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1 - 15 of 126 publications
Analyzing Prospects for Quantum Advantage in Topological Data Analysis
Dominic W. Berry
Yuan Su
Casper Gyurik
Robbie King
Joao Basso
Abhishek Rajput
Nathan Wiebe
Vedran Djunko
PRX Quantum, vol. 5 (2024), pp. 010319
Preview abstract
Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers in persistent homology (a way of characterizing topological features of data sets). Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples for which super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve some seemingly classically intractable instances.
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Drug Design on Quantum Computers
Raffaele Santagati
Alán Aspuru-Guzik
Matthias Degroote
Leticia Gonzalez
Elica Kyoseva
Nikolaj Moll
Markus Oppel
Robert Parrish
Michael Streif
Christofer Tautermann
Horst Weiss
Nathan Wiebe
Clemens Utschig-Utschig
Nature Physics (2024)
Preview abstract
The promised industrial applications of quantum computers often rest on their anticipated ability to perform accurate, efficient quantum chemical calculations. Computational drug discovery relies on accurate predictions of how candidate drugs interact with their targets in a cellular environment involving several thousands of atoms at finite temperatures. Although quantum computers are still far from being used as daily tools in the pharmaceutical industry, here we explore the challenges and opportunities of applying quantum computers to drug design. We discuss where these could transform industrial research and identify the substantial further developments needed to reach this goal.
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Model-based Optimization of Superconducting Qubit Readout
Alex Opremcak
Alexandre Bourassa
Alexander Korotkov
Jimmy Chen
Physical Review Letters, vol. 132 (2024), pp. 100603
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Measurement is one of the essential components of quantum algorithms, and for superconducting qubits it is often the most error prone. Here, we demonstrate a model-based readout optimization achieving low measurement errors while avoiding detrimental side-effects. For simultaneous and mid-circuit measurements across 17 qubits we observe 1.5% error per qubit with a duration of 500 ns end-to-end and minimal excess reset error from residual resonator photons. We also suppress measurement-induced state transitions and achieve a qubit leakage rate limited by natural heating.This technique can scale to hundreds of qubits, and be used to enhance performance of error-correcting codes as well as near-term applications
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Optimizing quantum gates towards the scale of logical qubits
Alexandre Bourassa
Andrew Dunsworth
Will Livingston
Vlad Sivak
Trond Andersen
Yaxing Zhang
Desmond Chik
Jimmy Chen
Charles Neill
Alejo Grajales Dau
Anthony Megrant
Alexander Korotkov
Vadim Smelyanskiy
Yu Chen
Nature Communications, vol. 15 (2024), pp. 2442
Preview abstract
A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major challenges that could become fundamental roadblocks are manufacturing high-performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dynamic control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by ~3.7× compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to a variety of quantum operations, algorithms, and computing architectures.
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Quantum Error Mitigation
Zhenyu Cai
Simon Benjamin
Suguru Endo
William J. Huggins
Ying Li
Thomas E O'Brien
Reviews of Modern Physics, vol. 95 (2023), pp. 045005
Preview abstract
For quantum computers to successfully solve real-world problems, it is necessary to tackle the challenge of noise: the errors that occur in elementary physical components due to unwanted or imperfect interactions. The theory of quantum fault tolerance can provide an answer in the long term, but in the coming era of noisy intermediate-scale quantum machines one must seek to mitigate errors rather than completely eliminate them. This review surveys the diverse methods that have been proposed for quantum error mitigation, assesses their in-principle efficacy, and describes the hardware demonstrations achieved to date. Commonalities and limitations among the methods are identified, while mention is made of how mitigation methods can be chosen according to the primary type of noise present, including algorithmic errors. Open problems in the field are identified, and the prospects for realizing mitigation-based devices that can deliver a quantum advantage with an impact on science and business are discussed.
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"Classical shadows" are estimators of an unknown quantum state, constructed from suitably distributed random measurements on copies of that state [Nature Physics 16, 1050-1057]. Here, we analyze classical shadows obtained using random matchgate circuits, which correspond to fermionic Gaussian unitaries. We prove that the first three moments of the Haar distribution over the continuous group of matchgate circuits are equal to those of the discrete uniform distribution over only the matchgate circuits that are also Clifford unitaries; thus, the latter forms a "matchgate 3-design." This implies that the classical shadows resulting from the two ensembles are functionally equivalent. We show how one can use these matchgate shadows to efficiently estimate inner products between an arbitrary quantum state and fermionic Gaussian states, as well as the expectation values of local fermionic operators and various other quantities, thus surpassing the capabilities of prior work. As a concrete application, this enables us to apply wavefunction constraints that control the fermion sign problem in the quantum-classical auxiliary-field quantum Monte Carlo algorithm (QC-AFQMC) [Nature 603, 416-420], without the exponential post-processing cost incurred by the original approach.
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Preview abstract
Quadratic programming over the (special) orthogonal group encompasses a broad class of optimization problems such as group synchronization, point-set registration, and simultaneous localization and mapping. Such problems are instances of the little noncommutative Grothendieck problem (LNCG), a natural generalization of quadratic combinatorial optimization where, instead of binary decision variables, one optimizes over orthogonal matrices. In this work, we establish an embedding of this class of LNCG problems over the orthogonal group onto a quantum Hamiltonian. This embedding is accomplished by identifying orthogonal matrices with their double cover (Pin and Spin group) elements, which we represent as quantum states. We connect this construction to the theory of free fermions, which provides a physical interpretation of the derived LNCG Hamiltonian as a two-body interacting-fermion model due to the quadratic nature of the problem. Determining extremal states of this Hamiltonian provides an outer approximation to the original problem, analogous to classical relaxations of the problem via semidefinite programming. Furthermore, we show that when considering optimization over the special orthogonal group, our quantum relaxation naturally obeys additional, powerful constraints based on the convex hull of rotation matrices. The classical size of this convex-hull representation is exponential in matrix dimension, whereas the quantum representation requires only a linear number of qubits. Finally, to project the relaxed solution into the feasible space, we employ rounding procedures which return orthogonal matrices from appropriate measurements of the quantum state. Through numerical experiments we provide evidence that this quantum relaxation can produce high-quality approximations.
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Josephson parametric amplifier with Chebyshev gain profile and high saturation
Ryan Kaufman
Mark Dykman
Andrea Iorio
George Sterling
Alex Opremcak
Lara Faoro
Tim Burger
Robert Gasca
Physical Review Applied, vol. 20 (2023), pp. 054058
Preview abstract
We demonstrate a Josephson parametric amplifier design with a band-pass impedance matching network based on a third-order Chebyshev prototype. We measured eight amplifiers operating at 4.6~GHz that exhibit gains of 20~dB with less than 1~dB gain ripple and up to 500~MHz bandwidth. The amplifiers further achieve high input saturation powers around $-93$~dBm based on the use of rf-SQUID arrays as their nonlinear element. We characterize the amplifiers' readout efficiency and their signal-to-noise ratio near saturation using a Sycamore processor. In addition, we measure the amplifiers intermodulation distortion in two-tone experiments as a function of input power and inter-tone detuning, and observe excess distortion at small detuning with a pronounced dip as a function of signal power, which we interpret in terms of power-dependent dielectric losses.
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Preview abstract
We demonstrate a 3-port Josephson parametric circulator, matched to 50 Ohm using second order Chebyshev networks. The device notably operates with two of its signal ports at the same frequency and uses only two out-of-phase pumps at a single frequency. As a consequence, When operated as an isolator it does not require phase coherence between the pumps and the signal, simplifying the requirements for its integration into standard dispersive qubit readout setups. The device utilizes parametric couplers based on a balanced bridge of rf-SQUID arrays, which offer purely parametric coupling and high dynamic range. We characterize the device by measuring its full 3x3 S-matrix as a function of frequency the relative phase between the two pumps. We find up to 15 dB nonreciprocity over a 200 MHz signal band, port match better than 10 dB, low insertion loss of 0.6 dB, and saturation power exceeding -80 dBm.
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Exponential Quantum Speedup in Simulating Coupled Classical Oscillators
Dominic Berry
Rolando Somma
Nathan Wiebe
Physical Review X, vol. 13 (2023), pp. 041041
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We present a quantum algorithm for simulating the classical dynamics of 2^n coupled oscillators (e.g., 2^n masses coupled by springs). Our approach leverages a mapping between the Schrodinger equation and Newton's equations for harmonic potentials such that the amplitudes of the evolved quantum state encode the momenta and displacements of the classical oscillators. When individual masses and spring constants can be efficiently queried, and when the initial state can be efficiently prepared, the complexity of our quantum algorithm is polynomial in n, almost linear in the evolution time, and sublinear in the sparsity. As an example application, we apply our quantum algorithm to efficiently estimate the kinetic energy of an oscillator at any time, for a specification of the problem that we prove is \BQP-complete. Thus, our approach solves a potentially practical application with an exponential speedup over classical computers. Finally, we show that under similar conditions our approach can efficiently simulate more general classical harmonic systems with 2^n modes.
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Evaluating the Evidence for Exponential Quantum Advantage in Ground-State Quantum Chemistry
Seunghoon Lee
Joonho Lee
Huanchen Zhai
Yu Tong
Alexander Dalzell
Ashutosh Kumar
Phillip Helms
Johnnie Gray
Zhi-Hao Cui
Michael Kastoryano
John Preskill
David Reichman
Earl Campbell
Edward Valeev
Lin Lin
Garnet Chan
Nature Communications, vol. 14 (2023)
Preview abstract
Due to intense interest in the potential applications of quantum computing, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in quantum chemistry, namely, ground-state energy estimation, for generic chemical problems where heuristic quantum state preparation might be assumed to be efficient. The availability of exponential quantum advantage then centers on whether features of the physical problem that enable efficient heuristic quantum state preparation also enable efficient solution by classical heuristics. Through numerical studies of quantum state preparation and empirical complexity analysis (including the error scaling) of classical heuristics, in both ab initio and model Hamiltonian settings, we conclude that evidence for such an exponential advantage across chemical space has yet to be found. While quantum computers may still prove useful for ground-state quantum chemistry through polynomial speedups, it may be prudent to assume exponential speedups are not generically available for this problem.
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Readout of a quantum processor with high dynamic range Josephson parametric amplifiers
Aaron Shorter
Alejandro Grajales Dau
Alex Crook
Alex Opremcak
Alexander Korotkov
Alexander Lill
Alexandre Bourassa
Alexis Morvan
Alfredo Torres
Andrew Dunsworth
Ani Nersisyan
Anthony Megrant
Ashley Anne Huff
Ben Curtin
Benjamin Chiaro
Bob Benjamin Buckley
Brooks Riley Foxen
Charles Neill
Christopher Schuster
Dave Landhuis
Ebrahim Forati
Fedor Kostritsa
Frank Carlton Arute
Grayson Robert Young
Jamie Yao
Jeremy Patterson Hilton
Jimmy Chen
JiunHow Ng
John Mark Kreikebaum
Josh Godfrey Cogan
Juhwan Yoo
Justin Thomas Iveland
Kannan Aryaperumal Sankaragomathi
Kenny Lee
Kunal Arya
Leon Brill
Lily MeeKit Laws
Marco Szalay
Marika Kieferova
Markus Ansmann
Michael C. Hamilton
Mike Shearn
Murray Nguyen
Nicholas Bushnell
Ningfeng Zhu
Pavel Laptev
Ping Yeh
Rajeev Acharya
Rebecca Potter
Reza Fatemi
Roberto Collins
Sean Harrington
Shirin Montazeri
Tim Burger
Trent Huang
Trevor Johnathan Mccourt
Vladimir Shvarts
Wayne Liu
William Giang
Xiao Mi
Yu Chen
Applied Physics Letters, vol. 122 (2023), pp. 014001
Preview abstract
We demonstrate a high dynamic range Josephson parametric amplifier (JPA) in which the active nonlinear element is implemented using an array of rf-SQUIDs. The device is matched to the 50 $\Omega$ environment with a Klopfenstein-taper impedance transformer and achieves a bandwidth of 250-300 MHz, with input saturation powers up to $-95$~dBm at 20 dB gain. A 54-qubit Sycamore processor was used to benchmark these devices, providing a calibration for readout power, an estimate of amplifier added noise, and a platform for comparison against standard impedance matched parametric amplifiers with a single dc-SQUID. We find that the high power rf-SQUID array design has no adverse effect on system noise, readout fidelity, and qubit dephasing, and we estimate an upper bound on amplifier added noise at 1.6 times the quantum limit. Lastly, amplifiers with this design show no degradation in readout fidelity due to gain compression, which can occur in multi-tone multiplexed readout with traditional JPAs.
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Suppressing quantum errors by scaling a surface code logical qubit
Anthony Megrant
Cody Jones
Jeremy Hilton
Jimmy Chen
Juan Atalaya
Kenny Lee
Michael Newman
Vadim Smelyanskiy
Yu Chen
Nature (2023)
Preview abstract
Practical quantum computing will require error rates that are well below what is achievable with
physical qubits. Quantum error correction [1, 2] offers a path to algorithmically-relevant error rates
by encoding logical qubits within many physical qubits, where increasing the number of physical
qubits enhances protection against physical errors. However, introducing more qubits also increases
the number of error sources, so the density of errors must be sufficiently low in order for logical
performance to improve with increasing code size. Here, we report the measurement of logical qubit
performance scaling across multiple code sizes, and demonstrate that our system of superconducting
qubits has sufficient performance to overcome the additional errors from increasing qubit number.
We find our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3
logical qubits on average, both in terms of logical error probability over 25 cycles and logical error
per cycle (2.914%±0.016% compared to 3.028%±0.023%). To investigate damaging, low-probability
error sources, we run a distance-25 repetition code and observe a 1.7 × 10−6 logical error per round
floor set by a single high-energy event (1.6 × 10−7 when excluding this event). We are able to
accurately model our experiment, and from this model we can extract error budgets that highlight
the biggest challenges for future systems. These results mark the first experimental demonstration
where quantum error correction begins to improve performance with increasing qubit number, and
illuminate the path to reaching the logical error rates required for computation.
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Purification-Based Quantum Error Mitigation of Pair-Correlated Electron Simulations
Thomas E O'Brien
Gian-Luca R. Anselmetti
Fotios Gkritsis
Vincent Elfving
Stefano Polla
William J. Huggins
Oumarou Oumarou
Kostyantyn Kechedzhi
Dmitry Abanin
Rajeev Acharya
Igor Aleiner
Richard Ross Allen
Trond Ikdahl Andersen
Kyle Anderson
Markus Ansmann
Frank Carlton Arute
Kunal Arya
Juan Atalaya
Michael Blythe Broughton
Bob Benjamin Buckley
Alexandre Bourassa
Leon Brill
Tim Burger
Nicholas Bushnell
Jimmy Chen
Yu Chen
Benjamin Chiaro
Desmond Chun Fung Chik
Josh Godfrey Cogan
Roberto Collins
Paul Conner
William Courtney
Alex Crook
Ben Curtin
Ilya Drozdov
Andrew Dunsworth
Daniel Eppens
Lara Faoro
Edward Farhi
Reza Fatemi
Ebrahim Forati
Brooks Riley Foxen
William Giang
Dar Gilboa
Alejandro Grajales Dau
Steve Habegger
Michael C. Hamilton
Sean Harrington
Jeremy Patterson Hilton
Trent Huang
Ashley Anne Huff
Sergei Isakov
Justin Thomas Iveland
Cody Jones
Pavol Juhas
Marika Kieferova
Andrey Klots
Alexander Korotkov
Fedor Kostritsa
John Mark Kreikebaum
Dave Landhuis
Pavel Laptev
Kim Ming Lau
Lily MeeKit Laws
Joonho Lee
Kenny Lee
Alexander T. Lill
Wayne Liu
Orion Martin
Trevor Johnathan Mccourt
Anthony Megrant
Xiao Mi
Masoud Mohseni
Shirin Montazeri
Alexis Morvan
Ramis Movassagh
Wojtek Mruczkiewicz
Charles Neill
Ani Nersisyan
Michael Newman
Jiun How Ng
Murray Nguyen
Alex Opremcak
Andre Gregory Petukhov
Rebecca Potter
Kannan Aryaperumal Sankaragomathi
Christopher Schuster
Mike Shearn
Aaron Shorter
Vladimir Shvarts
Jindra Skruzny
Vadim Smelyanskiy
Clarke Smith
Rolando Diego Somma
Doug Strain
Marco Szalay
Alfredo Torres
Guifre Vidal
Jamie Yao
Ping Yeh
Juhwan Yoo
Grayson Robert Young
Yaxing Zhang
Ningfeng Zhu
Christian Gogolin
Nature Physics (2023)
Preview abstract
An important measure of the development of quantum computing platforms has been the simulation of increasingly complex physical systems. Prior to fault-tolerant quantum computing, robust error mitigation strategies are necessary to continue this growth. Here, we study physical simulation within the seniority-zero electron pairing subspace, which affords both a computational stepping stone to a fully correlated model, and an opportunity to validate recently introduced ``purification-based'' error-mitigation strategies. We compare the performance of error mitigation based on doubling quantum resources in time (echo verification) or in space (virtual distillation), on up to 20 qubits of a superconducting qubit quantum processor. We observe a reduction of error by one to two orders of magnitude below less sophisticated techniques (e.g. post-selection); the gain from error mitigation is seen to increase with the system size. Employing these error mitigation strategies enables the implementation of the largest variational algorithm for a correlated chemistry system to-date. Extrapolating performance from these results allows us to estimate minimum requirements for a beyond-classical simulation of electronic structure. We find that, despite the impressive gains from purification-based error mitigation, significant hardware improvements will be required for classically intractable variational chemistry simulations.
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Quantum Simulation of Exact Electron Dynamics can be more Efficient than Classical Mean-Field Methods
William J. Huggins
Dominic W. Berry
Shu Fay Ung
Andrew Zhao
David Reichman
Andrew Baczewski
Joonho Lee
Nature Communications, vol. 14 (2023), pp. 4058
Preview abstract
Quantum algorithms for simulating electronic ground states are slower than popular classical mean-field algorithms such as Hartree-Fock and density functional theory, but offer higher accuracy. Accordingly, quantum computers have been predominantly regarded as competitors to only the most accurate and costly classical methods for treating electron correlation. However, here we tighten bounds showing that certain first quantized quantum algorithms enable exact time evolution of electronic systems with exponentially less space and polynomially fewer operations in basis set size than conventional real-time time-dependent Hartree-Fock and density functional theory. Although the need to sample observables in the quantum algorithm reduces the speedup, we show that one can estimate all elements of the k-particle reduced density matrix with a number of samples scaling only polylogarithmically in basis set size. We also introduce a more efficient quantum algorithm for first quantized mean-field state preparation that is likely cheaper than the cost of time evolution. We conclude that quantum speedup is most pronounced for finite temperature simulations and suggest several practically important electron dynamics problems with potential quantum advantage.
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