Matthew P Harrigan
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Expressing and Analyzing Quantum Algorithms with Qualtran
Charles Yuan
Anurudh Peduri
arXiv::2409.04643 (2024)
Preview abstract
Quantum computing's transition from theory to reality has spurred the need for novel software tools to manage the increasing complexity, sophistication, toil, and chance for error of quantum algorithm development. We present Qualtran, an open-source library for representing and analyzing quantum algorithms. Using carefully chosen abstractions and data structures, we can simulate and test algorithms, automatically generate information-rich diagrams, and tabulate resource requirements. Qualtran offers a \emph{standard library} of algorithmic building blocks that are essential for modern cost-minimizing compilations. Its capabilities are showcased through the re-analysis of key algorithms in Hamiltonian simulation, chemistry, and cryptography. The resulting architecture-independent resource counts can be forwarded to our implementation of cost models to estimate physical costs like wall-clock time and number of physical qubits assuming a surface-code architecture. Qualtran provides a foundation for explicit constructions and reproducible analysis, fostering greater collaboration within the quantum algorithm development community. We believe tools like Qualtran will accelerate progress in the field.
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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
Markus Hoffmann
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, 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
Markus Hoffmann
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, 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
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
Markus Rudolf Hoffmann
Trent Huang
Ashley Anne Huff
Sergei Isakov
Justin Thomas Iveland
Cody Jones
Pavol Juhas
Marika Kieferova
Andrey Klots
Alexander Korotkov
Fedor Kostritsa
John Mark Kreikebaum
Dave Landhuis
Pavel Laptev
Kim Ming Lau
Lily MeeKit Laws
Joonho Lee
Kenny Lee
Alexander T. Lill
Wayne Liu
Orion Martin
Trevor Johnathan Mccourt
Anthony Megrant
Xiao Mi
Masoud Mohseni
Shirin Montazeri
Alexis Morvan
Ramis Movassagh
Wojtek Mruczkiewicz
Charles Neill
Ani Nersisyan
Michael Newman
Jiun How Ng
Murray Nguyen
Alex Opremcak
Andre Gregory Petukhov
Rebecca Potter
Kannan Aryaperumal Sankaragomathi
Christopher Schuster
Mike Shearn
Aaron Shorter
Vladimir Shvarts
Jindra Skruzny
Vadim Smelyanskiy
Clarke Smith
Rolando Diego Somma
Doug Strain
Marco Szalay
Alfredo Torres
Guifre Vidal
Jamie Yao
Ping Yeh
Juhwan Yoo
Grayson Robert Young
Yaxing Zhang
Ningfeng Zhu
Christian Gogolin
Nature Physics (2023)
Preview abstract
An important measure of the development of quantum computing platforms has been the simulation of increasingly complex physical systems. Prior to fault-tolerant quantum computing, robust error mitigation strategies are necessary to continue this growth. Here, we study physical simulation within the seniority-zero electron pairing subspace, which affords both a computational stepping stone to a fully correlated model, and an opportunity to validate recently introduced ``purification-based'' error-mitigation strategies. We compare the performance of error mitigation based on doubling quantum resources in time (echo verification) or in space (virtual distillation), on up to 20 qubits of a superconducting qubit quantum processor. We observe a reduction of error by one to two orders of magnitude below less sophisticated techniques (e.g. post-selection); the gain from error mitigation is seen to increase with the system size. Employing these error mitigation strategies enables the implementation of the largest variational algorithm for a correlated chemistry system to-date. Extrapolating performance from these results allows us to estimate minimum requirements for a beyond-classical simulation of electronic structure. We find that, despite the impressive gains from purification-based error mitigation, significant hardware improvements will be required for classically intractable variational chemistry simulations.
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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
Markus Hoffmann
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, 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
Markus Rudolf Hoffmann
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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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, 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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What the foundations of quantum computer science teach us about chemistry
Joonho Lee
Thomas E O'Brien
William J. Huggins
Hsin-Yuan Huang
Journal of Chemical Physics, 155 (2021), pp. 150901
Preview abstract
With the rapid development of quantum technology, one of the leading applications that has been identified is the simulation of chemistry. Interestingly, even before full scale quantum computers are available, quantum computer science has exhibited a remarkable string of results that directly impact what is possible in chemical simulation, even with a quantum computer. Some of these results even impact our understanding of chemistry in the real world. In this perspective, we take the position that direct chemical simulation is best understood as a digital experiment. While on one hand this clarifies the power of quantum computers to extend our reach, it also shows us the limitations of taking such an approach too directly. Leveraging results that quantum computers cannot outpace the physical world, we build to the controversial stance that some chemical problems are best viewed as problems for which no algorithm can deliver their solution in general, known in computer science as undecidable problems. This has implications for the predictive power of thermodynamic models and topics like the ergodic hypothesis. However, we argue that this perspective is not defeatist, but rather helps shed light on the success of existing chemical models like transition state theory, molecular orbital theory, and thermodynamics as models that benefit from data. We contextualize recent results showing that data-augmented models are more powerful rote simulation. These results help us appreciate the success of traditional chemical theory and anticipate new models learned from experimental data. Not only can quantum computers provide data for such models, but they can extend the class and power of models that utilize data in fundamental ways. These discussions culminate in speculation on new ways for quantum computing and chemistry to interact and our perspective on the eventual roles of quantum computers in the future of chemistry.
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Low-Depth Mechanisms for Quantum Optimization
Masoud Mohseni
Vadim Smelyanskiy
PRX Quantum, 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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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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