Lev Ioffe
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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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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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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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Quantum Computation of Molecular Structure using Data from Challenging-to-Classically-Simulate Nuclear Magnetic Resonance Experiments
Thomas E O'Brien
Yuan Su
David Fushman
Vadim Smelyanskiy
PRX Quantum, 3 (2022)
Preview abstract
We propose a quantum algorithm for inferring the molecular nuclear spin Hamiltonian from time-resolved measurements of spin-spin correlators, which can be obtained via nuclear magnetic resonance (NMR). We focus on learning the anisotropic dipolar term of the Hamiltonian, which generates dynamics that are challenging to classically simulate in some contexts. We demonstrate the ability to directly estimate the Jacobian and Hessian of the corresponding learning problem on a quantum computer, allowing us to learn the Hamiltonian parameters. We develop algorithms for performing this computation on both noisy near-term and future fault-tolerant quantum computers. We argue that the former is promising as an early beyond-classical quantum application since it only requires evolution of a local spin Hamiltonian. We investigate the example of a protein (ubiquitin) confined on a membrane as a benchmark of our method. We isolate small spin clusters, demonstrate the convergence of our learning algorithm on one such example, and then investigate the learnability of these clusters as we cross the ergodic to nonergodic phase transition by suppressing the dipolar interaction. We see a clear correspondence between a drop in the multifractal dimension measured across many-body eigenstates of these clusters, and a transition in the structure of the Hessian of the learning cost function (from degenerate to learnable). Our hope is that such quantum computations might enable the interpretation and development of new NMR techniques for analyzing molecular structure.
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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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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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Resolving catastrophic error bursts from cosmic rays in large arrays of superconducting qubits
Lara Faoro
Kunal Arya
Andrew Dunsworth
Trent Huang
Frank Arute
Bob B. Buckley
Nicholas Bushnell
Jimmy Chen
Roberto Collins
Alan R. Derk
Sean Harrington
Fedor Kostritsa
Pavel Laptev
Xiao Mi
Shirin Montazeri
Josh Mutus
Charles Neill
Alex Opremcak
Nicholas Redd
Vladimir Shvarts
Jamie Yao
Ping Yeh
Juhwan Yoo
Yu Chen
Vadim Smelyanskiy
John Martinis
Anthony Megrant
Rami Barends
Nature Physics (2021)
Preview abstract
Scalable quantum computing can become a reality with error correction, provided that coherent qubits can be constructed in large arrays. The key premise is that physical errors can remain both small and sufficiently uncorrelated as devices scale, so that logical error rates can be exponentially suppressed. However, impacts from cosmic rays and latent radioactivity violate these assumptions. An impinging particle can ionize the substrate and induce a burst of quasiparticles that destroys qubit coherence throughout the device. High-energy radiation has been identified as a source of error in pilot superconducting quantum devices, but the effect on large-scale algorithms and error correction remains an open question. Elucidating the physics involved requires operating large numbers of qubits at the same rapid timescales necessary for error correction. Here, we use space- and time-resolved measurements of a large-scale quantum processor to identify bursts of quasiparticles produced by high-energy rays. We track the events from their initial localized impact as they spread, simultaneously and severely limiting the energy coherence of all qubits and causing chip-wide failure. Our results provide direct insights into the impact of these damaging error bursts and highlight the necessity of mitigation to enable quantum computing to scale.
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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, 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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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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