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Machine learning techniques are rapidly being adopted into the field of quantum manybody physics, including condensed matter theory, experiment, and quantum information science. The steady increase in data being produced by highlycontrolled quantum experiments brings the potential of machine learning algorithms to the forefront of scientific advancement. Particularly exciting is the prospect of using machine learning for the discovery and design of quantum materials, devices, and computers. In order to make progress, the field must address a number of fundamental questions related to the challenges of studying manybody quantum mechanics using classical computing algorithms and hardware.
The goal of this conference is to bring together experts in computational physics, machine learning, and quantum information, to make headway on a number of related topics, including:
 Datadrive quantum state reconstruction
 Machine learning strategies for quantum error correction
 Neuralnetwork based wavefunctions
 Nearterm prospects for data from quantum devices
 Machine learning for quantum algorithm discovery
Registration for this event is now closed.
Sponsorship for this event has been provided by:
 Marin Bukov, University of California, Berkeley
 Giuseppe Carleo, Flatiron Institute
 Michele Ceriotti, École polytechnique fédérale de Lausanne
 Paul Ginsparg, Cornell University
 Timothy Hsieh, Perimeter Institute
 Ehsan Khatami, San Jose State University
 EunAh Kim, Cornell University
 Stefan Leichenauer, Google
 Sebastiano Pilati, University of Camerino
 Pooya Ronagh, University of Waterloo
 Maria Schuld, University of KwaZuluNatal
 Kristan Temme, IBM Research
 Evert van Nieuwenburg, California Institute of Technology
 Lei Wang, Chinese Academy of Sciences
 Peter Wittek, University of Toronto
 YiZhuang You, University of California, San Diego
 Andrea Zen, University College London
 Nour Abura'ed, University of Dubai
 Aida Ahmadzadegan, Perimeter Institute & University of Waterloo
 Michael Albergo, Perimeter Institute
 Juan Atalaya, University of California, Berkeley
 Tanisha Bassan, The Knowledge Society
 Matthew Beach, Perimeter Institute
 Aleksandr Berezutskii, Skolkovo Institute of Science and Technology
 Yael Birenbaum, National Research Council Canada
 Kristine Boone, University of Waterloo
 Peter Cha, Cornell University
 Wissam Chemissany, California Institute of Technology
 Jianxin Chen, Alibaba Quantum Laboratory
 Mingshi Chi, University of Toronto
 Ian Convy, University of California, Berkeley
 Luuk Coopmans, Trinity College Dublin & Dublin Institute for Advanced Studies
 Emily Davis, Stanford University
 Isaac De Vlugt, University of Waterloo
 Nicolo Defenu, Heidelberg University
 DongLing Deng, Tsinghua University
 Olivia Di Matteo, TRIUMF
 Nicholas Duchene, Polytechnique Montréal
 Marcus Edwards, University of Waterloo
 Timo Felser, University of Padova & Univerity of Saarland
 Martin Ganahl, Perimeter Institute
 ChloeAminata GauvinNdiaye, University of Sherbrooke
 Paul Ginsparg, Cornell University
 Andrew Goldschmidt, University of Washington
 Anna Golubeva, Perimeter Institute
 Eliska Greplova, ETH Zurich
 Tarun Grover, University of California, San Diego
 Jan Friedrich Haase, Institute for Quantum Computing
 Lauren Hayward Sierens, Perimeter Institute
 Florian Hopfmueller, Perimeter Institute
 HongYe Hu, University of California, San Diego
 Emilie Huffman, Perimeter Institute
 ShihChun (Jimmy) Hung, Institute for Quantum Computing
 Katharine Hyatt, Flatiron Institute
 Pavithran Iyer, University of Waterloo
 Aditya Jain, Institute for Quantum Computing
 Angus Kan, Institute for Quantum Computing
 Achim Kempf, Perimeter Institute & University of Waterloo
 Faisal Khan, Khalifa University
 Jane Kim, Michigan State University
 Michael Kobierski, University of Waterloo
 Mohammad Kohandel, University of Waterloo
 Xiangzhou Kong, University of Waterloo
 Bohdan Kulchytskyy, Perimeter Institute & University of Waterloo
 Ryan LaRose, Michigan State University
 Samuel Lederer, Cornell University
 Marco Letizia, University of Waterloo and Perimeter Institute
 Junwei Liu, Hong Kong Univversity
 Yehua Liu, University of Sherbrooke
 Irene Lopez Gutierrez, Dresden University of Technology
 TsungCheng Lu, University of California, San Diego
 Ilia Luchnikov, Moscow Institute of Physics and Technology
 Xiuzhe Luo, University of Waterloo
 Hao Ma, 1QB Information Technologies
 Benjamin MacLellan, INRS
 Glen Bigan Mbeng, SISSA
 Kai Meinerz, University of Cologne
 Andre Melo, Delft University of Technology
 Ejaaz Merali, University of Waterloo
 Friederike Metz, Okinawa Institute of Science and Technology
 Christine Muschik, Perimeter Institute & University of Waterloo
 Reza Nourafkan, University of Sherbrooke
 Etude O'NeelJudy, University of Waterloo
 Evan Peters, University of Waterloo
 Jessica Pointing, Stanford University
 Jonathon Riddell, McMaster University
 Shengru Ren, 1QB Information Technologies
 Matt Richards, McMaster University
 Piotr Roztocki, INRSEMT
 Kevin Ryczko, University of Ottawa
 Hossein Sadeghi, DWave Systems Inc.
 Artur Scherer, 1QB Information Technologies
 Dan Sehayek, University of Waterloo
 Miles Stoudenmire, Flatiron Institute
 Isaac Tamblyn, National Research Council Canada
 Alain Tchagang, National Research Council Canada
 Hugo Theveniaut, CNRS
 Evan Thomas, University of Ottawa
 Brian Timar, California Institute of Technology
 Giacomo Torlai, Flatiron Institute
 Guillaume Verdon, Google
 Simon Verret, University of Montreal
 Stephen Vintskevich, Moscow Institute of Physics and Technology
 Yan Wang, University of Sherbrooke
 Yi Zhang, Peking University
Monday, July 8, 2019
Time 
Event 
Location 
9:00 – 9:25am 
Registration 
Reception 
9:25 – 9:30am 
Roger Melko, Perimeter Institute & University of Waterloo 
Theater 
9:30 – 10:15am 
Giuseppe Carleo, Flatiron Institute 
Theater 
10:1510:45am 
Coffee Break 
Bistro – 1st Floor 
10:45 – 11:30am 
Michele Ceriotti, École polytechnique fédérale de Lausanne 
Theater 
11:30 – 12:15pm 
Maria Schuld, University of KwaZuluNatal 
Theater 
12:15 – 2:00pm 
Lunch 
Bistro – 1st Floor 
2:00 – 2:30pm 
DongLing Deng, Tsinghua University 
Theater 
2:30 – 3:00pm 
Kevin Ryczko, University of Ottawa 
Theater 
3:00 – 3:30pm 
Coffee Break 
Bistro – 1st Floor 
3:30 – 4:00pm 
Emily Davis, Stanford University 
Theater 
4:00 – 4:30pm 
Giacomo Torlai, Flatiron Institute 
Theater 
4:30 – 7:00pm 
Break 

7:00 – 7:40pm 
Tanisha Bassan, The Knowledge Society 
Theater 
Tuesday, July 9, 2019
Time 
Event 
Location 
9:30 – 10:15am 
EunAh Kim, Cornell University 
Theater 
10:1510:45am 
Coffee Break 
Bistro – 1st Floor 
10:45 – 11:30am 
Evert van Nieuwenburg, California Institute of Technology 
Theater 
11:30 – 12:15pm 
Juan Carrasquilla, Vector Institute 
Theater 
12:15 – 2:00pm 
Lunch 
Bistro – 1st Floor 
2:00 – 2:30pm 
Eliska Greplova, ETH Zurich 
Theater 
2:30 – 3:00pm 
Glen Bigan Mbeng, SISSA 
Theater 
3:00 – 3:30pm 
Coffee Break 
Bistro – 1st Floor 
3:30 – 4:00pm 
Timothy Hsieh, Perimeter Institute 
Theater 
4:00 – 4:30pm 
Nicolo Defenu, Heidelberg University 
Theater 
Wednesday, July 10, 2019
Time 
Event 
Location 
9:30 – 10:15am 
Andrea Zen, University College London 
Theater 
10:1510:45am 
Coffee Break 
Bistro – 1st Floor 
10:45 – 11:30am 
Stefan Leichenauer, Google 
Theater 
11:30 – 12:15pm 
Peter Wittek, University of Toronto 
Theater 
12:15 – 2:00pm 
Lunch 
Bistro – 1st Floor 
2:00 – 2:45pm 
Kristan Temme, IBM Research 
Theater 
2:45 – 3:30pm 
Sebastiano Pilati, University of Camerino 
Theater 
3:30 – 3:45pm 
Conference Photo 
TBD 
3:45 – 4:15pm 
Coffee Break 
Bistro  1st Floor 
7:00pm onwards 
Offsite Event 
Chainsaw 
Thursday, July 11, 2019
Time 
Event 
Location 
9:30 – 10:15am 
Isaac Tamblyn, National Research Council Canada 
Theater 
10:1510:45am 
Coffee Break 
Bistro – 1st Floor 
10:45 – 11:30am 
Paul Ginsparg, Cornell University 
Theater 
11:30 – 12:15pm 
YiZhuang You, University of California, San Diego 
Theater 
12:15 – 2:00pm 
Lunch 
Bistro – 1st Floor 
2:00 – 2:45pm 
Ehsan Khatami, San Jose State University 
Theater 
2:45 – 3:30pm 
Coffee Break 
Bistro – 1st Floor 
3:30 – 4:00pm 
Olivia Di Matteo, TRUIMF 
Theater 
4:00 – 4:30pm 
Yehua Liu, University of Sherbrooke 
Theater 
5:00pm onwards 
BBQ 
Bistro – 1st Floor 
Friday, July 12, 2019
Time 
Event 
Location 
9:30 – 10:15am 
Marin Bukov, University of California, Berkeley 
Theater 
10:1510:45am 
Coffee Break 
Bistro – 1st Floor 
10:45 – 11:30am 
Pooya Ronagh, University of Waterloo 
Theater 
11:30 – 12:15pm 
Lei Wang, Chinese academy of sciences 
Theater 
12:15 – 12:20pm 
Roger Melko, Perimeter Institute & University of Waterloo 
Theater 
12:20 – 2:00pm 
Lunch 
Bistro – 1st Floor 
Speaker Talks
Marin Bukov, University of California, Berkeley
Glassy and Correlated Phases of Optimal Quantum Control
Modern Machine Learning (ML) relies on cost function optimization to train model parameters. The nonconvexity of cost function landscapes results in the emergence of local minima in which stateoftheart gradient descent optimizers get stuck. Similarly, in modern Quantum Control (QC), a key to understanding the difficulty of multiqubit state preparation holds the control landscape  the mapping assigning to every control protocol its cost function value. Reinforcement Learning (RL) and QC strive to find a better local minimum of the control landscape; the global minimum corresponds to the optimal protocol. Analyzing a decrease in the learning capability of our RL agent as we vary the protocol duration, we found rapid changes in the search for optimal protocols, reminiscent of phase transitions. These "control phase transitions" can be interpreted within Statistical Mechanics by viewing the cost function as "energy" and control protocols – as "spin configurations". I will show that optimal qubit control exhibits continuous and discontinuous phase transitions familiar from macroscopic systems: correlated/glassy phases and spontaneous symmetry breaking. I will then present numerical evidence for a universal spinglasslike transition controlled by the protocol time duration. The glassy critical point is marked by a proliferation of protocols with closetooptimal fidelity and with a true optimum that appears exponentially difficult to locate. Using a ML inspired framework based on the manifold learning algorithm tSNE, we visualize the geometry of the highdimensional control landscape in an effective lowdimensional representation. Across the transition, the control landscape features an exponential number of clusters separated by extensive barriers, which bears a strong resemblance with random satisfiability problems.
Giuseppe Carleo, Flatiron Institute
Deep learning for quantum manybody physics or: Toolmaking beyond the papyrus complexity
In this talk I will discuss some of the longterm challenges emerging with the effort of making deep learning a relevant tool for controlled scientific discovery in manybody quantum physics. The current state of the art of deep neural quantum states and learning tools will be discussed in connection with open challenging problems in condensed matter physics, including frustrated magnetism and quantum dynamics.
Michele Ceriotti, École polytechnique fédérale de Lausanne
Simulating Thermal and Quantum Fluctuations in Materials and Molecules
Both electrons and nuclei follow the laws of quantum mechanics, and even though classical approximations and/or empirical models can be quite successful in many cases, a full quantum description is needed to achieve predictive simulations of matter. Traditionally, simulations that treat both electrons and nuclei as quantum particles have been prohibitively demanding. I will present several recent algorithmic advances that have increased dramatically the range of systems that are amenable to quantum modeling: on one hand, by using accelerated path integral schemes to treat the nuclear degrees of freedom, and on the other by using machinelearning potentials to reproduce inexpensively highend electronicstructure calculations. I will give examples of both approaches, and discuss how the two can be used in synergy to make fully quantum modeling affordable.
Paul Ginsparg, Cornell University
Attention is all you get
For the past decade, there has been a new major architectural fad in deep learning every year or two.
One such fad for the past two years has been the transformer model, an implementation of the attention method which has superseded RNNs in most sequence learning applications. I'll give an overview of the model, with some discussion of nonphysics applications, and intimate some possibilities for physics.
Ehsan Khatami, San Jose State University
Machine learning phase discovery in quantum gas microscope images
Site resolution in quantum gas microscopes for ultracold atoms in optical lattices have transformed quantum simulations of manybody Hamiltonians. Statistical analysis of atomic snapshots can produce expectation values for various charge and spin correlation functions and have led to new discoveries for the Hubbard model in two dimensions. Conventional approaches, however, fail in general when the order parameter is not known or when an expected phase has no clear signatures in the density basis. In this talk, I will introduce our efforts in using machine learning techniques to overcome this challenge with snapshots of fermionic atoms. Collaborators: Richard Scalettar (UC Davis), Waseem Bakr (Princeton), and Juan Carrasquilla (Vector Institute)
Stefan Leichenauer, Google
Optimizing Quantum Optimization
Variational algorithms for a gatebased quantum computer, like the QAOA, prescribe a fixed circuit ansatz  up to a set of continuous parameters  that is designed to find a lowenergy state of a given target Hamiltonian. After reviewing the relevant aspects of the QAOA, I will describe attempts to make the algorithm more efficient. The strategies I will explore are 1) tuning the variational objective function away from the energy expectation value, 2) analytical estimates that allow elimination of some of the gates in the QAOA circuit, and 3) using methods of machine learning to search the design space of nearby circuits for improvements to the original ansatz. While there is evidence of room for improvement in the circuit ansatz, finding an ML algorithm to effect that improvement remains an outstanding challenge.
Sebastiano Pilati, University of Camerino
Machine learning groundstate energies and manybody wave functions
In the first part of this presentation, I will present supervised machinelearning studies of the lowlying energy levels of disordered quantum systems. We address singleparticle continuousspace models that describe coldatoms in speckle disorder, and also 1D quantum Ising glasses. Our results show that a sufficiently deep feedforward neural network (NN) can be trained to accurately predict lowlying energy levels. Considering the longterm prospect of using coldatoms quantum simulator to train neural networks to solve computationally intractable problems, we consider the effect of random noise in the training data, finding that the NN model is remarkably resilient. We explore the use of convolutional NN to build scalable models and to accelerate the training process via transfer learning.
In the second part, I will discuss how generative stochastic NN, specifically, restricted and unrestricted Boltzmann machines, can be used as variational Ansatz for the groundstate manybody wave functions. In particular, we show how to employ them to boost the efficiency of projective quantum Monte Carlo (QMC) simulations, and how to automatically train them within the projective QMC simulation itself.
SP, P. Pieri, Scientific Reports 9, 5613 (2019)
E. M. Inack, G. Santoro, L. Dell’Anna, SP, Physical Review B 98, 235145 (2018)
Maria Schuld, University of KwaZuluNatal
How to use a Gaussian Boson Sampler to learn from graphstructured data
A device called a ‘Gaussian Boson Sampler’ has initially been proposed as a nearterm demonstration of classically intractable quantum computation. But these devices can also be used to decide whether two graphs are similar to each other. In this talk, I will show how to construct a feature map and graph similarity measure (or ‘graph kernel’) using samples from an optical Gaussian Boson Sampler, and how to combine this with a support vector machine to do machine learning on graphstructured datasets. I will present promising benchmarking results and try to motivate why such a continuousvariable quantum computer can actually extract interesting properties from graphs.
Isaac Tamblyn, National Research Council Canada
Deep learning and density functional theory
Density functional theory is a widely used electronic structure method for simulating and designing nanoscale systems based on first principles. I will outline our recent efforts to improve density functionals using deep learning. Improvement would mean achieving higher accuracy, better scaling (with respect to system size), improved computational parallelizability, and achieving reliable performance transferability across different electronic environments.
To this end, we have generated a large and diverse dataset of 2d simulations of electrons (http://clean.energyscience.ca/datasets) with a varying number of electrons in confining potentials for several (approximate) density functionals. As a proofofprincipal, we have used extensive deep neural networks to reproduce the results of these simulations to high accuracy at significantly reduced computational cost. By learning the screening lengthscale of the electrons directly from the data, we are able to train on smallscale calculations, yet perform inference at effectively arbitrary lengthscales at only O(N) cost. This overcomes a keyscaling limitation of KohnSham DFT (which scales as O(N^3)), paving the way for accurate, large scale ab initio enabled design of nanoscale components and devices.
Kristan Temme, IBM Research
Quantum machine learning and the prospect of nearterm applications on noisy devices.
Prospective nearterm applications of early quantum devices rely on accurate estimates of expectation values to become relevant. Decoherence and gate errors lead to wrong estimates. This problem was, at least in theory, remedied with the advent of quantum error correction. However, the overhead that is needed to implement a fully faulttolerant gate set with current codes and current devices seems prohibitively large. In turn, steady progress is made in improving the quality of the quantum hardware, which leads to the believe that in the foreseeable future machines could be build that cannot be emulated by a conventional computer. In light of recent progress mitigating the effect of decoherence on expectation values, it becomes interesting to ask what these noisy devices can be used for. In this talk we will present our advances in finding quantum machine learning applications for noisy quantum computers.
Evert van Nieuwenburg, California Institute of Technology
Integrating Neural Networks with a Quantum Simulator for State Reconstruction
In this talk I will discuss how (unsupervised) machine learning methods can be useful for quantum experiments. Specifically, we will consider the use of a generative model to perform quantum manybody (pure) state reconstruction directly from experimental data. The power of this machine learning approach enables us to trade few experimentally complex measurements for many simpler ones, allowing for the extraction of sophisticated observables such as the Rényi mutual information. These results open the door to integration of machine learning architectures with intermediatescale quantum hardware.
Lei Wang, Chinese academy of sciences
Differentiable Programming Tensor Networks and Quantum Circuits
Differentiable programming makes the optimization of a tensor network much cheaper (in unit of brain energy consumption) than before [e.g. arXiv: 1903.09650]. This talk mainly focuses on the technical aspects of differentiable programming tensor networks and quantum circuits with Yao.jl (https://github.com/QuantumBFS/Yao.jl). I will also show how quantum circuits can help with contracting and differentiating tensor networks.
Peter Wittek, University of Toronto
Vulnerability of quantum systems to adversarial perturbations
Highdimensional quantum systems are vital for quantum technologies and are essential in demonstrating practical quantum advantage in quantum computing, simulation and sensing. Since dimensionality grows exponentially with the number of qubits, the potential power of noisy intermediatescale quantum (NISQ) devices over classical resources also stems from entangled states in high dimensions. An important family of quantum protocols that can take advantage of highdimensional Hilbert space are classification tasks. These include quantum machine learning algorithms, witnesses in quantum information processing and certain decision problems. However, due to counterintuitive geometrical properties emergent in high dimensions, classification problems are vulnerable to adversarial attacks. We demonstrate that the amount of perturbation needed for an adversary to induce a misclassification scales inversely with dimensionality. This is shown to be a fundamental feature independent of the details of the classification protocol. Furthermore, this leads to a tradeoff between the security of the classification algorithm against adversarial attacks and quantum advantages we expect for highdimensional problems. In fact, protection against these adversarial attacks require extra resources that scale at least polynomially with the Hilbert space dimension of the system, which can erase any significant quantum advantage that we might expect from a quantum protocol. This has wideranging implications in the use of both nearterm and future quantum technologies for classification.
YiZhuang You, University of California, San Diego
Machine Learning Physics: From Quantum Mechanics to Holographic Geometry
Inspired by the "third wave" of artificial intelligence (AI), machine learning has found rapid applications in various topics of physics research. Perhaps one of the most ambitious goals of machine learning physics is to develop novel approaches that ultimately allows AI to discover new concepts and governing equations of physics from experimental observations. In this talk, I will present our progress in applying machine learning technique to reveal the quantum wave function of BoseEinstein condensate (BEC) and the holographic geometry of conformal field theories. In the first part, we apply machine translation to learn the mapping between potential and density profiles of BEC and show how the concept of quantum wave function can emerge in the latent space of the translator and how the Schrodinger equation is formulated as a recurrent neural network. In the second part, we design a generative model to learn the field theory configuration of the XY model and show how the machine can identify the holographic bulk degrees of freedom and use them to probe the emergent holographic geometry.
Andrea Zen, University College London
The challenge to deliver high accuracy on large computer simulations
Computer simulations are extremely useful in providing insight on the physical and chemical processes taking places in nature. Very often simulations are complementary to experimental investigations, providing the interpretations and the molecular level understanding that experiments struggle to deliver. Yet, simulations are useful only when their results may be relied upon, that is, when they can accurately model the physical system and the forces therein.
Thriving nanotechnologies and exciting experiments pose a big challenge to computational approaches, especially when dealing with solidliquid interfaces. On the one hand, the systems to be simulated are large and often long molecular dynamics simulations are needed. On the other hand, extremely high accuracy is required.
We discuss here an approach to deliver high accuracy at low computational cost using quantum Monte Carlo and Machine Learning.
Contributed Talks
Emily Davis, Stanford University
Engineering Programmable Spin Interactions in a NearConcentric Cavity
Photonmediated interactions among atoms coupled to an optical cavity are a powerful tool for engineering quantum manybody Hamiltonians. We present observations of dynamics of spins evolving under continuously tunable Heisenberg models, where the relative strength and sign of spinexchange and Ising couplings are controllable parameters. The interaction dynamics manifest as rotations of large effective spins in a meanfield picture, as well as a spinmixing process seeded by quantum fluctuations, which in principle generates a highly entangled twin Fock state. Whereas the singlemode cavity most naturally mediates alltoall couplings, I will discuss progress in generalizing to control the distancedependence of the interactions. The optical access afforded by the nearconcentric cavity geometry enables spatiallydependent addressing and imaging with micronscale resolution, providing opportunities to perform both state and Hamiltonian tomography on the experimental data.
Nicolo Defenu, Heidelberg University
Quantum scale anomaly and spatial coherence in a 2D Fermi superfluid
Quantum anomalies are violations of classical scaling symmetries caused by quantum fluctuations. Although they appear prominently in quantum field theory to regularize divergent physical quanti ties, their influence on experimental observables is difficult to discern. Here, we discovered a striking manifestation of a quantum anomaly in the momentumspace dynamics of a 2D Fermi superfluid of ultracold atoms. We measured the position and pair momentum distribution of the superfluid during a breathing mode cycle for different interaction strengths across the BECBCS crossover. Whereas the system exhibits selfsimilar evolution in the weakly interacting BEC and BCS limits, we found a violation in the strongly interacting regime. The signature of scaleinvariance breaking is enhanced in the firstorder coherence function. In particular, the powerlaw exponents that char acterize longrange phase correlations in the system are modified due to this effect, indicating that the quantum anomaly has a significant influence on the critical properties of 2D superfluids.
DongLing Deng, Tsinghua University
Machine learning meets quantum physics
Recently, machine learning has attracted tremendous interest across different communities. In this talk, I will briefly introduce some new progresses in the emergent field of quantum machine learning an interdisciplinary field that explores the interactions between quantum physics and machine learning. On the one hand, I will talk about a couple of quantum algorithms that promise an exponential speedup for machine learning tasks. On the other hand, I will show how ideas and techniques from machine learning can help solve challenging problems in the quantum domain.
Olivia Di Matteo, TRIUMF
Operational quantum tomography
As quantum processors become increasingly refined, benchmarking them in useful ways becomes a critical topic. Traditional approaches to quantum tomography, such as state tomography, suffer from selfconsistency problems, requiring either perfectly precalibrated operations or measurements. This problem has recently been tackled by explicitly selfconsistent protocols such as randomized benchmarking, robust phase estimation, and gate set tomography (GST). An undesired sideeffect of selfconsistency is the presence of gauge degrees of freedom, arising from the lack fiducial reference frames, and leading to large families of gaugeequivalent descriptions of a quantum gate set which are difficult to interpret.
We solve this problem through introducing a gaugefree representation of a quantum gate set inspired by linear inversion GST. This allows for the efficient computation of any experimental frequency without a gauge fixing procedure. We use this approach to implement a Bayesian version of GST using the particle filter approach, which was previously not possible due to the gauge.
Within Bayesian GST, the prior information allows for inference on tomographically incomplete data sets, such as Ramsey experiments, without giving up selfconsistency. We demonstrate the stability and generality of both our gaugefree representation and Bayesian GST by simulating a number of common characterization protocols, such as randomized benchmarking, as well characterizing a trappedion qubit using experimental data.
Sandia National Labs is managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a subsidiary of Honeywell International, Inc., for the U.S. Dept. of Energy’s National Nuclear Security Administration under contract DENA0003525.
The views expressed in this presentation do not necessarily represent the views of the DOE, the ODNI, or the U.S. Government. This material was funded in part by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research Quantum Testbed Program.
Olivia Di Matteo, TRIUMF, Vancouver, BC, Canada and Microsoft Research, Redmond, WA, USA
John Gamble, Microsoft Research, Redmond, WA, USA
Chris Granada, Microsoft Research, Redmond, WA, USA
Kenneth Ruddinger, Quantum Performance Laboratory, Sandia National Laboratories, Albuquerque, NM, USA
Nathan Wiebe, Microsoft Research, Redmond, WA, USA
Eliska Greplova, ETH Zurich
Quantum Error Correction via Hamiltonian Learning
Successful implementation of error correction is imperative for faulttolerant quantum computing. At present, the toric code, surface code and related stabilizer codes are state of the art techniques in error correction.
Standard decoders for these codes usually assume uncorrelated single qubit noise, which can prove problematic in a general setting.
In this work, we use the knowledge of topological phases of modified toric codes to identify the underlying Hamiltonians for certain types of imperfections. The Hamiltonian learning is employed to adiabatically remove the underlying noise and approach the ideal toric code Hamiltonian. This approach can be used regardless of correlations. Our method relies on a neural network reconstructing the Hamiltonian given as input a linear amount of expectation values. The knowledge of the Hamiltonian offers significant improvement of standard decoding techniques
Eliska Greplova, Agnes Valenti, Evert van Nieuwenburg, Sebastian Huber
Timothy Hsieh, Perimeter Institute
Shortcuts in Real and Imaginary Time
In the first half, I will demonstrate an efficient and general approach for realizing nontrivial quantum states, such as quantum critical and topologically ordered states, in quantum simulators. In the second half, I will present a related variational ansatz for manybody quantum systems that is remarkably efficient. In particular, representing the critical point of the onedimensional transverse field Ising model only requires a number of variational parameters scaling logarithmically with system size. Though optimizing the ansatz generally requires Monte Carlo sampling, our ansatz potentially enables a partial mitigation of the sign problem at the expense of having to optimize a few parameters.
Yehua Liu, University of Sherbrooke
Neural BeliefPropagation Decoders for Quantum ErrorCorrecting Codes
Beliefpropagation (BP) decoders are responsible for the success of many modern coding schemes. While many classical coding schemes have been generalized to the quantum setting, the corresponding BP decoders are flawed by design in this setting. Inspired by an exact mapping between BP and deep neural networks, we train neural BP decoders for quantum lowdensity paritycheck codes, with a loss function tailored for the quantum setting. Training substantially improves the performance of the original BP decoders. The flexibility and adaptability of the neural BP decoders make them suitable for lowoverhead error correction in nearterm quantum devices.
Reference: arXiv:1811.07835 (to appear in PRL)
Glen Bigan Mbeng, SISSA
The Quantum Approximate Optimization Algorithm and spin chains
Various optimization problems that arise naturally in science are frequently solved by heuristic algorithms. Recently, multiple quantum enhanced algorithms have been proposed to speed up the optimization process, however a quantum speed up on practical problems has yet to be observed. One of the most promising candidates is the Quantum Approximate Optimization Algorithm (QAOA), introduced by Farhi et al. I will then discuss numerical and exact results we have obtained for the quantum Ising chain problem and compare the performance of the QAOA and the Quantum Annealing algorithm. I will also briefly describe the landscape that emerges from the optimization problem and how techniques borrowed from machine learning can be used to improve the optimization process.
Kevin Ryczko, University of Ottawa
Designing a Quantum Transducer With Genetic Algorithms and Electron Transport Calculations
The fields of quantum information and quantum computation are reliant on creating and maintaining lowdimensional quantum states. In twodimensional hexagonal materials, one can describe a twodimensional quantum state with electron quasimomentum. This description, often referred to as valleytronics allows one to define a twostate vector labelled by k and k', which correspond to symmetric valleys in the conduction band. In this work, we present an algorithm that allows one to construct a nanoscale device that topologically separates k and k' current. Our algorithm incorporates electron transport calculations, artificial neural networks, and genetic algorithms to find structures that optimize a custom objective function. Our first result is that when modifying the onsite energies via doping with simple shapes the genetic algorithm is able to find structures that are able to topologically separate the valley currents with approximately 90% purity. We then introduce an arbitrary shape generator via a policy defined by an artificial neural network to modify the onsite energies of the nanoribbons. We study the dynamics of the genetic algorithms for both cases. Lastly, we then attempt to physically motivate the solutions by mapping the high dimensional search space to a lower dimensional one that can be better understood.
Giacomo Torlai, Flatiron Institute
Alleviating the sign structure of quantum states
The sign structure of quantum states  the appearance of “probability” amplitudes with negative sign  is one of the most striking contrasts between the classical and the quantum world, with farreaching implications in condensed matter physics and quantum information science. Because it is a basisdependent property, one may wonder: is a given sign structure truly intrinsic, or can it be removed by a local change of basis? In this talk, I will present an algorithm based on automatic differentiation of tensor networks for discovering nonnegative representations of manybody wavefunctions. I will show some numerical results for ground states of a twoleg triangular Heisenberg ladder, including an exotic Bosemetal phase.
Goodbye and Closing Remarks
Differentiable Programming Tensor Networks and Quantum Circuits
Differentiable programming makes the optimization of a tensor network much cheaper (in unit of brain energy consumption) than before [e.g. arXiv: 1903.09650]. This talk mainly focuses on the technical aspects of differentiable programming tensor networks and quantum circuits with Yao.jl (https://github.com/QuantumBFS/Yao.jl). I will also show how quantum circuits can help with contracting and differentiating tensor networks.
RLdriven Quantum Computation
Glassy and Correlated Phases of Optimal Quantum Control
Modern Machine Learning (ML) relies on cost function optimization to train model parameters. The nonconvexity of cost function landscapes results in the emergence of local minima in which stateoftheart gradient descent optimizers get stuck. Similarly, in modern Quantum Control (QC), a key to understanding the difficulty of multiqubit state preparation holds the control landscape  the mapping assigning to every control protocol its cost function value.
Neural BeliefPropagation Decoders for Quantum ErrorCorrecting Codes
Beliefpropagation (BP) decoders are responsible for the success of many modern coding schemes. While many classical coding schemes have been generalized to the quantum setting, the corresponding BP decoders are flawed by design in this setting. Inspired by an exact mapping between BP and deep neural networks, we train neural BP decoders for quantum lowdensity paritycheck codes, with a loss function tailored for the quantum setting. Training substantially improves the performance of the original BP decoders.
Operational quantum tomography
As quantum processors become increasingly refined, benchmarking them in useful ways becomes a critical topic. Traditional approaches to quantum tomography, such as state tomography, suffer from selfconsistency problems, requiring either perfectly precalibrated operations or measurements. This problem has recently been tackled by explicitly selfconsistent protocols such as randomized benchmarking, robust phase estimation, and gate set tomography (GST).
Machine learning phase discovery in quantum gas microscope images
Site resolution in quantum gas microscopes for ultracold atoms in optical lattices have transformed quantum simulations of manybody Hamiltonians. Statistical analysis of atomic snapshots can produce expectation values for various charge and spin correlation functions and have led to new discoveries for the Hubbard model in two dimensions. Conventional approaches, however, fail in general when the order parameter is not known or when an expected phase has no clear signatures in the density basis.
Machine Learning Physics: From Quantum Mechanics to Holographic Geometry
Inspired by the "third wave" of artificial intelligence (AI), machine learning has found rapid applications in various topics of physics research. Perhaps one of the most ambitious goals of machine learning physics is to develop novel approaches that ultimately allows AI to discover new concepts and governing equations of physics from experimental observations. In this talk, I will present our progress in applying machine learning technique to reveal the quantum wave function of BoseEinstein condensate (BEC) and the holographic geometry of conformal field theories.
Attention is all you get
For the past decade, there has been a new major architectural fad in deep learning every year or two.
One such fad for the past two years has been the transformer model, an implementation of the attention method which has superseded RNNs in most sequence learning applications. I'll give an overview of the model, with some discussion of nonphysics applications, and intimate some possibilities for physics.
Deep learning and density functional theory
Density functional theory is a widely used electronic structure method for simulating and designing nanoscale systems based on first principles. I will outline our recent efforts to improve density functionals using deep learning. Improvement would mean achieving higher accuracy, better scaling (with respect to system size), improved computational parallelizability, and achieving reliable performance transferability across different electronic environments.
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 Juan Carrasquilla, Vector Institute
 Estelle Inack Perimeter Institute
 Roger Melko, Perimeter Institute & University of Waterloo
 Sandro Sorella, SISSA