Zhang Jiang
Zhang is part of Google's quantum AI team. He worked at NASA Ames research center before joining Google. His main interests include quantum control, quantum simulation, and quantum optimization.
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Stable quantum-correlated many-body states through engineered dissipation
Xiao Mi
Alexios Michailidis
Sara Shabani
Jerome Lloyd
Rajeev Acharya
Igor Aleiner
Trond Andersen
Markus Ansmann
Frank Arute
Kunal Arya
Juan Atalaya
Gina Bortoli
Alexandre Bourassa
Leon Brill
Michael Broughton
Bob Buckley
Tim Burger
Nicholas Bushnell
Jimmy Chen
Benjamin Chiaro
Desmond Chik
Charina Chou
Josh Cogan
Roberto Collins
Paul Conner
William Courtney
Alex Crook
Ben Curtin
Alejo Grajales Dau
Dripto Debroy
Agustin Di Paolo
ILYA Drozdov
Andrew Dunsworth
Lara Faoro
Edward Farhi
Reza Fatemi
Vinicius Ferreira
Ebrahim Forati
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
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
Science, vol. 383 (2024), pp. 1332-1337
Preview abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Dynamics of magnetization at infinite temperature in a Heisenberg spin chain
Trond Andersen
Rhine Samajdar
Andre Petukhov
Jesse Hoke
Dmitry Abanin
ILYA Drozdov
Xiao Mi
Alexis Morvan
Charles Neill
Rajeev Acharya
Richard Ross Allen
Kyle Anderson
Markus Ansmann
Frank Arute
Kunal Arya
Juan Atalaya
Gina Bortoli
Alexandre Bourassa
Leon Brill
Michael Broughton
Bob Buckley
Tim Burger
Nicholas Bushnell
Juan Campero
Hung-Shen Chang
Jimmy Chen
Benjamin Chiaro
Desmond Chik
Josh Cogan
Roberto Collins
Paul Conner
William Courtney
Alex Crook
Ben Curtin
Agustin Di Paolo
Andrew Dunsworth
Clint Earle
Lara Faoro
Edward Farhi
Reza Fatemi
Vinicius Ferreira
Ebrahim Forati
Brooks Foxen
Gonzalo Garcia
Élie Genois
William Giang
Dar Gilboa
Raja Gosula
Alejo Grajales Dau
Steve Habegger
Michael Hamilton
Monica Hansen
Sean Harrington
Paula Heu
Gordon Hill
Trent Huang
Ashley Huff
Bill Huggins
Sergei Isakov
Justin Iveland
Cody Jones
Pavol Juhas
Marika Kieferova
Alexei Kitaev
Andrey Klots
Alexander Korotkov
Fedor Kostritsa
John Mark Kreikebaum
Dave Landhuis
Pavel Laptev
Kim Ming Lau
Lily Laws
Joonho Lee
Kenny Lee
Yuri Lensky
Alexander Lill
Wayne Liu
Salvatore Mandra
Orion Martin
Steven Martin
Seneca Meeks
Amanda Mieszala
Shirin Montazeri
Ramis Movassagh
Wojtek Mruczkiewicz
Ani Nersisyan
Michael Newman
JiunHow Ng
Murray Ich Nguyen
Tom O'Brien
Seun Omonije
Alex Opremcak
Rebecca Potter
Leonid Pryadko
David Rhodes
Charles Rocque
Negar Saei
Kannan Sankaragomathi
Henry Schurkus
Christopher Schuster
Mike Shearn
Aaron Shorter
Noah Shutty
Vladimir Shvarts
Vlad Sivak
Jindra Skruzny
Clarke Smith
Rolando Somma
George Sterling
Doug Strain
Marco Szalay
Doug Thor
Alfredo Torres
Guifre Vidal
Cheng Xing
Jamie Yao
Ping Yeh
Juhwan Yoo
Grayson Young
Yaxing Zhang
Ningfeng Zhu
Jeremy Hilton
Anthony Megrant
Yu Chen
Vadim Smelyanskiy
Vedika Khemani
Sarang Gopalakrishnan
Tomaž Prosen
Science, vol. 384 (2024), pp. 48-53
Preview abstract
Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain’s center, P(M). The first two moments of P(M) show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Measurement-induced entanglement and teleportation on a noisy quantum processor
Jesse Hoke
Matteo Ippoliti
Dmitry Abanin
Rajeev Acharya
Trond Andersen
Markus Ansmann
Frank Arute
Kunal Arya
Juan Atalaya
Gina Bortoli
Alexandre Bourassa
Leon Brill
Michael Broughton
Bob Buckley
Tim Burger
Nicholas Bushnell
Jimmy Chen
Benjamin Chiaro
Desmond Chik
Josh Cogan
Roberto Collins
Paul Conner
William Courtney
Alex Crook
Ben Curtin
Alejo Grajales Dau
Agustin Di Paolo
ILYA Drozdov
Andrew Dunsworth
Daniel Eppens
Edward Farhi
Reza Fatemi
Vinicius Ferreira
Ebrahim Forati
Brooks Foxen
William Giang
Dar Gilboa
Raja Gosula
Steve Habegger
Michael Hamilton
Monica Hansen
Paula Heu
Trent Huang
Ashley Huff
Bill Huggins
Sergei Isakov
Justin Iveland
Cody Jones
Pavol Juhas
Kostyantyn Kechedzhi
Marika Kieferova
Alexei Kitaev
Andrey Klots
Alexander Korotkov
Fedor Kostritsa
John Mark Kreikebaum
Dave Landhuis
Pavel Laptev
Kim Ming Lau
Lily Laws
Joonho Lee
Kenny Lee
Yuri Lensky
Alexander Lill
Wayne Liu
Orion Martin
Amanda Mieszala
Shirin Montazeri
Alexis Morvan
Ramis Movassagh
Wojtek Mruczkiewicz
Charles Neill
Ani Nersisyan
Michael Newman
JiunHow Ng
Murray Ich Nguyen
Tom O'Brien
Seun Omonije
Alex Opremcak
Andre Petukhov
Rebecca Potter
Leonid Pryadko
Charles Rocque
Negar Saei
Kannan Sankaragomathi
Henry Schurkus
Christopher Schuster
Mike Shearn
Aaron Shorter
Noah Shutty
Vladimir Shvarts
Jindra Skruzny
Clarke Smith
Rolando Somma
George Sterling
Doug Strain
Marco Szalay
Alfredo Torres
Guifre Vidal
Cheng Xing
Jamie Yao
Ping Yeh
Juhwan Yoo
Grayson Young
Yaxing Zhang
Ningfeng Zhu
Jeremy Hilton
Anthony Megrant
Yu Chen
Vadim Smelyanskiy
Xiao Mi
Vedika Khemani
Nature, vol. 622 (2023), 481–486
Preview abstract
Measurement has a special role in quantum theory: by collapsing the wavefunction, it can enable phenomena such as teleportation and thereby alter the ‘arrow of time’ that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space–time that go beyond the established paradigms for characterizing phases, either in or out of equilibrium. For present-day noisy intermediate-scale quantum (NISQ) processors, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling to measurement-induced teleportation. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
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Noise-resilient Majorana Edge Modes on a Chain of Superconducting Qubits
Alejandro Grajales Dau
Alex Crook
Alex Opremcak
Alexa Rubinov
Alexander Korotkov
Alexandre Bourassa
Alexei Kitaev
Alexis Morvan
Andre Gregory Petukhov
Andrew Dunsworth
Andrey Klots
Anthony Megrant
Ashley Anne Huff
Benjamin Chiaro
Bernardo Meurer Costa
Bob Benjamin Buckley
Brooks Foxen
Charles Neill
Christopher Schuster
Cody Jones
Daniel Eppens
Dar Gilboa
Dave Landhuis
Dmitry Abanin
Doug Strain
Ebrahim Forati
Edward Farhi
Emily Mount
Fedor Kostritsa
Frank Carlton Arute
Guifre Vidal
Igor Aleiner
Jamie Yao
Jeremy Patterson Hilton
Joao Basso
John Mark Kreikebaum
Joonho Lee
Juan Atalaya
Juhwan Yoo
Justin Thomas Iveland
Kannan Aryaperumal Sankaragomathi
Kenny Lee
Kim Ming Lau
Kostyantyn Kechedzhi
Kunal Arya
Lara Faoro
Leon Brill
Marco Szalay
Masoud Mohseni
Michael Blythe Broughton
Michael Newman
Michel Henri Devoret
Mike Shearn
Nicholas Bushnell
Orion Martin
Paul Conner
Pavel Laptev
Ping Yeh
Rajeev Acharya
Rebecca Potter
Reza Fatemi
Roberto Collins
Sergei Isakov
Shirin Montazeri
Steve Habegger
Thomas E O'Brien
Trent Huang
Trond Ikdahl Andersen
Vadim Smelyanskiy
Vladimir Shvarts
Wayne Liu
William Courtney
William Giang
William J. Huggins
Wojtek Mruczkiewicz
Xiao Mi
Yaxing Zhang
Yu Chen
Yuan Su
Zijun Chen
Science (2022) (to appear)
Preview abstract
Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the kicked Ising model which exhibits Majorana edge modes (MEMs) protected by a $\mathbb{Z}_2$-symmetry. Remarkably, we find that any multi-qubit Pauli operator overlapping with the MEMs exhibits a uniform decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This finding allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Spectroscopic measurements further indicate exponentially suppressed hybridization between the MEMs over larger system sizes, which manifests as a strong resilience against low-frequency noise. Our work elucidates the noise sensitivity of symmetry-protected edge modes in a solid-state environment.
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Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
Kevin Jeffery Sung
Frank Carlton Arute
Kunal Arya
Juan Atalaya
Rami Barends
Michael Blythe Broughton
Bob Benjamin Buckley
Nicholas Bushnell
Jimmy Chen
Yu Chen
Ben Chiaro
Roberto Collins
William Courtney
Andrew Dunsworth
Brooks Riley Foxen
Rob Graff
Steve Habegger
Sergei Isakov
Cody Jones
Kostyantyn Kechedzhi
Alexander Korotkov
Fedor Kostritsa
Dave Landhuis
Pavel Laptev
Martin Leib
Mike Lindmark
Orion Martin
John Martinis
Anthony Megrant
Xiao Mi
Masoud Mohseni
Wojtek Mruczkiewicz
Josh Mutus
Charles Neill
Florian Neukart
Thomas E O'Brien
Bryan O'Gorman
A.G. Petukhov
Harry Putterman
Andrea Skolik
Vadim Smelyanskiy
Doug Strain
Michael Streif
Marco Szalay
Amit Vainsencher
Jamie Yao
Leo Zhou
Edward Farhi
Nature Physics (2021)
Preview abstract
Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning. Accordingly, the possibility of quantum enhanced optimization has driven much interest in quantum technologies. Here we demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA). Like past QAOA experiments, we study performance for problems defined on the planar connectivity graph native to our hardware; however, we also apply the QAOA to the Sherrington–Kirkpatrick model and MaxCut, non-native problems that require extensive compilation to implement. For hardware-native problems, which are classically efficient to solve on average, we obtain an approximation ratio that is independent of problem size and observe that performance increases with circuit depth. For problems requiring compilation, performance decreases with problem size. Circuits involving several thousand gates still present an advantage over random guessing but not over some efficient classical algorithms. Our results suggest that it will be challenging to scale near-term implementations of the QAOA for problems on non-native graphs. As these graphs are closer to real-world instances, we suggest more emphasis should be placed on such problems when using the QAOA to benchmark quantum processors.
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Realizing topologically ordered states on a quantum processor
Y.-J. Liu
A. Smith
C. Knapp
M. Newman
N. C. Jones
Z. Chen
X. Mi
A. Dunsworth
I. Aleiner
F. Arute
K. Arya
J. Atalaya
R. Barends
J. Basso
M. Broughton
B. B. Buckley
N. Bushnell
B. Chiaro
R. Collins
W. Courtney
A. R Derk
D. Eppens
L. Faoro
E. Farhi
B. Foxen
A. Greene
S. D. Harrington
J. Hilton
T. Huang
W. J. Huggins
S. V. Isakov
K. Kechedzhi
A. N. Korotkov
F. Kostritsa
D. Landhuis
P. Laptev
O. Martin
M. Mohseni
S. Montazeri
W. Mruczkiewicz
J. Mutus
C. Neill
T. E. O'Brien
A. Opremcak
B. Pato
A. Petukhov
V. Shvarts
D. Strain
M. Szalay
Z. Yao
P. Yeh
J. Yoo
A. Megrant
Y. Chen
V. Smelyanskiy
A. Kitaev
M. Knap
F. Pollmann
Science, vol. 374 (2021), pp. 1237-1241
Preview abstract
The discovery of topological order has revolutionized the understanding of quantum matter in modern physics and provided the theoretical foundation for many quantum error correcting codes. Realizing topologically ordered states has proven to be extremely challenging in both condensed matter and synthetic quantum systems. Here, we prepare the ground state of the emblematic toric code Hamiltonian using an efficient quantum circuit on a superconducting quantum processor. We measure a topological entanglement entropy of Stopo ≈ −0.95 × ln 2 and simulate anyon interferometry to extract the braiding statistics of the emergent excitations. Furthermore, we investigate key aspects of the surface code, including logical state injection and the decay of the non-local order parameter. Our results illustrate the topological nature of these states and demonstrate their potential for implementing the surface code.
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Efficient and Noise Resilient Measurements for Quantum Chemistry on Near-Term Quantum Computers
William Huggins
Nathan Wiebe
K. Birgitta Whaley
Nature Quantum Information, vol. 7 (2021)
Preview abstract
Variational algorithms are a promising paradigm for utilizing near-term quantum devices for modeling electronic states of molecular systems. However, previous bounds on the measurement time required have suggested that the application of these techniques to larger molecules might be infeasible. We present a measurement strategy based on a low-rank factorization of the two-electron integral tensor. Our approach provides a cubic reduction in term groupings over prior state-of-the-art and enables measurement times three orders of magnitude smaller than those suggested by commonly referenced bounds for the largest systems we consider. Although our technique requires execution of a linear-depth circuit prior to measurement, this is compensated for by eliminating challenges associated with sampling nonlocal Jordan–Wigner transformed operators in the presence of measurement error, while enabling a powerful form of error mitigation based on efficient postselection. We numerically characterize these benefits with noisy quantum circuit simulations for ground-state energies of strongly correlated electronic systems.
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Exponential suppression of bit or phase flip errors with repetitive quantum error correction
Alan Derk
Alan Ho
Alex Opremcak
Alexander Korotkov
Alexandre Bourassa
Andre Gregory Petukhov
Andrew Dunsworth
Anthony Megrant
Bálint Pató
Benjamin Chiaro
Brooks Riley Foxen
Charles Neill
Cody Jones
Daniel Eppens
Dave Landhuis
Doug Strain
Edward Farhi
Eric Ostby
Fedor Kostritsa
Frank Carlton Arute
Igor Aleiner
Jamie Yao
Jeremy Patterson Hilton
Jimmy Chen
Josh Mutus
Juan Atalaya
Kostyantyn Kechedzhi
Kunal Arya
Marco Szalay
Masoud Mohseni
Matt Trevithick
Michael Broughton
Michael Newman
Nicholas Bushnell
Nicholas Redd
Orion Martin
Pavel Laptev
Ping Yeh
Rami Barends
Roberto Collins
Sean Harrington
Sergei Isakov
Thomas E O'Brien
Trent Huang
Trevor Mccourt
Vadim Smelyanskiy
Vladimir Shvarts
William Courtney
Wojtek Mruczkiewicz
Xiao Mi
Yu Chen
Nature (2021)
Preview abstract
Realizing the potential of quantum computing will require achieving sufficiently low logical error rates. Many applications call for error rates below 10^-15, but state-of-the-art quantum platforms typically have physical error rates near 10^-3. Quantum error correction (QEC) promises to bridge this divide by distributing quantum logical information across many physical qubits so that errors can be corrected. Logical errors are then exponentially suppressed as the number of physical qubits grows, provided that the physical error rates are below a certain threshold. QEC also requires that the errors are local, and that performance is maintained over many rounds of error correction, a major outstanding experimental challenge. Here, we implement 1D repetition codes embedded in a 2D grid of superconducting qubits which demonstrate exponential suppression of bit or phase-flip errors, reducing logical error per round by more than 100x when increasing the number of qubits from 5 to 21. Crucially, this error suppression is stable over 50 rounds of error correction. We also introduce a method for analyzing error correlations with high precision, and characterize the locality of errors in a device performing QEC for the first time. Finally, we perform error detection using a small 2D surface code logical qubit on the same device, and show that the results from both 1D and 2D codes agree with numerical simulations using a simple depolarizing error model. These findings demonstrate that superconducting qubits are on a viable path towards fault tolerant quantum computing.
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Tuning Quantum Information Scrambling on a 53-Qubit Processor
Alan Derk
Alan Ho
Alex Opremcak
Alexander Korotkov
Alexandre Bourassa
Andre Gregory Petukhov
Andrew Dunsworth
Anthony Megrant
Bálint Pató
Benjamin Chiaro
Brooks Riley Foxen
Charles Neill
Cody Jones
Daniel Eppens
Dave Landhuis
Doug Strain
Edward Farhi
Eric Ostby
Fedor Kostritsa
Frank Carlton Arute
Igor Aleiner
Jamie Yao
Jeffrey Marshall
Jeremy Patterson Hilton
Jimmy Chen
Josh Mutus
Juan Atalaya
Kostyantyn Kechedzhi
Kunal Arya
Marco Szalay
Masoud Mohseni
Matt Trevithick
Michael Blythe Broughton
Michael Newman
Nicholas Bushnell
Nicholas Redd
Orion Martin
Pavel Laptev
Ping Yeh
Rami Barends
Roberto Collins
Salvatore Mandra
Sean Harrington
Sergei Isakov
Thomas E O'Brien
Trent Huang
Trevor Mccourt
Vadim Smelyanskiy
Vladimir Shvarts
William Courtney
Wojtek Mruczkiewicz
Xiao Mi
Yu Chen
arXiv (2021)
Preview abstract
As entanglement in a quantum system grows, initially localized quantum information is spread into the exponentially many degrees of freedom of the entire system. This process, known as quantum scrambling, is computationally intensive to study classically and lies at the heart of several modern physics conundrums. Here, we characterize scrambling of different quantum circuits on a 53-qubit programmable quantum processor by measuring their out-of-time-order correlators (OTOCs). We observe that the spatiotemporal spread of OTOCs, as well as their circuit-to-circuit fluctuation, unravel in detail the time-scale and extent of quantum scrambling. Comparison with numerical results indicates a high OTOC measurement accuracy despite the large size of the quantum system. Our work establishes OTOC as an experimental tool to diagnose quantum scrambling at the threshold of being classically inaccessible.
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Low-Depth Mechanisms for Quantum Optimization
Masoud Mohseni
Vadim Smelyanskiy
PRX Quantum, vol. 3 (2021), pp. 030312
Preview abstract
One of the major application areas of interest for both near-term and fault-tolerant quantum computers is the optimization of classical objective functions. In this work, we develop intuitive constructions for a large class of these algorithms based on connections to simple dynamics of quantum systems, quantum walks, and classical continuous relaxations. We focus on developing a language and tools connected with kinetic energy on a graph for understanding the physical mechanisms of success and failure to guide algorithmic improvement. This physical language, in combination with uniqueness results related to unitarity, allow us to identify some potential pitfalls from kinetic energy fundamentally opposing the goal of optimization. This is connected to effects from wavefunction confinement, phase randomization, and shadow defects lurking in the objective far away from the ideal solution. As an example, we explore the surprising deficiency of many quantum methods in solving uncoupled spin problems and how this is both predictive of performance on some more complex systems while immediately suggesting simple resolutions. Further examination of canonical problems like the Hamming ramp or bush of implications show that entanglement can be strictly detrimental to performance results from the underlying mechanism of solution in approaches like QAOA. Kinetic energy and graph Laplacian perspectives provide new insights to common initialization and optimal solutions in QAOA as well as new methods for more effective layerwise training. Connections to classical methods of continuous extensions, homotopy methods, and iterated rounding suggest new directions for research in quantum optimization. Throughout, we unveil many pitfalls and mechanisms in quantum optimization using a physical perspective, which aim to spur the development of novel quantum optimization algorithms and refinements.
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Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization
Ian Kivlichan
Dominic Berry
Wei Sun
Alán Aspuru-Guzik
Quantum, vol. 4 (2020), pp. 296
Preview abstract
Recent work has deployed linear combinations of unitaries techniques to significantly reduce the cost of performing fault-tolerant quantum simulations of correlated electron models. Here, we show that one can sometimes improve over those results with optimized implementations of Trotter-Suzuki-based product formulas. We show that low-order Trotter methods perform surprisingly well when used with phase estimation to compute relative precision quantities (e.g. energy per unit cell), as is often the goal for condensed-phase (e.g. solid-state) systems. In this context, simulations of the Hubbard model and plane wave electronic structure models with $N < 10^5$ fermionic modes can be performed with roughly O(1) and O(N^2) T complexities. We also perform numerics that reveal tradeoffs between the error of a Trotter step and Trotter step gate complexity across various implementations; e.g., we show that split-operator techniques have less Trotter error than popular alternatives. By compiling to surface code fault-tolerant gates using lattice surgery and assuming error rates of one part in a thousand, we show that one can error-correct quantum simulations of interesting, classically intractable instances with only a few hundred thousand physical qubits.
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Decoding Quantum Errors Using Subspace Expansions
Nature Communications, vol. 11 (2020), pp. 636
Preview abstract
With the rapid developments in quantum hardware comes a push towards the first practical applications on these devices. While fully fault-tolerant quantum computers may still be years away, one may ask if there exist intermediate forms of error correction or mitigation that might enable practical applications before then. In this work, we consider the idea of post-processing error decoders using existing quantum codes, which are capable of mitigating errors on encoded logical qubits using classical post-processing with no complicated syndrome measurements or additional qubits beyond those used for the logical qubits. This greatly simplifies the experimental exploration of quantum codes on near-term devices, removing the need for locality of syndromes or fast feed-forward, allowing one to study performance aspects of codes on real devices. We provide a general construction equipped with a simple stochastic sampling scheme that does not depend explicitly on a number of terms that we extend to approximate projectors within a subspace. This theory then allows one to generalize to the correction of some logical errors in the code space, correction of some physical unencoded Hamiltonians without engineered symmetries, and corrections derived from approximate symmetries. In this work, we develop the theory of the method and demonstrate it on a simple example with the perfect [[5,1,3]] code, which exhibits a pseudo-threshold of p≈0.50 under a single qubit depolarizing channel applied to all qubits. We also provide a demonstration under the application of a logical operation and performance on an unencoded hydrogen molecule, which exhibits a significant improvement over the entire range of possible errors incurred under a depolarizing channel.
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Demonstrating a Continuous Set of Two-qubit Gates for Near-term Quantum Algorithms
Brooks Riley Foxen
Charles Neill
Andrew Dunsworth
Ben Chiaro
Anthony Megrant
Jimmy Chen
Rami Barends
Frank Carlton Arute
Kunal Arya
Yu Chen
Roberto Collins
Edward Farhi
Rob Graff
Trent Huang
Sergei Isakov
Kostyantyn Kechedzhi
Alexander Korotkov
Fedor Kostritsa
Dave Landhuis
Xiao Mi
Masoud Mohseni
Josh Mutus
Vadim Smelyanskiy
Amit Vainsencher
Jamie Yao
John Martinis
arXiv:2001.08343 (2020)
Preview abstract
Quantum algorithms offer a dramatic speedup for computational problems in machine learning, material science, and chemistry. However, any near-term realizations of these algorithms will need to be heavily optimized to fit within the finite resources offered by existing noisy quantum hardware. Here, taking advantage of the strong adjustable coupling of gmon qubits, we demonstrate a continuous two qubit gate set that can provide a 5x reduction in circuit depth. We implement two gate families: an iSWAP-like gate to attain an arbitrary swap angle, $\theta$, and a CPHASE gate that generates an arbitrary conditional phase, $\phi$. Using one of each of these gates, we can perform an arbitrary two qubit gate within the excitation-preserving subspace allowing for a complete implementation of the so-called Fermionic Simulation, or fSim, gate set. We benchmark the fidelity of the iSWAP-like and CPHASE gate families as well as 525 other fSim gates spread evenly across the entire fSim($\theta$, $\phi$) parameter space achieving purity-limited average two qubit Pauli error of $3.8 \times 10^{-3}$ per fSim gate.
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Creating and manipulating a Laughlin-type ν=1/3 fractional quantum Hall state on a quantum computer with linear depth circuits
Armin Rahmani
Kevin J. Sung
Harald Putterman
Pouyan Ghaemi
PRX Quantum, vol. 1 (2020), pp. 020309
Preview abstract
Here we present an efficient quantum algorithm to generate an equivalent many-body state to Laughlin’s ν= 1/3 fractional quantum Hall state on a digitized quantum computer. Our algorithm only uses quantum gates acting on neighboring qubits in a quasi one-dimensional setting, and its circuit depth is linear in the number of qubits, i.e., the number of Landau levels in the second quantized picture. We identify correlation functions that serve as signatures of the Laughlin state and discuss how to obtain them on a quantum computer. We also discuss a generalization of the algorithm for creating quasiparticles in the Laughlin state. This paves the way for several important studies, including quantum simulation of non-equilibrium dynamics and braiding of quasiparticles in quantum Hall states.
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Increasing the Representation Accuracy of Quantum Simulations of Chemistry without Extra Quantum Resources
Tyler Takeshita
Eunseok Lee
Physical Review X, vol. 10 (2020), pp. 011004
Preview abstract
Proposals for near-term experiments in quantum chemistry on quantum computers leverage the ability to target a subset of degrees of freedom containing the essential quantum behavior, sometimes called the active space. This approximation allows one to treat more difficult problems using fewer qubits and lower gate depths than would otherwise be possible. However, while this approximation captures many important qualitative features, it may leave the results wanting in terms of absolute accuracy (basis error) of the representation. In traditional approaches, increasing this accuracy requires increasing the number of qubits and an appropriate increase in circuit depth as well. Here we introduce a technique requiring no additional qubits or circuit depth that is able to remove much of this approximation in favor of additional measurements. The technique is constructed and analyzed theoretically, and some numerical proof of concept calculations are shown. As an example, we show how to achieve the accuracy of a 20 qubit representation using only 4 qubits and a modest number of additional measurements for a simple hydrogen molecule. We close with an outlook on the impact this technique may have on both near-term and fault-tolerant quantum simulations.
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Hartree-Fock on a Superconducting Qubit Quantum Computer
Frank Carlton Arute
Kunal Arya
Rami Barends
Michael Blythe Broughton
Bob Benjamin Buckley
Nicholas Bushnell
Yu Chen
Jimmy Chen
Benjamin Chiaro
Roberto Collins
William Courtney
Andrew Dunsworth
Edward Farhi
Brooks Riley Foxen
Rob Graff
Steve Habegger
Alan Ho
Trent Huang
William J. Huggins
Sergei Isakov
Cody Jones
Kostyantyn Kechedzhi
Alexander Korotkov
Fedor Kostritsa
Dave Landhuis
Pavel Laptev
Mike Lindmark
Orion Martin
John Martinis
Anthony Megrant
Xiao Mi
Masoud Mohseni
Wojtek Mruczkiewicz
Josh Mutus
Charles Neill
Thomas E O'Brien
Eric Ostby
Andre Gregory Petukhov
Harry Putterman
Vadim Smelyanskiy
Doug Strain
Kevin Jeffery Sung
Marco Szalay
Tyler Y. Takeshita
Amit Vainsencher
Nathan Wiebe
Jamie Yao
Ping Yeh
Science, vol. 369 (2020), pp. 6507
Preview abstract
As the search continues for useful applications of noisy intermediate scale quantum devices, variational simulations of fermionic systems remain one of the most promising directions. Here, we perform a series of quantum simulations of chemistry which involve twice the number of qubits and more than ten times the number of gates as the largest prior experiments. We model the binding energy of ${\rm H}_6$, ${\rm H}_8$, ${\rm H}_{10}$ and ${\rm H}_{12}$ chains as well as the isomerization of diazene. We also demonstrate error-mitigation strategies based on $N$-representability which dramatically improve the effective fidelity of our experiments. Our parameterized ansatz circuits realize the Givens rotation approach to free fermion evolution, which we variationally optimize to prepare the Hartree-Fock wavefunction. This ubiquitous algorithmic primitive corresponds to a rotation of the orbital basis and is required by many proposals for correlated simulations of molecules and Hubbard models. Because free fermion evolutions are classically tractable to simulate, yet still generate highly entangled states over the computational basis, we use these experiments to benchmark the performance of our hardware while establishing a foundation for scaling up more complex correlated quantum simulations of chemistry.
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Using Models to Improve Optimizers for Variational Quantum Algorithms
Kevin Jeffery Sung
Jiahao Yao
Lin Lin
Quantum Science and Technology, vol. 5 (2020), pp. 044008
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Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers. These algorithms depend on a classical optimization outer-loop that minimizes some function of a parameterized quantum circuit. In practice, finite sampling error and gate errors make this a stochastic optimization with unique challenges that must be addressed at the level of the optimizer. The sharp trade-off between precision and sampling time in conjunction with experimental constraints necessitates the development of new optimization strategies to minimize overall wall clock time in this setting. We introduce an optimization method and numerically compare its performance with common methods in use today. The method is a simple surrogate model-based algorithm designed to improve reuse of collected data. It does so by estimating the gradient using a least-squares quadratic fit of sampled function values within a moving trusted region. To make fair comparisons between optimization methods, we develop experimentally relevant cost models designed to balance efficiency in testing and accuracy with respect to cloud quantum computing systems. The results here underscore the need to both use relevant cost models and optimize hyperparameters of existing optimization methods for competitive performance. We compare tuned methods using cost models presented by superconducting devices accessed through cloud computing platforms. The method introduced here has several practical advantages in realistic experimental settings, and has been used successfully in a separately published experiment on Google's Sycamore device.
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We introduce a fermion-to-qubit mapping using ternary trees. The mapping has a simple structure where any single Majorana operator on an n-mode fermionic system is mapped to a multi-qubit Pauli operator acting nontrivially on log_3 (2n+1) qubits. We prove that the ternary-tree mapping is optimal in the sense that it is impossible to construct a Pauli operator in any fermion-to-qubit mapping which acts nontrivially on less than log_3 (2n+1) qubits. We apply this mapping to the problem of learning k-fermion reduced density matrix (RDM); a problem relevant in various quantum simulation applications. We show that using this mapping one can determine the elements of all k-fermion RDMs, to precision ε, by repeating a single quantum circuit for ~ (2n+1) k / ε^2 times. This result is based on a method we develop here that allows one to determine the elements of all k-qubit RDMs, to precision ε, by repeating a single quantum circuit for ~ 3k /ε^2 times, independent of the system size. This method improves over existing ones for determining qubit RDMs.
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Accurately computing electronic properties of materials using eigenenergies
Alan Derk
Alan Ho
Alex Opremcak
Alexander Korotkov
Andre Gregory Petukhov
Andrew Dunsworth
Anthony Megrant
Bálint Pató
Benjamin Chiaro
Bob Benjamin Buckley
Brooks Riley Foxen
Charles Neill
Cody Jones
Daniel Eppens
Dave Landhuis
Doug Strain
Edward Farhi
Eric Ostby
Fedor Kostritsa
Frank Carlton Arute
Igor Aleiner
Jamie Yao
Jeremy Patterson Hilton
Jimmy Chen
Josh Mutus
Juan Atalaya
Juan Campero
Kostyantyn Kechedzhi
Kunal Arya
Marco Szalay
Masoud Mohseni
Matt Jacob-Mitos
Matt Trevithick
Michael Blythe Broughton
Michael Newman
Nicholas Bushnell
Nicholas Redd
Orion Martin
Pavel Laptev
Ping Yeh
Rami Barends
Roberto Collins
Sean Harrington
Sergei Isakov
Thomas E O'Brien
Trent Huang
Trevor Mccourt
Vadim Smelyanskiy
Vladimir Shvarts
William Courtney
William J. Huggins
Wojtek Mruczkiewicz
Xiao Mi
Yu Chen
arXiv preprint arXiv:2012.00921 (2020)
Preview abstract
A promising approach to study quantum materials is to simulate them on an engineered quantum platform. However, achieving the accuracy needed to outperform classical methods has been an outstanding challenge. Here, using superconducting qubits, we provide an experimental blueprint for a programmable and accurate quantum matter simulator and demonstrate how to probe fundamental electronic properties. We illustrate the underlying method by reconstructing the single-particle band-structure of a one-dimensional wire. We demonstrate nearly complete mitigation of decoherence and readout errors and arrive at an accuracy in measuring energy eigenvalues of this wire with an error of ~0.01 radians, whereas typical energy scales are of order 1 radian. Insight into this unprecedented algorithm fidelity is gained by highlighting robust properties of a Fourier transform, including the ability to resolve eigenenergies with a statistical uncertainty of 1e-4 radians. Furthermore, we synthesize magnetic flux and disordered local potentials, two key tenets of a condensed-matter system. When sweeping the magnetic flux, we observe avoided level crossings in the spectrum, a detailed fingerprint of the spatial distribution of local disorder. Combining these methods, we reconstruct electronic properties of the eigenstates where we observe persistent currents and a strong suppression of conductance with added disorder. Our work describes an accurate method for quantum simulation and paves the way to study novel quantum materials with superconducting qubits.
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Learning to learn with quantum neural networks via classical neural networks
Guillaume Verdon
Michael Broughton
Kevin Jeffery Sung
Masoud Mohseni
arXiv:1907.05415 (2019)
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Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges. One such challenge is finding good parameter initialization heuristics that ensure rapid and consistent convergence to local minima of the parameterized quantum circuit landscape. In this work, we train classical neural networks to assist in the quantum learning process, also know as meta-learning, to rapidly find approximate optima in the parameter landscape for several classes of quantum variational algorithms. Specifically, we train classical recurrent neural networks to find approximately optimal parameters within a small number of queries of the cost function for the Quantum Approximate Optimization Algorithm (QAOA) for MaxCut, QAOA for Sherrington-Kirkpatrick Ising model, and for a Variational Quantum Eigensolver for the Hubbard model. By initializing other optimizers at parameter values suggested by the classical neural network, we demonstrate a significant improvement in the total number of optimization iterations required to reach a given accuracy. We further demonstrate that the optimization strategies learned by the neural network generalize well across a range of problem instance sizes. This opens up the possibility of training on small, classically simulatable problem instances, in order to initialize larger, classically intractably simulatable problem instances on quantum devices, thereby significantly reducing the number of required quantum-classical optimization iterations.
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Majorana Loop Stabilizer Codes for Error Mitigation in Fermionic Quantum Simulations
Physical Review Applied, vol. 12 (2019), pp. 064041
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Fermion-to-qubit mappings that preserve geometric locality are especially useful for simulating lattice fermion models (e.g., the Hubbard model) on a quantum computer. They avoid the overhead associated with geometric nonlocal parity terms in mappings such as the Jordan-Wigner transformation and the Bravyi-Kitaev transformation. As a result, they often provide quantum circuits with lower depth and gate complexity. In such encodings, fermionic states are encoded in the common +1 eigenspace of a set of stabilizers, akin to stabilizer quantum error-correcting codes. Here, we discuss several known geometric locality-preserving mappings and their abilities to correct and detect single-qubit errors. We introduce a geometric locality-preserving map, whose stabilizers correspond to products of Majorana operators on closed paths of the fermionic hopping graph. We show that our code, which we refer to as the Majorana loop stabilizer code (MLSC) can correct all single-qubit errors on a two-dimensional square lattice, while previous geometric locality-preserving codes can only detect single-qubit errors on the same lattice. Compared to existing codes, the MLSC maps the relevant fermionic operators to lower-weight qubit operators despite having higher code distance. Going beyond lattice models, we demonstrate that the MLSC is compatible with state-of-the-art algorithms for simulating quantum chemistry, and can offer those simulations the same error-correction properties without additional asymptotic overhead. These properties make the MLSC a promising candidate for error-mitigated quantum simulations of fermions on near-term devices
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Quantum Supremacy using a Programmable Superconducting Processor
Frank Arute
Kunal Arya
Rami Barends
Rupak Biswas
Fernando Brandao
David Buell
Yu Chen
Jimmy Chen
Ben Chiaro
Roberto Collins
William Courtney
Andrew Dunsworth
Edward Farhi
Brooks Foxen
Austin Fowler
Rob Graff
Keith Guerin
Steve Habegger
Michael Hartmann
Alan Ho
Trent Huang
Travis Humble
Sergei Isakov
Kostyantyn Kechedzhi
Sergey Knysh
Alexander Korotkov
Fedor Kostritsa
Dave Landhuis
Mike Lindmark
Dmitry Lyakh
Salvatore Mandrà
Anthony Megrant
Xiao Mi
Kristel Michielsen
Masoud Mohseni
Josh Mutus
Charles Neill
Eric Ostby
Andre Petukhov
Eleanor G. Rieffel
Vadim Smelyanskiy
Kevin Jeffery Sung
Matt Trevithick
Amit Vainsencher
Benjamin Villalonga
Z. Jamie Yao
Ping Yeh
John Martinis
Nature, vol. 574 (2019), 505–510
Preview abstract
The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 2^53 (about 10^16). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times-our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy for this specific computational task, heralding a much-anticipated computing paradigm.
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Characterizing Quantum Supremacy in Near-Term Devices
Sergei Isakov
Vadim Smelyanskiy
Michael J. Bremner
John Martinis
Nature Physics, vol. 14 (2018), 595–600
Preview abstract
A critical question for quantum computing in the near future is whether quantum devices without error correction can perform a well-defined computational task beyond the capabilities of supercomputers. Such a demonstration of what is referred to as quantum supremacy requires a reliable evaluation of the resources required to solve tasks with classical approaches. Here, we propose the task of sampling from the output distribution of random quantum circuits as a demonstration of quantum supremacy. We extend previous results in computational complexity to argue that this sampling task must take exponential time in a classical computer. We introduce cross-entropy benchmarking to obtain the experimental fidelity of complex multiqubit dynamics. This can be estimated and extrapolated to give a success metric for a quantum supremacy demonstration. We study the computational cost of relevant classical algorithms and conclude that quantum supremacy can be achieved with circuits in a two-dimensional lattice of 7 × 7 qubits and around 40 clock cycles. This requires an error rate of around 0.5% for two-qubit gates (0.05% for one-qubit gates), and it would demonstrate the basic building blocks for a fault-tolerant quantum computer
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Postponing the Orthogonality Catastrophe: Efficient State Preparation for Electronic Structure Simulations on Quantum Devices
Norman Tubman
Carlos Mejuto Zaera
Jeffrey Epstein
Diptarka Hait
Daniel Levine
William Huggins
Martin Head-Gordon
K. Birgitta Whaley
arXiv:1809.05523 (2018)
Preview abstract
Despite significant work on resource estimation for quantum simulation of electronic systems, the challenge of preparing states with sufficient ground state support has so far been largely neglected. In this work we investigate this issue in several systems of interest, including organic molecules, transition metal complexes, the uniform electron gas, Hubbard models, and quantum impurity models arising from embedding formalisms such as dynamical mean-field theory. Our approach uses a state-of-the-art classical technique for high-fidelity ground state approximation. We find that easy-to-prepare single Slater determinants such as the Hartree-Fock state often have surprisingly robust support on the ground state for many applications of interest. For the most difficult systems, single-determinant reference states may be insufficient, but low-complexity reference states may suffice. For this we introduce a method for preparation of multi-determinant states on quantum computers.
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Understanding Quantum Tunneling through Quantum Monte Carlo Simulations
Sergei Isakov
Guglielmo Mazzola
Vadim Smelyanskiy
Matthias Troyer
PRL (2016)
Preview abstract
The tunneling between the two ground states of an Ising ferromagnet is a typical example of many-body tunneling processes between two local minima, as they occur during quantum annealing. Performing quantum Monte Carlo (QMC) simulations we find that the QMC tunneling rate displays the same scaling with system size, as the rate of incoherent tunneling. The scaling in both cases is O(Δ2), where Δ is the tunneling splitting. An important consequence is that QMC simulations can be used to predict the performance of a quantum annealer for tunneling through a barrier. Furthermore, by using open instead of periodic boundary conditions in imaginary time, equivalent to a projector QMC algorithm, we obtain a quadratic speedup for QMC, and achieve linear scaling in Δ. We provide a physical understanding of these results and their range of applicability based on an instanton picture.
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