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Dave Bacon

Dave Bacon

Dave Bacon is a Senior Staff Software Engineer at Google. Prior to Google he was a research assistant professor at the University of Washington. His research interests include quantum computing and machine intelligence.
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    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
    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
    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
    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
    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
    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
    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
    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
    Preview abstract We consider quantum algorithms for the unique sink orientation problem on cubes. This problem is widely considered to be of intermediate computational complexity. This is because there is no known polynomial algorithm (classical or quantum) for the problem and yet it arises as part of a series of problems for which it being intractable would imply complexity-theoretic collapses. We give a reduction which proves that if one can efficiently evaluate the kth power of the unique sink orientation outmap, then there exists a polynomial time quantum algorithm for the unique sink orientation problem on cubes. View details
    Preview abstract Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model with training data distributed over a large number of clients each with unreliable and relatively slow network connections. We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global model. The typical clients in this setting are mobile phones, and communication efficiency is of utmost importance. In this paper, we propose two ways to reduce the uplink communication costs. The proposed methods are evaluated on the application of training a deep neural network to perform image classification. Our best approach reduces the upload communication required to train a reasonable model by two orders of magnitude. View details
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