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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Stable quantum-correlated many-body states through engineered dissipation
Sara Shabani
Dripto Debroy
Jerome Lloyd
Alexios Michailidis
Andrew Dunsworth
Bill Huggins
Markus Hoffmann
Alexis Morvan
Josh Cogan
Ben Curtin
Guifre Vidal
Bob Buckley
Tom O'Brien
John Mark Kreikebaum
Rajeev Acharya
Joonho Lee
Ningfeng Zhu
Shirin Montazeri
Sergei Isakov
Jamie Yao
Clarke Smith
Rebecca Potter
Sean Harrington
Jeremy Hilton
Paula Heu
Alexei Kitaev
Alex Crook
Fedor Kostritsa
Kim Ming Lau
Dmitry Abanin
Trent Huang
Aaron Shorter
Steve Habegger
Gina Bortoli
Charles Rocque
Vladimir Shvarts
Alfredo Torres
Anthony Megrant
Charles Neill
Michael Hamilton
Dar Gilboa
Lily Laws
Nicholas Bushnell
Ramis Movassagh
Mike Shearn
Wojtek Mruczkiewicz
Desmond Chik
Leonid Pryadko
Xiao Mi
Brooks Foxen
Frank Arute
Alejo Grajales Dau
Yaxing Zhang
Lara Faoro
Alexander Lill
JiunHow Ng
Justin Iveland
Marco Szalay
Orion Martin
Juhwan Yoo
Michael Newman
William Giang
Alex Opremcak
Amanda Mieszala
William Courtney
Andrey Klots
Wayne Liu
Pavel Laptev
Charina Chou
Paul Conner
Rolando Somma
Vadim Smelyanskiy
Benjamin Chiaro
Grayson Young
Tim Burger
ILYA Drozdov
Agustin Di Paolo
Jimmy Chen
Marika Kieferova
Michael Broughton
Negar Saei
Juan Atalaya
Markus Ansmann
Pavol Juhas
Murray Ich Nguyen
Yuri Lensky
Roberto Collins
Élie Genois
Jindra Skruzny
Igor Aleiner
Yu Chen
Reza Fatemi
Leon Brill
Ashley Huff
Doug Strain
Monica Hansen
Noah Shutty
Ebrahim Forati
Dave Landhuis
Kenny Lee
Ping Yeh
Kunal Arya
Henry Schurkus
Cheng Xing
Cody Jones
Edward Farhi
Raja Gosula
Andre Petukhov
Alexander Korotkov
Ani Nersisyan
Christopher Schuster
George Sterling
Kostyantyn Kechedzhi
Trond Andersen
Alexandre Bourassa
Kannan Sankaragomathi
Vinicius Ferreira
Science, 383 (2024), pp. 1332-1337
Preview abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Dynamics of magnetization at infinite temperature in a Heisenberg spin chain
Tomaž Prosen
Vedika Khemani
Rhine Samajdar
Jesse Hoke
Sarang Gopalakrishnan
Andrew Dunsworth
Bill Huggins
Markus Hoffmann
Alexis Morvan
Josh Cogan
Ben Curtin
Guifre Vidal
Bob Buckley
Tom O'Brien
John Mark Kreikebaum
Rajeev Acharya
Joonho Lee
Ningfeng Zhu
Shirin Montazeri
Sergei Isakov
Jamie Yao
Clarke Smith
Rebecca Potter
Sean Harrington
Jeremy Hilton
Paula Heu
Alexei Kitaev
Alex Crook
Fedor Kostritsa
Kim Ming Lau
Dmitry Abanin
Trent Huang
Aaron Shorter
Steve Habegger
Steven Martin
Gina Bortoli
Seun Omonije
Richard Ross Allen
Charles Rocque
Vladimir Shvarts
Alfredo Torres
Anthony Megrant
Charles Neill
Michael Hamilton
Dar Gilboa
Lily Laws
Nicholas Bushnell
Kyle Anderson
Ramis Movassagh
David Rhodes
Mike Shearn
Wojtek Mruczkiewicz
Desmond Chik
Leonid Pryadko
Xiao Mi
Brooks Foxen
Frank Arute
Alejo Grajales Dau
Yaxing Zhang
Lara Faoro
Alexander Lill
Gordon Hill
JiunHow Ng
Justin Iveland
Marco Szalay
Orion Martin
Juan Campero
Juhwan Yoo
Michael Newman
William Giang
Gonzalo Garcia
Alex Opremcak
Amanda Mieszala
William Courtney
Andrey Klots
Wayne Liu
Pavel Laptev
Paul Conner
Rolando Somma
Vadim Smelyanskiy
Benjamin Chiaro
Grayson Young
Tim Burger
ILYA Drozdov
Agustin Di Paolo
Jimmy Chen
Marika Kieferova
Hung-Shen Chang
Michael Broughton
Negar Saei
Juan Atalaya
Markus Ansmann
Pavol Juhas
Murray Ich Nguyen
Yuri Lensky
Roberto Collins
Élie Genois
Jindra Skruzny
Yu Chen
Reza Fatemi
Leon Brill
Seneca Meeks
Ashley Huff
Doug Strain
Monica Hansen
Noah Shutty
Ebrahim Forati
Doug Thor
Dave Landhuis
Kenny Lee
Ping Yeh
Kunal Arya
Henry Schurkus
Cheng Xing
Cody Jones
Edward Farhi
Vlad Sivak
Raja Gosula
Andre Petukhov
Clint Earle
Alexander Korotkov
Ani Nersisyan
Christopher Schuster
George Sterling
Trond Andersen
Alexandre Bourassa
Salvatore Mandra
Kannan Sankaragomathi
Vinicius Ferreira
Science, 384 (2024), pp. 48-53
Preview abstract
Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain’s center, P(M). The first two moments of P(M) show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Purification-Based Quantum Error Mitigation of Pair-Correlated Electron Simulations
Christian Gogolin
Vincent Elfving
Fotios Gkritsis
Oumarou Oumarou
Gian-Luca R. Anselmetti
Masoud Mohseni
Andrew Dunsworth
William J. Huggins
Markus Rudolf Hoffmann
Alexis Morvan
Josh Godfrey Cogan
Ben Curtin
Guifre Vidal
Bob Benjamin Buckley
Trevor Johnathan Mccourt
Thomas E O'Brien
John Mark Kreikebaum
Rajeev Acharya
Joonho Lee
Ningfeng Zhu
Shirin Montazeri
Sergei Isakov
Jamie Yao
Clarke Smith
Rebecca Potter
Sean Harrington
Jeremy Patterson Hilton
Alex Crook
Fedor Kostritsa
Kim Ming Lau
Dmitry Abanin
Trent Huang
Aaron Shorter
Steve Habegger
Richard Ross Allen
Vladimir Shvarts
Alfredo Torres
Stefano Polla
Anthony Megrant
Charles Neill
Michael C. Hamilton
Dar Gilboa
Lily MeeKit Laws
Nicholas Bushnell
Kyle Anderson
Ramis Movassagh
Mike Shearn
Wojtek Mruczkiewicz
Desmond Chun Fung Chik
Xiao Mi
Brooks Riley Foxen
Frank Carlton Arute
Alejandro Grajales Dau
Yaxing Zhang
Lara Faoro
Alexander T. Lill
Jiun How Ng
Justin Thomas Iveland
Marco Szalay
Orion Martin
Juhwan Yoo
Michael Newman
William Giang
Alex Opremcak
William Courtney
Andrey Klots
Wayne Liu
Pavel Laptev
Paul Conner
Rolando Diego Somma
Vadim Smelyanskiy
Benjamin Chiaro
Grayson Robert Young
Tim Burger
Ilya Drozdov
Jimmy Chen
Marika Kieferova
Michael Blythe Broughton
Juan Atalaya
Markus Ansmann
Pavol Juhas
Murray Nguyen
Daniel Eppens
Roberto Collins
Jindra Skruzny
Igor Aleiner
Yu Chen
Reza Fatemi
Leon Brill
Ashley Anne Huff
Doug Strain
Ebrahim Forati
Dave Landhuis
Kenny Lee
Ping Yeh
Kunal Arya
Cody Jones
Edward Farhi
Andre Gregory Petukhov
Alexander Korotkov
Ani Nersisyan
Christopher Schuster
Kostyantyn Kechedzhi
Trond Ikdahl Andersen
Alexandre Bourassa
Kannan Aryaperumal Sankaragomathi
Nature Physics (2023)
Preview abstract
An important measure of the development of quantum computing platforms has been the simulation of increasingly complex physical systems. Prior to fault-tolerant quantum computing, robust error mitigation strategies are necessary to continue this growth. Here, we study physical simulation within the seniority-zero electron pairing subspace, which affords both a computational stepping stone to a fully correlated model, and an opportunity to validate recently introduced ``purification-based'' error-mitigation strategies. We compare the performance of error mitigation based on doubling quantum resources in time (echo verification) or in space (virtual distillation), on up to 20 qubits of a superconducting qubit quantum processor. We observe a reduction of error by one to two orders of magnitude below less sophisticated techniques (e.g. post-selection); the gain from error mitigation is seen to increase with the system size. Employing these error mitigation strategies enables the implementation of the largest variational algorithm for a correlated chemistry system to-date. Extrapolating performance from these results allows us to estimate minimum requirements for a beyond-classical simulation of electronic structure. We find that, despite the impressive gains from purification-based error mitigation, significant hardware improvements will be required for classically intractable variational chemistry simulations.
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Measurement-induced entanglement and teleportation on a noisy quantum processor
Vedika Khemani
Matteo Ippoliti
Andrew Dunsworth
Bill Huggins
Markus Hoffmann
Alexis Morvan
Josh Cogan
Ben Curtin
Guifre Vidal
Bob Buckley
Tom O'Brien
John Mark Kreikebaum
Rajeev Acharya
Joonho Lee
Ningfeng Zhu
Shirin Montazeri
Sergei Isakov
Jamie Yao
Clarke Smith
Rebecca Potter
Jeremy Hilton
Paula Heu
Alexei Kitaev
Alex Crook
Fedor Kostritsa
Kim Ming Lau
Dmitry Abanin
Trent Huang
Aaron Shorter
Steve Habegger
Gina Bortoli
Seun Omonije
Charles Rocque
Vladimir Shvarts
Alfredo Torres
Anthony Megrant
Charles Neill
Michael Hamilton
Dar Gilboa
Lily Laws
Nicholas Bushnell
Ramis Movassagh
Mike Shearn
Wojtek Mruczkiewicz
Desmond Chik
Leonid Pryadko
Xiao Mi
Brooks Foxen
Frank Arute
Alejo Grajales Dau
Yaxing Zhang
Alexander Lill
JiunHow Ng
Justin Iveland
Marco Szalay
Orion Martin
Juhwan Yoo
Michael Newman
William Giang
Alex Opremcak
Amanda Mieszala
William Courtney
Andrey Klots
Wayne Liu
Pavel Laptev
Paul Conner
Rolando Somma
Vadim Smelyanskiy
Jesse Hoke
Benjamin Chiaro
Grayson Young
Tim Burger
ILYA Drozdov
Agustin Di Paolo
Jimmy Chen
Marika Kieferova
Michael Broughton
Negar Saei
Juan Atalaya
Markus Ansmann
Pavol Juhas
Murray Ich Nguyen
Yuri Lensky
Daniel Eppens
Roberto Collins
Jindra Skruzny
Yu Chen
Reza Fatemi
Leon Brill
Ashley Huff
Doug Strain
Monica Hansen
Noah Shutty
Ebrahim Forati
Dave Landhuis
Kenny Lee
Ping Yeh
Kunal Arya
Henry Schurkus
Cheng Xing
Cody Jones
Edward Farhi
Raja Gosula
Andre Petukhov
Alexander Korotkov
Ani Nersisyan
Christopher Schuster
George Sterling
Kostyantyn Kechedzhi
Trond Andersen
Alexandre Bourassa
Kannan Sankaragomathi
Vinicius Ferreira
Nature, 622 (2023), 481–486
Preview abstract
Measurement has a special role in quantum theory: by collapsing the wavefunction, it can enable phenomena such as teleportation and thereby alter the ‘arrow of time’ that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space–time that go beyond the established paradigms for characterizing phases, either in or out of equilibrium. For present-day noisy intermediate-scale quantum (NISQ) processors, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling to measurement-induced teleportation. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
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Suppressing quantum errors by scaling a surface code logical qubit
Jeremy Hilton
Anthony Megrant
Michael Newman
Vadim Smelyanskiy
Jimmy Chen
Juan Atalaya
Yu Chen
Kenny Lee
Cody Jones
Nature (2023)
Preview abstract
Practical quantum computing will require error rates that are well below what is achievable with
physical qubits. Quantum error correction [1, 2] offers a path to algorithmically-relevant error rates
by encoding logical qubits within many physical qubits, where increasing the number of physical
qubits enhances protection against physical errors. However, introducing more qubits also increases
the number of error sources, so the density of errors must be sufficiently low in order for logical
performance to improve with increasing code size. Here, we report the measurement of logical qubit
performance scaling across multiple code sizes, and demonstrate that our system of superconducting
qubits has sufficient performance to overcome the additional errors from increasing qubit number.
We find our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3
logical qubits on average, both in terms of logical error probability over 25 cycles and logical error
per cycle (2.914%±0.016% compared to 3.028%±0.023%). To investigate damaging, low-probability
error sources, we run a distance-25 repetition code and observe a 1.7 × 10−6 logical error per round
floor set by a single high-energy event (1.6 × 10−7 when excluding this event). We are able to
accurately model our experiment, and from this model we can extract error budgets that highlight
the biggest challenges for future systems. These results mark the first experimental demonstration
where quantum error correction begins to improve performance with increasing qubit number, and
illuminate the path to reaching the logical error rates required for computation.
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Noise-resilient Majorana Edge Modes on a Chain of Superconducting Qubits
Zijun Chen
Brooks Foxen
Masoud Mohseni
Emily Mount
Joao Basso
Andrew Dunsworth
William J. Huggins
Yuan Su
Markus Rudolf Hoffmann
Alexis Morvan
Guifre Vidal
Bob Benjamin Buckley
Thomas E O'Brien
John Mark Kreikebaum
Rajeev Acharya
Joonho Lee
Shirin Montazeri
Sergei Isakov
Jamie Yao
Rebecca Potter
Jeremy Patterson Hilton
Alexei Kitaev
Alex Crook
Fedor Kostritsa
Kim Ming Lau
Dmitry Abanin
Trent Huang
Steve Habegger
Alexa Rubinov
Vladimir Shvarts
Anthony Megrant
Charles Neill
Dar Gilboa
Nicholas Bushnell
Mike Shearn
Wojtek Mruczkiewicz
Xiao Mi
Frank Carlton Arute
Alejandro Grajales Dau
Yaxing Zhang
Lara Faoro
Justin Thomas Iveland
Marco Szalay
Orion Martin
Juhwan Yoo
Michael Newman
William Giang
Alex Opremcak
William Courtney
Andrey Klots
Wayne Liu
Pavel Laptev
Paul Conner
Vadim Smelyanskiy
Benjamin Chiaro
Bernardo Meurer Costa
Michael Blythe Broughton
Juan Atalaya
Daniel Eppens
Roberto Collins
Igor Aleiner
Yu Chen
Reza Fatemi
Leon Brill
Ashley Anne Huff
Doug Strain
Ebrahim Forati
Dave Landhuis
Kenny Lee
Ping Yeh
Kunal Arya
Michel Henri Devoret
Cody Jones
Edward Farhi
Andre Gregory Petukhov
Alexander Korotkov
Christopher Schuster
Kostyantyn Kechedzhi
Trond Ikdahl Andersen
Alexandre Bourassa
Kannan Aryaperumal Sankaragomathi
Science (2022) (to appear)
Preview abstract
Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the kicked Ising model which exhibits Majorana edge modes (MEMs) protected by a $\mathbb{Z}_2$-symmetry. Remarkably, we find that any multi-qubit Pauli operator overlapping with the MEMs exhibits a uniform decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This finding allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Spectroscopic measurements further indicate exponentially suppressed hybridization between the MEMs over larger system sizes, which manifests as a strong resilience against low-frequency noise. Our work elucidates the noise sensitivity of symmetry-protected edge modes in a solid-state environment.
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Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
Michael Streif
Florian Neukart
Andrea Skolik
Martin Leib
Ben Chiaro
Bryan O'Gorman
A.G. Petukhov
Masoud Mohseni
Andrew Dunsworth
Rami Barends
Amit Vainsencher
John Martinis
Josh Mutus
Bob Benjamin Buckley
Thomas E O'Brien
Sergei Isakov
Jamie Yao
Fedor Kostritsa
Steve Habegger
Anthony Megrant
Charles Neill
Nicholas Bushnell
Harry Putterman
Wojtek Mruczkiewicz
Xiao Mi
Leo Zhou
Brooks Riley Foxen
Frank Carlton Arute
Marco Szalay
Orion Martin
William Courtney
Pavel Laptev
Vadim Smelyanskiy
Jimmy Chen
Mike Lindmark
Michael Blythe Broughton
Juan Atalaya
Roberto Collins
Yu Chen
Kevin Jeffery Sung
Doug Strain
Rob Graff
Dave Landhuis
Kunal Arya
Cody Jones
Edward Farhi
Alexander Korotkov
Kostyantyn Kechedzhi
Nature Physics (2021)
Preview abstract
Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning. Accordingly, the possibility of quantum enhanced optimization has driven much interest in quantum technologies. Here we demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA). Like past QAOA experiments, we study performance for problems defined on the planar connectivity graph native to our hardware; however, we also apply the QAOA to the Sherrington–Kirkpatrick model and MaxCut, non-native problems that require extensive compilation to implement. For hardware-native problems, which are classically efficient to solve on average, we obtain an approximation ratio that is independent of problem size and observe that performance increases with circuit depth. For problems requiring compilation, performance decreases with problem size. Circuits involving several thousand gates still present an advantage over random guessing but not over some efficient classical algorithms. Our results suggest that it will be challenging to scale near-term implementations of the QAOA for problems on non-native graphs. As these graphs are closer to real-world instances, we suggest more emphasis should be placed on such problems when using the QAOA to benchmark quantum processors.
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Demonstrating a Continuous Set of Two-qubit Gates for Near-term Quantum Algorithms
Ben Chiaro
Edward Farhi
Masoud Mohseni
Andrew Dunsworth
Rami Barends
Amit Vainsencher
John Martinis
Josh Mutus
Sergei Isakov
Jamie Yao
Fedor Kostritsa
Trent Huang
Anthony Megrant
Charles Neill
Xiao Mi
Brooks Riley Foxen
Frank Carlton Arute
Vadim Smelyanskiy
Jimmy Chen
Roberto Collins
Yu Chen
Rob Graff
Dave Landhuis
Kunal Arya
Alexander Korotkov
Kostyantyn Kechedzhi
arXiv:2001.08343 (2020)
Preview abstract
Quantum algorithms offer a dramatic speedup for computational problems in machine learning, material science, and chemistry. However, any near-term realizations of these algorithms will need to be heavily optimized to fit within the finite resources offered by existing noisy quantum hardware. Here, taking advantage of the strong adjustable coupling of gmon qubits, we demonstrate a continuous two qubit gate set that can provide a 5x reduction in circuit depth. We implement two gate families: an iSWAP-like gate to attain an arbitrary swap angle, $\theta$, and a CPHASE gate that generates an arbitrary conditional phase, $\phi$. Using one of each of these gates, we can perform an arbitrary two qubit gate within the excitation-preserving subspace allowing for a complete implementation of the so-called Fermionic Simulation, or fSim, gate set. We benchmark the fidelity of the iSWAP-like and CPHASE gate families as well as 525 other fSim gates spread evenly across the entire fSim($\theta$, $\phi$) parameter space achieving purity-limited average two qubit Pauli error of $3.8 \times 10^{-3}$ per fSim gate.
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Hartree-Fock on a Superconducting Qubit Quantum Computer
Nathan Wiebe
Tyler Y. Takeshita
Masoud Mohseni
Andrew Dunsworth
Alan Ho
William J. Huggins
Eric Ostby
Rami Barends
Amit Vainsencher
John Martinis
Josh Mutus
Bob Benjamin Buckley
Thomas E O'Brien
Sergei Isakov
Jamie Yao
Fedor Kostritsa
Trent Huang
Steve Habegger
Anthony Megrant
Charles Neill
Nicholas Bushnell
Harry Putterman
Wojtek Mruczkiewicz
Xiao Mi
Brooks Riley Foxen
Frank Carlton Arute
Marco Szalay
Orion Martin
William Courtney
Pavel Laptev
Vadim Smelyanskiy
Benjamin Chiaro
Jimmy Chen
Mike Lindmark
Michael Blythe Broughton
Roberto Collins
Yu Chen
Kevin Jeffery Sung
Doug Strain
Rob Graff
Dave Landhuis
Ping Yeh
Kunal Arya
Cody Jones
Edward Farhi
Andre Gregory Petukhov
Alexander Korotkov
Kostyantyn Kechedzhi
Science, 369 (2020), pp. 6507
Preview abstract
As the search continues for useful applications of noisy intermediate scale quantum devices, variational simulations of fermionic systems remain one of the most promising directions. Here, we perform a series of quantum simulations of chemistry which involve twice the number of qubits and more than ten times the number of gates as the largest prior experiments. We model the binding energy of ${\rm H}_6$, ${\rm H}_8$, ${\rm H}_{10}$ and ${\rm H}_{12}$ chains as well as the isomerization of diazene. We also demonstrate error-mitigation strategies based on $N$-representability which dramatically improve the effective fidelity of our experiments. Our parameterized ansatz circuits realize the Givens rotation approach to free fermion evolution, which we variationally optimize to prepare the Hartree-Fock wavefunction. This ubiquitous algorithmic primitive corresponds to a rotation of the orbital basis and is required by many proposals for correlated simulations of molecules and Hubbard models. Because free fermion evolutions are classically tractable to simulate, yet still generate highly entangled states over the computational basis, we use these experiments to benchmark the performance of our hardware while establishing a foundation for scaling up more complex correlated quantum simulations of chemistry.
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TensorFlow Quantum: A Software Framework for Quantum Machine Learning
Martin Leib
Owen Lockwood
Sofiene Jerbi
Alexander Zlokapa
Roeland Wiersema
Hongxiang Chen
Trevor McCourt
David Von Dollen
Chuxiang Cao
Vedran Djunko
Hsin-Yuan Huang
Evan Peters
Michael Streif
Andrea Skolik
Antonio J. Martinez
Masoud Mohseni
Alan K. Ho
Guillaume Verdon
Sergei V. Isakov
Michael Broughton
Philip Massey
Ramin Halavati
(2020)
Preview abstract
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage.
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