ben Chiaro

Roberto Collins

William Courtney

Andrew Dunsworth

Edward Farhi

Brooks Foxen

Austin Fowler

Rob Graff

Keith Guerin

Steve Habegger

Michael Hartmann

Alan Ho

Trent Huang

Travis Humble

Sergei Isakov

Kostyantyn Kechedzhi

Sergey Knysh

Alexander Korotkov

Fedor Kostritsa

Dave Landhuis

Mike Lindmark

Dmitry Lyakh

Salvatore Mandrà

Anthony Megrant

Xiao Mi

Kristel Michielsen

Masoud Mohseni

Josh Mutus

Charles Neill

Eric Ostby

Andre Petukhov

Eleanor G. Rieffel

Vadim Smelyanskiy

Kevin Jeffery Sung

Matt Trevithick

Amit Vainsencher

Benjamin Villalonga

Z. Jamie Yao

Ping Yeh

John Martinis

Nature, vol. 574 (2019), 505–510

Preview abstract
The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 2^53 (about 10^16). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times-our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy for this specific computational task, heralding a much-anticipated computing paradigm.
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Diabatic gates for frequency-tunable superconducting qubits

Rami Barends

A.G. Petukhov

Yu Chen

Kostyantyn Kechedzhi

Roberto Collins

Frank Carlton Arute

Kunal Arya

Jimmy Chen

Ben Chiaro

Andrew Dunsworth

Brooks Foxen

Rob Graff

Trent Huang

Fedor Kostritsa

Dave Landhuis

Anthony Megrant

Xiao Mi

Josh Mutus

Charles Neill

Eric Ostby

Amit Vainsencher

Jamie Yao

Ping Yeh

Vadim Smelyanskiy

John Martinis

Physical Review Letters, vol. 123 (2019), pp. 210501

Preview abstract
We demonstrate diabatic two-qubit gates with Pauli error rates down to 4.3(2)*10^{-3} in as fast as 18 ns using frequency-tunable superconducting qubits. This is achieved by synchronizing the entangling parameters with minima in the leakage channel. The synchronization shows a landscape in gate parameter space that agrees with model predictions and facilitates robust tune-up. We test both iSWAP-like and CPHASE gates with cross-entropy benchmarking. The presented approach can be extended to multibody operations as well.
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A flexible high-performance simulator for the verification and benchmarking of quantum circuits implemented on real hardware

Benjamin Villalonga

Bron Nelson

Christopher Henze

Eleanor Rieffel

Rupak Biswas

Salvatore Mandra

arXiv:1811.09599 (2018)

Preview abstract
Here we present a flexible tensor network based simulator for quantum circuits on different topologies, including the Google Bristlecone QPU. Our simulator can compute both exact amplitudes, a task essential for the verification of the quantum hardware, as well as low-fidelity amplitudes to mimic Noisy Intermediate-Scale Quantum (NISQ) devices. While our simulator can be used to compute amplitudes of arbitrary quantum circuits, we focus on random quantum circuits (RQCs) [Boixo et al., Nature Physics 14] in the range of sizes expected for supremacy experiments. Our simulator enables the simulation of sampling on quantum circuits that were out of reach for previous approaches. For instance, our simulator is able to output single amplitudes with depth 1+32+1 for the full Google Bristlecone QPU in less than (f · 4200) hours on a single core, where 0 < f ≤ 1 is the target fidelity, on 2 × 20-core Intel Xeon Gold 6148 processors (Skylake). We also estimate that computing 106 amplitudes (with fidelity 0.50%) needed to sample from the full Google Bristlecone QPU with depth (1+32+1) would require about 3.5 days using the NASA Pleiades and Electra supercomputers combined. In addition, we discuss the hardness of the classical simulation of RQCs, as well as give evidence for the higher complexity in the simulation of Google’s Bristlecone topology as compared to other two-dimensional grids with the same number of qubits. Our analysis is supported by extensive simulations on NASA HPC clusters Pleiades (27th in the November 2018 TOP500 list) and Electra (33rd in the November 2018 TOP500 list). For the most computationally demanding simulation we had, namely the simulation of a 60-qubit sub-lattice of Bristlecone, the two HPC clusters combined reached a peak of 20 PFLOPS (single precision), that is 64% of their maximum achievable performance. To date, this numerical computation is the largest in terms of sustained PFLOPS and number of nodes utilized ever run on NASA HPC clusters.
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Barren Plateaus in Quantum Neural Network Training Landscapes

Vadim Smelyanskiy

Nature Communications, vol. 9 (2018), pp. 4812

Preview abstract
Many experimental proposals for noisy intermediate scale quantum devices involve training a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum-classical algorithms are popular for applications in quantum simulation, optimization, and machine learning. Due to its simplicity and hardware efficiency, random circuits are often proposed as initial guesses for exploring the space of quantum states. We show that the exponential dimension of Hilbert space and the gradient estimation complexity make this choice unsuitable for hybrid quantum-classical algorithms run on more than a few qubits. Specifically, we show that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits. We argue that this is related to the 2-design characteristic of random circuits, and that solutions to this problem must be studied.
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Characterizing Quantum Supremacy in Near-Term Devices

Sergei Isakov

Vadim Smelyanskiy

Michael J. Bremner

John Martinis

Nature Physics, vol. 14 (2018), 595–600

Preview abstract
A critical question for quantum computing in the near future is whether quantum devices without error correction can perform a well-defined computational task beyond the capabilities of supercomputers. Such a demonstration of what is referred to as quantum supremacy requires a reliable evaluation of the resources required to solve tasks with classical approaches. Here, we propose the task of sampling from the output distribution of random quantum circuits as a demonstration of quantum supremacy. We extend previous results in computational complexity to argue that this sampling task must take exponential time in a classical computer. We introduce cross-entropy benchmarking to obtain the experimental fidelity of complex multiqubit dynamics. This can be estimated and extrapolated to give a success metric for a quantum supremacy demonstration. We study the computational cost of relevant classical algorithms and conclude that quantum supremacy can be achieved with circuits in a two-dimensional lattice of 7 × 7 qubits and around 40 clock cycles. This requires an error rate of around 0.5% for two-qubit gates (0.05% for one-qubit gates), and it would demonstrate the basic building blocks for a fault-tolerant quantum computer
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Fluctuations of Energy-Relaxation Times in Superconducting Qubits

Jimmy Chen

Anthony Megrant

Rami Barends

Kunal Arya

Ben Chiaro

Yu Chen

Andrew Dunsworth

Brooks Foxen

Rob Graff

Trent Huang

Josh Mutus

Charles Neill

Amit Vainsencher

Jim Wenner

Vadim Smelyanskiy

John Martinis

Physical Review Letters, vol. 121 (2018), pp. 090502

Preview abstract
Superconducting qubits are an attractive platform for quantum computing since they have demonstrated high-fidelity quantum gates and extensibility to modest system sizes. Nonetheless, an outstanding challenge is stabilizing their energy-relaxation times, which can fluctuate unpredictably in frequency and time. Here, we use qubits as spectral and temporal probes of individual two-level-system defects to provide direct evidence that they are responsible for the largest fluctuations. This research lays the foundation for stabilizing qubit performance through calibration, design and fabrication.
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Commercialize Quantum Technologies in Five Years

Masoud Mohseni

Peter Read

Vadim Smelyanskiy

John Martinis

Nature, vol. 543 (2017), 171–174

Preview abstract
Masoud Mohseni, Peter Read, Hartmut Neven and colleagues at Google's Quantum AI Laboratory set out investment opportunities on the road to the ultimate quantum machines.
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Understanding Quantum Tunneling through Quantum Monte Carlo Simulations

Sergei Isakov

Guglielmo Mazzola

Vadim Smelyanskiy

Matthias Troyer

PRL (2016)

Preview abstract
The tunneling between the two ground states of an Ising ferromagnet is a typical example of many-body tunneling processes between two local minima, as they occur during quantum annealing. Performing quantum Monte Carlo (QMC) simulations we find that the QMC tunneling rate displays the same scaling with system size, as the rate of incoherent tunneling. The scaling in both cases is O(Δ2), where Δ is the tunneling splitting. An important consequence is that QMC simulations can be used to predict the performance of a quantum annealer for tunneling through a barrier. Furthermore, by using open instead of periodic boundary conditions in imaginary time, equivalent to a projector QMC algorithm, we obtain a quadratic speedup for QMC, and achieve linear scaling in Δ. We provide a physical understanding of these results and their range of applicability based on an instanton picture.
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What is the Computational Value of Finite Range Tunneling?

Sergei Isakov

Vadim Smelyanskiy

John Martinis

Physical Review X, vol. 6 (2016), pp. 031015

Preview abstract
Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic exploiting tunneling. Here, we demonstrate how finite-range tunneling can provide considerable computational advantage. For a crafted problem designed to have tall and narrow energy barriers separating local minima, the D-Wave 2X quantum annealer achieves significant runtime advantages relative to simulated annealing (SA). For instances with 945 variables, this results in a time-to-99%-success-probability that is ~1e8 times faster than SA running on a single processor core. We also compare physical QA with the quantum Monte Carlo algorithm, an algorithm that emulates quantum tunneling on classical processors. We observe a substantial constant overhead against physical QA: D-Wave 2X again runs up to ~ 1e8 times faster than an optimized implementation of the quantum Monte Carlo algorithm on a single core. We note that there exist heuristic classical algorithms that can solve most instances of Chimera structured problems in a time scale comparable to the D-Wave 2X. However, it is well known that such solvers will become ineffective for sufficiently dense connectivity graphs. To investigate whether finite-range tunneling will also confer an advantage for problems of practical interest, we conduct numerical studies on binary optimization problems that cannot yet be represented on quantum hardware. For random instances of the number partitioning problem, we find numerically that algorithms designed to simulate QA scale better than SA. We discuss the implications of these findings for the design of next-generation quantum annealers.
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Computational multiqubit tunnelling in programmable quantum annealers

Vadim N Smelyanskiy

Alireza Shabani

Sergei V Isakov

Mark Dykman

Mohammad H Amin

Anatoly Yu Smirnov

Masoud Mohseni

Nature Communications, vol. 7 (2016)

Preview abstract
Quantum tunnelling is a phenomenon in which a quantum state traverses energy barriers higher than the energy of the state itself. Quantum tunnelling has been hypothesized as an advantageous physical resource for optimization in quantum annealing. However, computational multiqubit tunnelling has not yet been observed, and a theory of co-tunnelling under high- and low-frequency noises is lacking. Here we show that 8-qubit tunnelling plays a computational role in a currently available programmable quantum annealer. We devise a probe for tunnelling, a computational primitive where classical paths are trapped in a false minimum. In support of the design of quantum annealers we develop a nonperturbative theory of open quantum dynamics under realistic noise characteristics. This theory accurately predicts the rate of many-body dissipative quantum tunnelling subject to the polaron effect. Furthermore, we experimentally demonstrate that quantum tunnelling outperforms thermal hopping along classical paths for problems with up to 200 qubits containing the computational primitive.
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Quantum Algorithms for Discrete Optimization

Preview
Rolando Somma

Quantum Optimization: Fields Institute Communications, Springer (2015) (to appear)

Hearing the Shape of the Ising Model with a Programmable Superconducting-Flux Annealer

Walter Vinci

Klas Markström

Aidan Roy

Federico M. Spedalieri

Paul A. Warburton

Simone Severini

Scientific Reports, vol. 4 (2014)

Preview abstract
Two objects can be distinguished if they have different measurable properties. Thus, distinguishability depends on the Physics of the objects. In considering graphs, we revisit the Ising model as a framework to define physically meaningful spectral invariants. In this context, we introduce a family of refinements of the classical spectrum and consider the quantum partition function. We demonstrate that the energy spectrum of the quantum Ising Hamiltonian is a stronger invariant than the classical one without refinements. For the purpose of implementing the related physical systems, we perform experiments on a programmable annealer with superconducting flux technology. Departing from the paradigm of adiabatic computation, we take advantage of a noisy evolution of the device to generate statistics of low energy states. The graphs considered in the experiments have the same classical partition functions, but different quantum spectra. The data obtained from the annealer distinguish non-isomorphic graphs via information contained in the classical refinements of the functions but not via the differences in the quantum spectra.
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Computational complexity of time-dependent density functional theory

J D Whitfield

M-H Yung

D G Templ

A Aspuru-Guzik

New Journal of Physics, vol. 16 (2014), pp. 083035

Preview abstract
Time-dependent density functional theory (TDDFT) is rapidly emerging as a premier method for solving dynamical many-body problems in physics and chemistry. The mathematical foundations of TDDFT are established through the formal existence of a fictitious non-interacting system (known as the Kohn–Sham system), which can reproduce the one-electron reduced probability density of the actual system. We build upon these works and show that on the interior of the domain of existence, the Kohn–Sham system can be efficiently obtained given the time-dependent density. We introduce a V-representability parameter which diverges at the boundary of the existence domain and serves to quantify the numerical difficulty of constructing the Kohn–Sham potential. For bounded values of V-representability, we present a polynomial time quantum algorithm to generate the time-dependent Kohn–Sham potential with controllable error bounds.
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Defining and detecting quantum speedup

Troels F. Rønnow

Zhihui Wang

Joshua Job

Sergei V. Isakov

David Wecker

John M. Martinis

Daniel A. Lidar

Matthias Troyer

Science, vol. 345 (2014), pp. 420-424

Preview abstract
The development of small-scale quantum devices raises the question of how to fairly assess and detect quantum speedup. Here, we show how to define and measure quantum speedup and how to avoid pitfalls that might mask or fake such a speedup. We illustrate our discussion with data from tests run on a D-Wave Two device with up to 503 qubits. By using random spin glass instances as a benchmark, we found no evidence of quantum speedup when the entire data set is considered and obtained inconclusive results when comparing subsets of instances on an instance-by-instance basis. Our results do not rule out the possibility of speedup for other classes of problems and illustrate the subtle nature of the quantum speedup question. How to benchmark a quantum computer: Quantum machines offer the possibility of performing certain computations much faster than their classical counterparts. However, how to define and measure quantum speedup is a topic of debate. Rønnow et al. describe methods for fairly evaluating the difference in computational power between classical and quantum processors. They define various types of quantum speedup and consider quantum processors that are designed to solve a specific class of problems. Science, this issue p. 420
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Entanglement in a Quantum Annealing Processor

T. Lanting

A. J. Przybysz

A. Yu. Smirnov

F. M. Spedalieri

M. H. Amin

A. J. Berkley

R. Harris

F. Altomare

P. Bunyk

N. Dickson

C. Enderud

J. P. Hilton

E. Hoskinson

M. W. Johnson

E. Ladizinsky

N. Ladizinsky

R. Neufeld

T. Oh

I. Perminov

C. Rich

M. C. Thom

E. Tolkacheva

S. Uchaikin

A. B. Wilson

G. Rose

Physical Review X, vol. 4 (2014), pp. 021041

Preview abstract
Entanglement lies at the core of quantum algorithms designed to solve problems that are intractable by classical approaches. One such algorithm, quantum annealing (QA), provides a promising path to a practical quantum processor. We have built a series of architecturally scalable QA processors consisting of networks of manufactured interacting spins (qubits). Here, we use qubit tunneling spectroscopy to measure the energy eigenspectrum of two- and eight-qubit systems within one such processor, demonstrating quantum coherence in these systems. We present experimental evidence that, during a critical portion of QA, the qubits become entangled and entanglement persists even as these systems reach equilibrium with a thermal environment. Our results provide an encouraging sign that QA is a viable technology for large-scale quantum computing.
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Evidence for quantum annealing with more than one hundred qubits

Troels F. Rønnow

Sergei V. Isakov

Zhihui Wang

David Wecker

Daniel A. Lidar

John M. Martinis

Matthias Troyer

Nature Physics, vol. 10 (2014), pp. 218-224

Introduction to Quantum Algorithms for Physics and Chemistry

Man-Hong Yung

James D. Whitfield

David G. Tempel

Aláan Aspuru-Guzik

Quantum Information and Computation for Chemistry, John Wiley \& Sons, Inc. (2014), pp. 67-106

Demon-like algorithmic quantum cooling and its realization with quantum optics

Jin-Shi Xu

Man-Hong Yung

Xiao-Ye Xu

Zheng-Wei Zhou

Chuan-Feng Li

Alán Aspuru-Guzik

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