# Machine Learning for Quantum Design

Conference Date:
Monday, July 8, 2019 (All day) to Friday, July 12, 2019 (All day)
Scientific Areas:
Condensed Matter
Quantum Information

Machine learning techniques are rapidly being adopted into the field of quantum many-body physics, including condensed matter theory, experiment, and quantum information science.  The steady increase in data being produced by highly-controlled 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 many-body 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:

• Data-drive quantum state reconstruction
• Machine learning strategies for quantum error correction
• Neural-network based wavefunctions
• Near-term prospects for data from quantum devices
• Machine learning for quantum algorithm discovery

Registartion 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
• Eun-Ah Kim, Cornell University
• Sebastiano Pilati, University of Camerino
• Pooya Ronagh, University of Waterloo
• Maria Schuld, University of KwaZulu-Natal
• Kristan Temme, California Institute of Technology
• Evert van Nieuwenburg, California Institute of Technology
• Lei Wang, Chinese Academy of Sciences
• Peter Wittek, University of Toronto
• Yi-Zhuang You, University of California, San Diego
• Andrea Zen, University College London
• Nour Abura'ed, University of Dubai
• 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
• Dong-Ling 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
• Chloe-Aminata Gauvin-Ndiaye, 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
• Timothy Hsieh, Perimeter Institute
• Hong-Ye Hu, University of California, San Diego
• Emilie Huffman, Perimeter Institute
• Shih-Chun (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
• Ehsan Khatami, San Jose State University
• Jane Kim, Michigan State University
• Michael Kobierski, University of Waterloo
• Mohammad Kohandel, University of Waterloo
• Xiangzhou Kong, University of Waterloo
• Emine Kucukbenli, SISSA
• Bohdan Kulchytskyy, Perimeter Institute & University of Waterloo
• Ryan LaRose, Michigan State University
• Samuel Lederer, Cornell University
• Marco Letizia, University of Waterloo and Perimeter Institute
• JinGuo Liu, Chinese Academy of Sciences
• Junwei Liu, Hong Kong Univversity
• Yehua Liu, University of Sherbrooke
• Irene Lopez Gutierrez, Dresden University of Technology
• Tsung-Cheng 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'Neel-Judy, 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, INRS-EMT
• Kevin Ryczko, University of Ottawa
• Hossein Sadeghi, D-Wave 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
• 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 WaterlooWelcome and Opening Remarks Theater 9:30 – 10:15am Giuseppe Carleo, Flatiron InstituteDeep learning for quantum many-body physics or: Toolmaking beyond the papyrus complexity Theater 10:15-10:45am Coffee Break Bistro – 1st Floor 10:45 – 11:30am Michele Ceriotti, École polytechnique fédérale de LausanneSimulating Thermal and Quantum Fluctuations in Materials and Molecules Theater 11:30 – 12:15pm Maria Schuld, University of KwaZulu-NatalHow to use a Gaussian Boson Sampler to learn from graph-structured data Theater 12:15 – 2:00pm Lunch Bistro – 2nd Floor 2:00 – 2:30pm Dong-Ling Deng, Tsinghua UniversityMachine learning meets quantum physics Theater 2:30 – 3:00pm Kevin Ryczko, University of OttawaDesigning a Quantum Transducer with Genetic Algorithms and Electron Transport Calculations Theater 3:00 – 3:30pm Coffee Break Bistro – 1st Floor 3:30 – 4:00pm Emily Davis, Stanford UniversityEngineering Programmable Spin Interactions in a Near-Concentric Cavity Theater 4:00 – 4:30pm Giacomo Torlai, Flatiron InstituteAlleviating the sign structure of quantum states Theater 4:30 – 7:00pm Break 7:00 – 7:40pm Tanisha Bassan, The Knowledge SocietyTBA Theater

Tuesday, July 9, 2019

 Time Event Location 9:30 – 10:15am Eun-Ah Kim, Cornell UniversityTBA Theater 10:15-10:45am Coffee Break Bistro – 1st Floor 10:45 – 11:30am Paul Ginsparg, Cornell UniversityTBA Theater 11:30 – 12:15pm Lei Wang, Chinese Academy of SciencesTBA Theater 12:15 – 2:00pm Lunch Bistro – 2nd Floor 2:00 – 2:30pm Eliska Greplova, ETH ZurichQuantum Error Correction via Hamiltonian Learning Theater 2:30 – 3:00pm Glen Bigan Mbend, SISSAThe Quantum Approximate Optimization Algorithm and spin chains Theater 3:00 – 3:30pm Coffee Break Bistro – 1st Floor 3:30 – 4:00pm JinGuo Liu, Chinese Academy of SciencesDifferentiable Programming Tensor Networks and Quantum Circuits Theater 4:00 – 4:30pm Nicolo Defenu, Heidelberg UniversityQuantum scale anomaly and spatial coherence in a 2D Fermi superfluid Theater

Wednesday, July 10, 2019

 Time Event Location 9:30 – 10:15am Andrea Zen, University College LondonThe challenge to deliver high accuracy on large computer simulations Theater 10:15-10:45am Coffee Break Bistro – 1st Floor 10:45 – 11:30am Stefan Leichenauer, GoogleOptimizing Quantum Optimization Theater 11:30 – 12:15pm Peter Wittek, University of TorontoVulnerability of quantum systems to adversarial perturbations Theater 12:15 – 2:00pm Lunch Bistro – 2nd Floor 2:00 – 2:45pm Kristan Temme, California Institute of TechnologyQuantum machine learning and the prospect of near-term applications on noisy devices. Theater 2:45 – 3:30pm Sebastiano Pilati, University of CamerinoMachine learning ground-state energies and many-body wave functions Theater 3:30 – 4:00pm Coffee Break Bistro – 1st Floor 4:00 – 4:15pm Conference Photo TBA 5:00pm onwards Offsite Event Chainsaw

Thursday, July 11, 2019

 Time Event Location 9:30 – 10:15am Isaac Tamblyn, TBATBA Theater 10:15-10:45am Coffee Break Bistro – 1st Floor 10:45 – 11:30am Evert van Nieuwenburg, California Institute of TechnologyIntegrating Neural Networks with a Quantum Simulator for State Reconstruction Theater 11:30 – 12:15pm Yi-Zhuang You, University of California, San DiegoMachine Learning Physics: From Quantum Mechanics to Holographic Geometry Theater 12:15 – 2:00pm Lunch Bistro – 2nd Floor 2:00 – 2:30pm Emine Kucukbenli, SISSAMachine learning inter-atomic potentials Theater 2:30 – 3:00pm Olivia Di Matteo, TRUIMFOperational quantum tomography Theater 3:00 – 3:30pm Coffee Break Bistro – 1st Floor 3:30 – 4:00pm Ehsan Khatami, San Jose State UniversityMachine learning phase discovery in quantum gas microscope images Theater 4:00 – 4:30pm Yehua Liu, University of SherbrookeNeural Belief-Propagation Decoders for Quantum Error-Correcting Codes Theater 5:00pm onwards BBQ Bistro – 2nd Floor

Friday, July 12, 2019

 Time Event Location 9:30 – 10:15am Marin Bukov, University of California, BerkeleyGlassy and Correlated Phases of Optimal Quantum Control Theater 10:15-10:45am Coffee Break Bistro – 1st Floor 10:45 – 11:30am Pooya Ronagh, University of WaterlooTBA Theater 11:30 – 12:15pm Juan Carrasquilla, Vector InstituteTBA Theater 12:15 – 12:20pm Roger Melko, Perimeter Institute & University of WaterlooGoodbye and Closing remarks Theater 12:20 – 2:00pm Lunch Bistro – 2nd 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 non-convexity of cost function landscapes results in the emergence of local minima in which state-of-the-art 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 spin-glass-like transition controlled by the protocol time duration. The glassy critical point is marked by a proliferation of protocols with close-to-optimal fidelity and with a true optimum that appears exponentially difficult to locate. Using a ML inspired framework based on the manifold learning algorithm t-SNE, we visualize the geometry of the high-dimensional control landscape in an effective low-dimensional 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 many-body physics or: Toolmaking beyond the papyrus complexity

In this talk I will discuss some of the long-term challenges emerging with the effort of making deep learning a relevant tool for controlled scientific discovery in many-body 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 machine-learning  potentials to reproduce inexpensively high-end electronic-structure calculations. I will give examples of both approaches, and discuss how the two can be used in synergy to make fully quantum modeling affordable.

Optimizing Quantum Optimization

Variational algorithms for a gate-based quantum computer, like the QAOA, prescribe a fixed circuit ansatz --- up to a set of continuous parameters --- that is designed to find a low-energy 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 ground-state energies and many-body wave functions

In the first part of this presentation, I will present supervised machine-learning studies of the low-lying energy levels of disordered quantum systems. We address single-particle continuous-space models that describe cold-atoms in speckle disorder, and also 1D quantum Ising glasses. Our results show that a sufficiently deep feed-forward neural network (NN) can be trained to accurately predict low-lying energy levels. Considering the long-term prospect of using cold-atoms 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 ground-state many-body 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 KwaZulu-Natal

How to use a Gaussian Boson Sampler to learn from graph-structured data

A device called a ‘Gaussian Boson Sampler’ has initially been proposed as a near-term 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 graph-structured datasets. I will present promising benchmarking results and try to motivate why such a continuous-variable quantum computer can actually extract interesting properties from graphs.

Kristan Temme, California Institute of Technology

Quantum machine learning and the prospect of near-term applications on noisy devices.

Prospective near-term 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 fault-tolerant 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 many-body (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 intermediate-scale quantum hardware.

Peter Wittek, University of Toronto

Vulnerability of quantum systems to adversarial perturbations

Yi-Zhuang 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 Bose-Einstein 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 solid-liquid 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 Near-Concentric Cavity

Photon-mediated interactions among atoms coupled to an optical cavity are a powerful tool for engineering quantum many-body Hamiltonians. We present observations of dynamics of spins evolving under continuously tunable Heisenberg models, where the relative strength and sign of spin-exchange and Ising couplings are controllable parameters. The interaction dynamics manifest as rotations of large effective spins in a mean-field picture, as well as a spin-mixing process seeded by quantum fluctuations, which in principle generates a highly entangled twin Fock state. Whereas the single-mode cavity most naturally mediates all-to-all couplings, I will discuss progress in generalizing to control the distance-dependence of the interactions. The optical access afforded by the near-concentric cavity geometry enables spatially-dependent addressing and imaging with micron-scale 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 momentum-space 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 BEC-BCS crossover. Whereas the system exhibits self-similar evolution in the weakly interacting BEC and BCS limits, we found a violation in the strongly interacting regime. The signature of scale-invariance breaking is enhanced in the first-order coherence function. In particular, the power-law exponents that char- acterize long-range 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.

Dong-Ling 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 speed-up 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 self-consistency problems, requiring either perfectly pre-calibrated operations or measurements. This problem has recently been tackled by explicitly self-consistent protocols such as randomized benchmarking, robust phase estimation, and gate set tomography (GST). An undesired side-effect of self-consistency is the presence of gauge degrees of freedom, arising from the lack fiducial reference frames, and leading to large families of gauge-equivalent descriptions of a quantum gate set which are difficult to interpret.

We solve this problem through introducing a gauge-free 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 self-consistency. We demonstrate the stability and generality of both our gauge-free representation and Bayesian GST by simulating a number of common characterization protocols, such as randomized benchmarking, as well characterizing a trapped-ion 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 DE-NA0003525.
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 fault-tolerant 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

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 many-body 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)

Emine Kucukbenli, SISSA

Machine learning inter-atomic potentials

Describing the relationship between atomic positions and total energy, E({R}), is a fundamental aim for many modern physics, chemistry and material science simulations. This relationship, due to its quantum mechanical foundation, is tractable only in a small domain of systems, and even relatively low cost first principles methods such as Density Functional Theory are limited in addressing systems of realistic size and complexity. To overcome those limits, inter-atomic potentials have been parametrized to approximate the function that maps atomic positions to energy in the domain of a target material. In this talk, we will report our efforts in performing this functional approximation via neural network methods on a range of materials. We will examine the dependence of network performance on the data, representation, activation functions and training dynamics; and explore the strategies of obtaining the best results with the least computational effort. We will conclude with a brief overview of current developments and challenges in the field.

JinGuo Liu, 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.

Yehua Liu, University of Sherbrooke

Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes

Belief-propagation (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 low-density parity-check 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 low-overhead error correction in near-term 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 low-dimensional quantum states. In two-dimensional hexagonal materials, one can describe a two-dimensional quantum state with electron quasi-momentum. This description, often referred to as valleytronics allows one to define a two-state 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 on-site 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 on-site 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 far-reaching implications in condensed matter physics and quantum information science. Because it is a basis-dependent 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 non-negative representations of many-body wavefunctions. I will show some numerical results for ground states of a two-leg triangular Heisenberg ladder, including an exotic Bose-metal phase.

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• Exit Highway 401 at Highway 8 West.
• Take exit 278 (Highway 8 West) and follow 8 West for 5 km to Highway 85, towards Highway 7 East.
• Proceed on Highway 85 for 5 km to Bridgeport Road exit. Turn right at the off-ramp, traveling west.
• Bridgeport is a four-lane one-way road. It becomes Caroline Street at Albert Street. Continue straight ahead.
• Travel forward another 200 metres, but ease over into the right-hand lane. As you go down a hill and around a curve, look for the green Perimeter Institute sign on the right hand side. The parking lot entrance is just after the sign (past the historic grist mill that sits on the edge of Silver Lake).
• Turn right into the PI parking lot entrance.

Please note that parking is extremely limited at Perimeter Institute and you must have a parking permit to park in the lot long term.

If you are an invited speaker to this event, Perimeter Institute staff will contact you to coordinate hotel accommodations on your behalf.

If you need accommodations while attending this workshop, we offer suggestions for lodging below.  When booking your reservation, please indicate that you will be attending an event at Perimeter in order to receive the best possible rate.

Delta Waterloo 110 Erb Street West Waterloo, ON N2L 0C6
Phone: 1-888-890-3222
Distance from PI: 450 m

Comfort Inn 190 Weber Street North Waterloo, ON N2J 3H4
Phone: 519-747-9400
Distance from PI: 2.3 km

Walper Terrace Hotel 1 King Street West Kitchener, ON N2G 1A1
Phone: 519-745-4321
Distance from PI: 3.7 km

Crowne Plaza Kitchener-Waterloo 105 King Street East Waterloo, ON N2G 2K8
Phone: 519-744-4141
Distance from PI: 3.8 km

Holiday Inn Express & Suites 14 Benjamin Road Waterloo, ON N2V 2J9
Phone:  519-772-9800
Distance from PI:  5.6 km

Homewood Suites by Hilton 45 Benjamin Road Waterloo, ON N2V 2G8
Phone:  519-514-0088
Distance from PI:  5.6 km

Courtyard by Marriott 50 Benjamin Road East Waterloo, ON N2V 2J9
Phone: 519-884-9295
Distance from PI: 5.6 km

• Juan Carrasquilla, Vector Institute
• Estelle Inack Perimeter Institute
• Roger Melko, Perimeter Institute & University of Waterloo
• Sandro Sorella, SISSA