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Sergio Boixo

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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    Quantum Supremacy Is Both Closer and Farther than It Appears
    Igor Markov
    Aneeqa Fatima
    Sergei Isakov
    arxiv (not yet submitted) (2018)
    Preview abstract As quantum computers improve in the number of qubits and fidelity, the question of when they surpass state-of-the-art classical computation for a well-defined computational task is attracting much attention. The leading candidate task for this quantum computational supremacy milestone entails sampling from the output distribution defined by a random quantum circuit. We perform this task on conventional computers for larger circuits than in previous results, by trading circuit fidelity for computational resources to match the fidelity of a given quantum computer. By using publicly available Google Cloud Computing, we can price such simulations and enable comparisons by total cost across multiple hardware types. We simulate approximate sampling from the output of a circuit with 7 x 8 qubits and depth 1+40+1 by producing one million bitstring probabilities with fidelity 0.5%, at an estimated cost of $35184. The simulation costs scale linearly with fidelity, and using this scaling we estimate that extending circuit depth to 1+48+1 increases costs to one million dollars. Yet, for a quantum computer, approximate sampling would take seconds. We pay particular attention to validating simulation results. Finally, we explain why recently refined benchmarks substantially increase computation cost of leading simulators, halving the circuit depth that can be simulated within the same time. View details
    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. View details
    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. View details
    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 View details
    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. View details
    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. View details
    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. View details
    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. View details
    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. View details
    Quantum Algorithms for Discrete Optimization
    Rolando Somma
    Quantum Optimization: Fields Institute Communications, Springer (2015) (to appear)
    Preview
    Fast quantum methods for optimization
    G. Ortiz
    R. Somma
    The European Physical Journal Special Topics, vol. 224 (2015), pp. 35
    Preview abstract Discrete combinatorial optimization consists in finding the optimal configuration that minimizes a given discrete objective function. An interpretation of such a function as the energy of a classical system allows us to reduce the optimization problem into the preparation of a low-temperature thermal state of the system. Motivated by the quantum annealing method, we present three strategies to prepare the low-temperature state that exploit quantum mechanics in remarkable ways. We focus on implementations without uncontrolled errors induced by the environment. This allows us to rigorously prove a quantum advantage. The first strategy uses a classical-to-quantum mapping, where the equilibrium properties of a classical system in d spatial dimensions can be determined from the ground state properties of a quantum system also in d spatial dimensions. We show how such a ground state can be prepared by means of quantum annealing, including quantum adiabatic evolutions. This mapping also allows us to unveil some fundamental relations between simulated and quantum annealing. The second strategy builds upon the first one and introduces a technique called spectral gap amplification to reduce the time required to prepare the same quantum state adiabatically. If implemented on a quantum device that exploits quantum coherence, this strategy leads to a quadratic improvement in complexity over the well-known bound of the classical simulated annealing method. The third strategy is not purely adiabatic; instead, it exploits diabatic processes between the low-energy states of the corresponding quantum system. For some problems it results in an exponential speedup (in the oracle model) over the best classical algorithms. View details
    Quantum Algorithms for Simulated Annealing
    Rolando Somma
    Encyclopedia of Algorithms, Springer (2014) (to appear)
    Preview
    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. View details
    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. View details
    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 View details
    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. View details
    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
    Guang-Can Guo
    Nature Photonics, vol. advance online publication (2014)
    Comment on:
    Lei Wang
    Troels F. Rønnow
    Sergei V. Isakov
    Zhihui Wang
    David Wecker
    Daniel A. Lidar
    John M. Martinis
    Matthias Troyer
    arXiv:1305.5837 (2013)
    Experimental signature of programmable quantum annealing
    Tameem Albash
    Federico M. Spedalieri
    Nicholas Chancellor
    Daniel A. Lidar
    Nature Communications, vol. 4 (2013)
    Spectral Gap Amplification
    R. D. Somma
    SIAM Journal on Computing, vol. 42 (2013), pp. 593-610
    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
    arXiv:1304.4595 (2013)
    Entangled and Sequential Quantum Protocols with Dephasing
    Chris Heunen
    Physical Review Letters, vol. 108 (2012), pp. 120402
    Potential energy savings from cool roofs in Spain and Andalusia
    Marian Diaz-Vicente
    Antonio Colmenar
    Manuel Alonso Castro
    Energy, vol. 38 (2012), pp. 425-438
    Quantum adiabatic Markovian master equations
    Tameem Albash
    Daniel A Lidar
    Paolo Zanardi
    New Journal of Physics, vol. 14 (2012), pp. 123016
    Completely positive classical structures and sequentializable quantum protocols
    Chris Heunen
    Electronic Proceedings in Theoretical Computer Science, vol. 95 (2012), pp. 91-101
    Operational interpretations of quantum discord
    D. Cavalcanti
    L. Aolita
    K. Modi
    M. Piani
    A. Winter
    Physical Review A, vol. 83 (2011), pp. 032324
    Quantum locking of classical correlations and quantum discord of classical-quantum states
    L. Aolita
    D. Cavalcanti
    K. Modi
    A. Winter
    International Journal of Quantum Information, vol. 09 (2011), pp. 1643
    Necessary condition for the quantum adiabatic approximation
    R. D. Somma
    Physical Review A, vol. 81 (2010), pp. 032308
    Nonlinear interferometry with Bose-Einstein condensates
    Alexandre B. Tacla
    Animesh Datta
    Anil Shaji
    Carlton M. Caves
    Physical Review A, vol. 82 (2010), pp. 053636
    Preparing Thermal States of Quantum Systems by Dimension Reduction
    Ersen Bilgin
    Physical Review Letters, vol. 105 (2010), pp. 170405
    Fast quantum algorithms for traversing paths of eigenstates
    E. Knill
    R. D Somma
    arXiv:1005.3034 (2010)
    Local Quantum Measurement and No-Signaling Imply Quantum Correlations
    H. Barnum
    S. Beigi
    M. B. Elliott
    S. Wehner
    Physical Review Letters, vol. 104 (2010), pp. 140401
    Quantum metrology from an information theory perspective
    Animesh Datta
    Matthew J. Davis
    Steven T. Flammia
    Anil Shaji
    Alexandre B. Tacla
    Carlton M. Caves
    Quantum Communcation, Measurement and Computing (QCMC): Ninth International Conference on QCMC (2009), pp. 427-432
    Eigenpath traversal by phase randomization
    E. Knill
    R. D Somma
    Quantum Information and Computation, vol. 9 (2009), pp. 833-855
    Quantum-limited metrology and Bose-Einstein condensates
    Animesh Datta
    Matthew J. Davis
    Anil Shaji
    Alexandre B. Tacla
    Carlton M. Caves
    Physical Review A, vol. 80 (2009), pp. 032103-16
    Quantum metrology with Bose-Einstein condensates
    Animesh Datta
    Matthew J. Davis
    Steven T. Flammia
    Anil Shaji
    Alexandre B. Tacla
    Carlton M. Caves
    Quantum Communcation, Measurement and Computing (QCMC): Ninth International Conference on QCMC (2009), pp. 423-426
    Parameter estimation with mixed-state quantum computation
    Rolando D. Somma
    Physical Review A, vol. 77 (2008), pp. 052320-12
    Quantum Metrology: Dynamics versus Entanglement
    Animesh Datta
    Matthew J. Davis
    Steven T. Flammia
    Anil Shaji
    Carlton M. Caves
    Physical Review Letters, vol. 101 (2008), pp. 040403-4
    Operational Interpretation for Global Multipartite Entanglement
    A. Monras
    Physical Review Letters, vol. 100 (2008), pp. 100503-4
    Quantum Simulations of Classical Annealing Processes
    R. D. Somma
    H. Barnum
    E. Knill
    Physical Review Letters, vol. 101 (2008), pp. 130504-4
    Quantum-limited metrology with product states
    Animesh Datta
    Steven T. Flammia
    Anil Shaji
    Emilio Bagan
    Carlton M. Caves
    Physical Review A, vol. 77 (2008), pp. 012317-15
    Generalized Limits for Single-Parameter Quantum Estimation
    Steven T. Flammia
    Carlton M. Caves
    JM Geremia
    Physical Review Letters, vol. 98 (2007), pp. 090401-4
    Quantum Simulated Annealing
    R. Somma
    H. Barnum
    arXiv:0712.1008 (2007)
    Generalized coherent states as preferred states of open quantum systems
    L. Viola
    G. Ortiz
    Europhysics Letters, vol. 79 (2007), pp. 40003
    On decoherence in quantum clock synchronization
    C. M. Caves
    A. Datta
    A. Shaji
    Laser Physics, vol. 16 (2006), pp. 1525-1532