The IEEE Quantum Week Posters program presents excellent opportunities for graduate students, undergraduate students, researchers, practitioners, entrepreneurs, and start-ups to showcase their work and engage with the international quantum computing R&D community as an integral part of the IEEE Quantum Week Exhibits. QCE20 Posters are intended to stimulate discussions on recent advances, experiences, and challenges in quantum computing and engineering.
QCE20 Format and Schedule
QCE20 will be held in a digital-only or virtual-only format during the week of Oct 12-16, 2020. QCE20 was originally scheduled to be held in Broomfield, Colorado which is in the Mountain Time Zone (MT) or UTC-6. The QCE20 daily program will be broadcast from 8:30 to 19:45 Mountain Time. Recorded sessions will be available for registered QCE20 attendees a few hours after the live sessions until November 30, 2020. Every day of this week, QCE20 hosts 9-10 parallel tracks of quantum computing and engineering programming including Workshops, Tutorials, Technical Paper Tracks intermixed with Keynotes, Panels, Exhibits, Posters, Birds of a Feather, and Networking sessions featuring a total of over 270 hours of programming.
Please visit the Preliminary Conference Program for the time and date of the QCE20 Posters. As a QCE20 Poster Presenter, please visit this Panel Presentation Guideline page regularly for updates on how to prepare your QCE20 Poster presentation for the week of October 12-16, 2020.
For any inquiries or questions about Posters, please contact Posters Co-Chair Ulrike Stege.
The following Posters have been accepted for presentation at the virtual QCE20 and inclusion of the abstracts Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE20). QCE20 Posters have been peer-reviewed by at least two experts who are members of the QCE20 Posters committee.
QCE20 Posters will be presented virtually in dedicated Poster Rooms during the week of Oct 12-16, 2020 every day in the Poster Sessions between 10:00-10:45, 12:15-13:00, 14:30-15:15, and 16:45-17:30 Mountain Time (MT) — UTC-6.
Mon, Oct 12 — Quantum Information Science Tools
- 10:00-10:45 — Milan Williams, Elisa Zhao Hang, Adinawa Adjagbodjou, Robert Krueger and Johanna Beyer, Harvard University, USA: QuVis: A Quantum Circuit Visualization Tool for Novices
- 10:00-10:45 — Alena Mastiukova, Evgeniy Kiktenko and Aleksey Fedorov, Russian Quantum Center and Moscow Institute of Physics and Technology: Suppressing Decoherence in Quantum Systems with Unitary Operations
Mon, Oct 12 — Practical Quantum Computing and Applications
- 12:15-13:00 — James Cruise, Neil Gillespie and Brendan Reid, Riverlane, Cambridge, UK: Practical Quantum Computing: The Value of Local Computation
- 12:15-13:00 — Saasha Joshi, Panjab University, Chandigarh, India: Defence Applications of Quantum Computing
Tue, Oct 13 — Ion Trap Hardware and Software Technologies 1
- 10:00-10:45 — Virginia Frey, Richard Rademacher, Noah Greenberg, Nikolay Videnov, Matthew Day, Crystal Senko and Rajibul Islam, University of Waterloo, Canada: A Unified Software Control System for Open-access Trapped Ion Quantum Computers
- 10:00-10:45 — Richard Rademacher, Virginia Frey, Noah Greenberg, Nikolay Videnov, Matthew Day, Crystal Senko and Rajibul Islam, University of Waterloo, Canada: A Unified Electronic Control System for Open-access Trapped Ion Quantum Computers
Tue, Oct 13 — Ion Trap Hardware and Software Technologies 2
- 12:15-13:00 —Quentin Bodart, Foni R. Lebrun-Gallagher, Nicholas Johnson, Martin Siegele, Seokjun Hong, Sebastian Weidt and Winfried K. Hensinger, University of Sussex, UK: Constructing a Scalable Trapped-ion Quantum Computer Demonstrator Device
- 12:15-13:00 —Samuel Hile, Alex Owens, David Bretaud, Raphael Lebrun, Martin Siegele, Seokjun Hong, Reuben Puddy, Sebastian Weidt and Winfried Hensinger, University of Sussex, UK: Engineering a Scalable Logical Qubit in a 2D Surface Ion Trap Array
- 12:15-13:00 —Tomas Navickas, Mitchell Peaks, Chris Knapp, Christophe Valahu, Foni R. Lebrun-Gallagher, Martin Siegele, Reuben K. Puddy, Seokjun Hong, David F. Murgia, Eamon D. Standing, Adam M. Lawrence, Zak D. Romaszko, Sebastian Weidt and Winfried K. Hensinger, University of Sussex and Imperial College London, UK: Towards High-fidelity Logical Gates with Trapped Ion Qubits
- 12:15-13:00 —Mark Webber, Steven Herbert, Sebastian Weidt and Winfried Hensinger, University of Sussex, UK: Enabling Global Connectivity in a Shuttling Based Trapped Ion Quantum Computer with Efficient Routing
- 12:15-13:00 —David Bretaud, Samuel Hile, Alexander Owens, Daisy Smith, Sebastian Weidt, Florian Mintert and Winfried Hensinger, University of Sussex and Imperial College London, UK: Open Source Quantum Code Compilation for Scalable Trapped Ion Quantum Processors
Tue, Oct 13 — Ion Trap Hardware and Software Technologies 3
- 14:30-15:15 — David Allcock, Chris Ballance, Sébastien Bourdeauducq, Joseph Britton, Michal Gaska, Thomas Harty, Jakub Jarosinski, Robert Jördens, Paweł Kulik, David Nadlinger, Krzysztof Pozniak, Tomasz Przywozki, Daniel Slichter, Mikolaj Sowinski, Weida Zhang and Grzegorz Kasprowicz, University of Sussex and Imperial College London, UK: Sinara: An Open Hardware Ecosystem for Quantum
- 14:30-15:15 — Miguel Usach, Jon Kraft and Fintan Leamy, Analog Devices, USA: Low Noise Controllers for Ion-Trap Quantum Computers
Tue, Oct 13—Ion Trap Hardware and Software Technologies 4
- 16:45-17:15 — Dave Campagna and Tom Markham, Honeywell Quantum Solutions, USA: Engineering Mid-Circuit Measurement
- 16:45-17:15 — Ryan Daniel, Honeywell Quantum Solutions, USA: Cryotronics Test Chamber
- 16:45-17:15 — Ryan Jacobs, Ben Spaun, Jeremy Parks, David Liefer, and Charlie Dosdall, Honeywell Quantum Solutions: Automated Testing Methods of Surface Ion Traps in Quantum Computing
Wed, Oct 14 — Hybrid Quantum-Classical Computing and Applications
- 10:00-10:45 — Daniel Claudino, Jerimiah Wright, Alexander McCaskey, Dmitry Lyakh and Travis Humble, Oak Ridge National Laboratory (ORNL), USA: VQE Approaches for Quantum Chemistry in XACC
- 10:00-10:45 — Prashanti Priya Angara, University of Victoria, Canada: Problem Solving in the NISQ Era: Exploring Hybrid Quantum-Classical Approaches
Wed, Oct 14 — Quantum Machine Learning (QML)
- 12:15-13:00 — Siddharth Sharma, Stanford University, USA: Implementing a Novel Quantum K-Nearest Neighbors Learning Algorithm for Breast Cancer Detection
- 12:15-13:00 — Vinit Kumar Singh, Indian Institute of Technology and Brenda Rubenstein, Brown University, USA: Quantum Neural Networks for Analyzing X-Ray Scattering Data
Wed, Oct 14 — Variational Techniques
- 14:30-15:15 — Zak Webb, Oak Ridge National Laboratory (ORNL), USA: On the Universality of the Variational Quantum Eigensolver Framework
- 14:30-15:15 — Slimane Thabet and Jean-Francois Hullo, EDF Energy
Hove, UK: Spectral Embedding of Graphs Using Quantum Variational Circuits
Thu, Oct 15 — Quantum Optimization 1
- 10:00-10:45 — Sara Ayman Metwalli, Francois Le Gall and Rodney Van Meter, Keio University and Nagoya University, Japan: A Practical Quantum Approach to the k-clique Problem
- 10:00-10:45 — Rebekah Herrman, Phillip Lotshaw, James Ostrowski and Travis Humble, University of Tennessee Knoxville and Oak Ridge National Laboratory (ORNL), USA: Graph Coloring, Circuit Depth, & Optimality in QAOA
Thu, Oct 15 — Quantum Optimization 2
- 12:15-13:00 — Matias Jonsson, Jason Larkin and Gian Guerreschi, Carnegie Mellon University and Intel Labs, USA: Assessment of Alternative Objective Functions for Quantum Variational Combinatorial Optimization
- 12:15-13:00 — Alex Fischer and Don Towsley, University of Massachusetts, Amherst: Distributing Graph States Across Quantum Networks
Fri, Oct 16 — Quantum Simulation 1
- 10:00-10:45 — Megan Lilly and Travis Humble, University of Tennessee, Knoxville and Oak Ridge National Laboratory (ORNL), USA: Evaluating Performance of Quantum Computers with Cycle Benchmarking
- 10:00-10:45 — Paul Kairys and Travis Humble, University of Tennessee and Oak Ridge National Laboratory (ORNL), USA: High performance digital quantum simulation through analog control optimization
Fri, Oct 16 — Quantum Simulation 2
- 12:15-13:00 — Teik Guan Tan and Jianying Zhou, Singapore University of Technology and Design (SUTD): Quantum Interpreted Circuits (QuIC): Rapidly Simulating Quantum Algorithms
- 12:15-13:00 — Andrea Delgado and Travis Humble, Oak Ridge National Laboratory (ORNL), Lauren Ice, Gregory Quiroz, Johns Hopkins University, Jim Kowalkowski, Stephen Mrenna, Fermi National Accelerator Laboratory (FNAL): Quantum Algorithms for Event Reconstruction and Simulation in High Energy Physics Experiments
QuVis: A Quantum Circuit Visualization Tool for Novices
Milan Williams, Elisa Zhao Hang, Adinawa Adjagbodjou, Robert Krueger and Johanna Beyer: Harvard University, Cambridge, USA
Abstract: QuVis is a novel educational platform for quantum circuit composition, exploration, and analysis for novices. The rapid growth of the quantum computing field surpasses the educational capacity of existing systems, which makes novices particularly in need of accessible tools. Often, novices struggle to understand a fundamental concept: how quantum gates affect circuit state probabilities over time. QuVis contributes two novel visualizations to develop this intuition. One visualization teaches users about the relationship between gates and single qubit probabilities. This feature integrates stacked bar charts into the traditional quantum circuit diagram to display the probability distribution of a single qubit after each gate application. The second visualization provides insight into the intermediate steps that contribute to the final state probability distribution. The combination of a radial bar chart and a time slider helps users see the probability distribution of the entire quantum circuit at a user-specified execution step. Based on preliminary user testing, our results showed that QuVis users created quantum circuits faster and found the platform more intuitive than a comparable tool.
Suppressing Decoherence in Quantum Systems with Unitary Operations
Alena Mastiukova, Evgeniy Kiktenko and Aleksey Fedorov: Russian Quantum Center and Moscow Institute of Physics and Technology, Russia
Abstract: Decoherence is a serious obstacle to the implementation of large-scale and low-noise quantum computing devices. In the present works, we investigate the role of the fidelity of finite-dimensional quantum systems in the context of their robustness to decoherence. We suggest an approach for suppressing errors by employing pre-processing and post-processing unitary operations, which precede and follow the action of a decoherence channel. We consider the case of decoherence channels acting on a single qubit belonging to a many-qubit state. Preprocessing and postprocessing operators can be either individual, which is acting on the qubit effected by the decoherence channel only, or collective, which is acting on the whole multiqubit state. We give a classification of possible strategies for the protection scheme, analyze them, and derive expressions for the optimal unitary operators providing the maximal value of the fidelity regarding initial and final states. We then consider the realization of our approach for the basic decoherence models, which include single-qubit depolarizing, dephasing, and amplitude damping channels. We also demonstrate that the decoherence robustness of multiqubit states for these decoherence models is determined by the entropy of the reduced state of the qubit undergoing the decoherence channel.
Practical Quantum Computing: The Value of Local Computation
James Cruise, Neil Gillespie and Brendan Reid: Riverlane, Cambridge, UK
Abstract: As we move towards the era of quantum computing we need to better understand the bottlenecks in the classical computing support hardware and how to mitigate these to fully utilise the available qubits. In this poster we discuss three key bottlenecks in near term quantum computers; bandwidth restrictions from transferring data from the CPU to QPU; latency delays in the classical communication channels for round-trip communication between the QPU and the CPU; and the timing restrictions driven by high error rates. In each case we consider a near term application to highlight the bottleneck: for bandwidth, randomized benchmarking; for latency, adaptive algorithms, to improve VQE; and for error rate, experimental error correction. In all three cases, we discuss why the bottleneck arises in the current computational paradigm of carrying out all the classical computation on the CPU.
These bottlenecks can be mitigated by providing access to available local classical computational resources in the QPU. Currently, this takes the form of an FPGA which is used to control the qubit hardware. In all three cases, we show how local computation can be used to remove the bottleneck.
Defence Applications of Quantum Computing
Saasha Joshi: Panjab University, Chandigarh, India
Abstract: Quantum Computing has the potential to bring a paradigm shift in defence technology by creating a significant impact on future military capabilities. Undoubtedly, defence and national security are among the first domains to adopt the emerging quantum technologies such as quantum radars, quantum network sensors for detection of stealth weaponry, and submarine navigation. The presented poster, with particular focus on quantum satellites, provides an overview of the current state of research, future advances, and challenges that can be encountered in quantum communication technology. It highlights the use of an entanglement-based Quantum Key Distribution (QKD) to provide methods for long-range satellite-to-ground and inter-satellite secure quantum communication for the military advantage. This technology of QKD can also be leveraged to build a denser network of quantum satellites that could provide a means for secure and wireless communication, therefore, helping with the establishment of a space-based quantum internet. Further advancements with such QKD technology have far-reaching effects for military capabilities, missile defence system, intelligent services, and law-enforcement agencies. The poster aims to analyze such quantum technologies that could provide enhanced functionality to impact warfare systems concerned with communication, sensing, and even artificial intelligence.
A Unified Software Control System for Open-access Trapped Ion Quantum Computers
Virginia Frey, Richard Rademacher, Noah Greenberg, Nikolay Videnov, Matthew Day, Crystal Senko and Rajibul Islam: University of Waterloo, Canada
Abstract: Here we present the software control system of the QuantumION platform: an open-access, remotely accessible quantum computer based on trapped ions built at the Institute for Quantum Computing in Waterloo, Canada. In the past, programming of ion trap experiments by direct control of the components in a host language like Python has been used to control of the apparatus at a low level, but this approach becomes more cumbersome as experiments focus more on higher-level concepts like quantum gates and circuits, while more abstract frameworks, on the other hand, have lacked the low-level controls needed for fine-grained experiment control. We have designed a scalable, layered software system that allows for precise control at the gate- and circuit-level, as well as on lower hardware levels ranging from pulse design, arbitrary waveform control, and down to controlling individual trap parameters. Full control is available on the timing layer, including real-time decision making for e.g. error correction experiments. We present a conceptual overview of these abstraction layers and showcase example experiments and quantum operations that users working on these different layers can utilize. This pristine level of access that our platform provides is the first of its kind and we hope to contribute to advancing the entire field by opening our platform up to the broad research community.
A Unified Electronic Control System for Open-access Trapped Ion Quantum Computers
Richard Rademacher, Virginia Frey, Noah Greenberg, Nikolay Videnov, Matthew Day, Crystal Senko and Rajibul Islam: University of Waterloo, Canada
Abstract: Here we present the control system of the QuantumION platform: an open-access, remotely accessible quantum computer based on trapped ions built at the Institute for Quantum Computing in Waterloo, Canada. In the past, the (classical) control systems designed to operate small, closed-laboratory quantum computers have lacked a unified framework of both hardware and software to support the precision timing, flexible controls, scheduling, calibration, security etc. required by quantum computing experiments in a remote-access environment. To overcome these challenges, we have designed a fully custom, integrated control system framework to increase automation, improve timing precision, and provide new waveform generation techniques for the unique needs of quantum experiments. This system leverages a custom FPGA execution engine concept to provide massively parallel operations in order to facilitate sub-nanosecond pulse timing, photon counts, frequency control, and arbitrary waveform playback. In this way, the QuantumION control system is a full hardware/software ecosystem that supports a range of cutting-edge experiments including benchmarking, characterization, quantum simulation, and error-correcting codes.
Constructing a Scalable Trapped-ion Quantum Computer Demonstrator Device
Quentin Bodart, Foni R. Lebrun-Gallagher, Nicholas Johnson, Martin Siegele, Seokjun Hong, Sebastian Weidt and Winfried K. Hensinger: University of Sussex, UK
Abstract: The recent development of microfabricated ion traps offers a promising pathway towards the large-scale implementation of quantum information processing technologies. However, achieving fault-tolerant quantum computation requires the coherent manipulation and storage of millions of ions. Thus, accessing large numbers of storage and gate locations is a major challenge to ensure scalability. Our architecture envisions a surface array of ion traps constructed from multiple identical modules. On-chip zones are dedicated to ion loading, storage and microwave based quantum logic gates. As an intermediate step towards this scalable architecture, we are developing a two-module ion trap processor demonstrating the key methods required to realise a scalable trapped-ion quantum computer: combining microwave and radiofrequency fields and a magnetic field gradient for quantum logic operations, a piezo positioning system for close chip alignment, ion shuttling between chips, and a modular cryogenic cooling system.
Engineering a Scalable Logical Qubit in a 2D Surface Ion Trap Array
Samuel Hile, Alex Owens, David Bretaud, Raphael Lebrun, Martin Siegele, Seokjun Hong, Reuben Puddy, Sebastian Weidt and Winfried Hensinger: University of Sussex, UK
Abstract: Trapped ion qubits achieve excellent coherence times and gate fidelities, well below the threshold for fault tolerant quantum error correction. A key challenge now is to scale ion quantum processors to the large number of qubits required for error corrected algorithms to run. We present progress on the implementation of a high-fidelity logical qubit in a modular architecture designed for true scalability, based on robust microwave-driven quantum gates and physical shuttling of ion qubits. We show an X-junction surface ion trap, made using industrial silicon microfabrication techniques. The 4-arm geometry permits dedicated zones, optimized for ion loading, memory, entangling interactions and readout. Qubits are defined in the internal hyperfine states of the 171Yb+ ion, and entanglement is generated by modulating the Coulomb interaction between ion pairs using direct microwave and RF drive signals in a dynamically adjustable magnetic field gradient. We outline recent verification of ion transport and reconfiguration operations in our X-junction ion trap, and the protocols required to operate and characterize an error-corrected logical qubit within the architecture.
Towards High-fidelity Logical Gates with Trapped Ion Qubits
Tomas Navickas, Mitchell Peaks, Chris Knapp, Christophe Valahu, Foni R. Lebrun-Gallagher, Martin Siegele, Reuben K. Puddy, Seokjun Hong, David F. Murgia, Eamon D. Standing, Adam M. Lawrence, Zak D. Romaszko, Sebastian Weidt and Winfried K. Hensinger: University of Sussex and Imperial College, London, and Universal Quantum, UK
Abstract: Trapped ion qubit state manipulation with microwave radiation provides an enticing promise of low-error state preparation and readout while using readily-accessible electronic control systems. The caveat is the requirement of a significant static or an oscillating magnetic field to significantly couple the ion electronic and motional states. We report on the latest progress on our experimental system, which incorporates a set of SmCo magnets under a microfabricated ion trap. The measured magnetic field gradient is ~4 times larger compared to an equivalent setup. We outline the expected sources of error in single and multi-qubit operations, and technical requirements to demonstrate fidelities of >99%.
Enabling Global Connectivity in a Shuttling Based Trapped Ion Quantum Computer with Efficient Routing
Mark Webber, Steven Herbert, Sebastian Weidt and Winfried Hensinger: University of Sussex and University of Oxford, UK
Abstract: The cost of enabling connectivity in near term quantum computers is an important factor in determining the achievable depth for a given circuit and therefore computational power. We have created a routing algorithm that enables efficient global connectivity in a previously proposed trapped ion quantum computing architecture. The routing algorithm was characterized against a strict lower bound and also against an alternative routing procedure which utilised positional swaps. The results of simulating the routing problem were then combined with a proposed error model, which was used to estimate the achievable circuit depth and quantum volume of the device as a function of experimental parameters. A new metric based on quantum volume, but with native two-qubit gates is proposed and used to assess the cost of connectivity relative to the upper bound of free, all to all connectivity. The new metric is also used to assess a square-grid superconducting device. For the shuttling parameters used, the trapped ion design has a substantially lower cost associated with connectivity as compared to the square-grid superconducting device.
Open Source Quantum Code Compilation for Scalable Trapped Ion Quantum Processors
David Bretaud, Samuel Hile, Alexander Owens, Daisy Smith, Sebastian Weidt, Florian Mintert and Winfried Hensinger: University of Sussex and Imperial College, London, UK
Abstract: Constructing and optimizing the key physical elements to build a quantum computer is currently a clear priority in academia. Many labs are aiming for sub-threshold quantum error correction as the final enabling step for large scale quantum computation. The recent emergence of NISQ devices has uncovered additional software and hardware challenges in the process of transforming a quantum algorithm into physical control pulses. Most quantum processing units (QPU) require a clear formulation of quantum circuits for a given algorithm, and also physical mapping of gate operations to the relevant signal emitters. Quantum charged coupled devices (QCCD) add an additional intermediary level of abstraction, with global connectivity achieved by ion shuttling across microfabricated traps. A new syntax combining quantum circuit elements and shuttling operations is presented for easier development of compilation tools. As an application, the syntax is included inside a full stack solution built on open source frameworks and designed to run a QPU prototype developed at the Ion Quantum Technology group in Sussex. The prototype is a X-junction trap aiming to implement microwave based entangling gates with magnetic field gradient generated by microfabricated current wires. Comparisons with existing frameworks and early experimental results are presented.
Sinara: An Open Hardware Ecosystem for Quantum
David Allcock, Chris Ballance, Sébastien Bourdeauducq, Joseph Britton, Michal Gaska, Thomas Harty, Jakub Jarosinski, Robert Jördens, Paweł Kulik, David Nadlinger, Krzysztof Pozniak, Tomasz Przywozki, Daniel Slichter, Mikolaj Sowinski, Weida Zhang and Grzegorz Kasprowicz: The Sinara Collaboration, USA
Abstract: Sinara is a modular, open-source measurement and control hardware ecosystem dedicated to quantum applications that require deterministic high-resolution timing. It is based on industrial standards and consists of over 50 scalable card-based modules built to perform a variety of analog (dc to microwave) and digital input and output tasks with precision timing. The hardware is controlled and managed by the ARTIQ open-source software platform, which provides nanosecond timing resolution and sub-microsecond latency via a high-level programming language.
Low Noise Controllers for Ion-Trap Quantum Computers
Miguel Usach, Jon Kraft and Fintan Leamy: Analog Devices, Norwood, USA
Abstract: Ion-trap controllers require a mix of precision, fast response, ultra-small transients, and low noise and drift. While these specifications are desirable for any system, for this application, it is a requirement in order to accurately control the Qu-bits and mitigate the contributions of the controller to the system error. Ion-trap controllers represent a major challenge for digital to analog converters (DACs), requiring a fine balance among precision, noise, and drift. In addition, such a dynamic signal requires a large output bandwidth to accommodate the slew requirements, but not less important, a considerable current consumption to fulfil those requirements. The associate thermal dissipation, as the Johnson equation predicts, has an associated thermal noise, requiring high efficiency power regulators to minimize power loss, and reducing the heat dissipation, while reducing the tones at the switching frequency. The ultimate objective is to eliminate any kind of tones or spurious that could be present at the output signal that may potentially degrade the Qu-bits fidelity, or even worst, generate de-coherency. These requirements pose significant challenges to the electronic components and demand a deeper level of system understanding to properly resolve the different trade-offs, electronic capabilities, and Qu-bits needs.
Engineering Mid-Circuit Measurement
Dave Campagna and Tom Markham: Honeywell Quantum Solutions, USA
Abstract: What is mid-circuit measurement and what benefits does it provide in a quantum computer? This poster provides a system engineering perspective. First, it introduces three circuits using mid circuit measurement to serve as use cases. 1) Error correction is made possible by measuring the state of one or more qubits and using them to modify the state of other qubits. 2) Qubit reuse measures the state of a qubit of interest then re-initializes that qubit so that it participates in the downstream circuit. 3) Qubit fan-out measures 1 qubit and uses the result to initialize one or more qubits. Having established the value of mid-circuit measurement, the corresponding system requirements and goals are then derived. The system must perform a measurement, execute decision and control logic then feed the result back into the circuit in a fraction of the decoherence time. We introduce a trapped ion architecture that meets the requirements. Finally, practical challenges in implementing scalable quantum computers with mid-circuit measurements are presented. Challenges include: 1) Developing algorithms that leverage mid-circuit measurements. 2) Performing measurement, decision and control in a fraction of the decoherence time. 3) Effectively pipelining instructions when the branch prediction is unknown.
Cryotronics Test Chamber
Ryan Daniel: Honeywell Quantum Solutions, USA
Abstract: How can you predict the performance of electronics in a 4 Kelvin environment under ultra-high vacuum? Honeywell Quantum Solutions’ quantum computer operates with many of its electronics outside the vacuum chamber. However, moving some of the external electronic components closer to the heart of the quantum computer, the ion trap, could lead to many benefits like improving signal-to-noise performance and reducing cable throughput. With limited research available, the best method to determine cryogenic electronics performance is by experimental tests. To efficiently test new devices, HQS created a cryotronics test chamber to allow for quick turnaround experiments. The test stand was designed to have an abundance of I/Os, including hermetically sealed feedthroughs for power, coaxial, and optical access. In addition, custom feedthroughs were implemented to heat sink and allow signals to pass through the 80 K radiation shield while keeping a light-tight seal to reduce heat load on the 4 K stage. The HQS cryotronic test chamber will facilitate future cryogenic electronics development ultimately leading to a more efficient quantum computer.
Automated Testing Methods of Surface Ion Traps in Quantum Computing
Ryan Jacobs, Ben Spaun, Jeremy Parks, David Liefer, and Charlie Dosdall: Honeywell Quantum Solutions, USA
Abstract: Due to their demonstrated long internal state coherence times, high fidelity gate operations, and fundamentally identical internal structures; qubits created out of trapped ions provide an ideal platform for quantum computing. In this technology space surface electrode traps, created through standard CMOS microfabrication processing techniques, are of paramount importance as they create the electric fields which act as trapping potentials (colloquially “wells”) for the ion qubits. The commercial viability of trapped ion quantum computing systems, therefore, hinges on manufacturing a significant volume of complex trap chips with comprehensive ion transport functionality, through low noise connections to the surface electrodes. These volume and performance demands, coupled with the impracticality of debugging trap defects at the system level, necessitates automated, repeatable, and precise testing of the ion traps at the device level. We propose a custom designed trap tester to measure the electrical characteristics of ion traps. We detail the use cases, implementation, and calibration of the trap tester on internally fabricated ion traps. The trap tester provides a scalable way to decrease test time, improve accuracy, and increase reliability of microfabricated ion traps. The paper closes with a discussion of extending the trap tester to other components of trapped ion quantum computers.
VQE Approaches for Quantum Chemistry in XACC
Daniel Claudino, Jerimiah Wright, Alexander McCaskey, Dmitry Lyakh and Travis Humble: Oak Ridge National Laboratory (ORNL), USA
Abstract: Quantum simulations of quantum chemistry are seen as one of the most immediate applications of quantum computing. While current quantum devices are not yet mature enough to cope with all the demands involved in these simulations in a timely and fault-tolerant fashion, hybrid quantum-classical algorithms such as the variational quantum eigensolver (VQE) will remain a valuable compromise for the purposes of quantum chemistry studies. The eXtreme ACCelerator is a flexible framework aimed at heterogeneous CPU-QPU computations (possibly involving GPUs) based on a hardware-agnostic paradigm that can leverage HPC clusters in conjunction with a variety of backends, being able to target simulators and local and vendor QPUs on an equal footing. Here we present some of the latest developments in extending the scope of XACC in terms of quantum chemical simulations, precisely the implementation of the ADAPT-VQE and related components relevant to efficient circuit optimization. The performance of the algorithm and a comparison of optimization strategies currently implemented in XACC are illustrated in the context on potential energy curves describing the dissociation of alkali metal hydrides.
Problem Solving in the NISQ Era: Exploring Hybrid Quantum-Classical Approaches
Prashanti Priya Angara: University of Victoria, Canada
Abstract: In our research, we aim to (1) develop a toolkit to identify quantum hybrid problems; (2) explore hybrid quantum problem solving and algorithm design techniques; and (3) realize tool integration platforms for specific application domains. In the current NISQ (Noisy Intermediate Scale Quantum) era, using quantum-classical hybrid approaches is a way to overcome some of the limitations of NISQ devices; such approaches range from developing quantum-classical hybrid algorithms to having architectures that support switching effectively between classical and quantum computing nodes. This poster highlights some of our research in studying hybrid quantum-classical algorithms and architectures. We look at some significant results in the areas of quantum algorithms, quantum machine learning and software architectures, which support hybrid quantum-classical computing. We also characterize some quantum building blocks (such as powerful quantum routines) that could be used when designing with hybrid quantum-classical algorithms.
Implementing a Novel Quantum K-Nearest Neighbors Learning Algorithm for Breast Cancer Detection
Siddharth Sharma: Stanford University, USA
Abstract: Machine learning and quantum computing are two technologies which are causing a paradigm shift in the performance and the behavior of certain algorithms, achieving previously unattainable results. While current machine learning classifiers like the Support Vector Machine are seeing gradual improvements in performance, there are still severe limitations on the efficiency and scalability of such algorithms due to a limited feature space which makes the kernel functions computationally expensive to estimate. By integrating quantum circuits into traditional Machine Learning, we may solve this problem through the use of a quantum feature space, a technique which improves existing Machine Learning algorithms through the use of parallelization and the reduction of the storage space from exponential to linear. This research expands on this concept of the Hilbert space and applies it for classical machine learning by implementing the quantum-enhanced version of the K-nearest neighbors’ algorithm (an existing lazy-learning deterministic classifier). The primary experiment of this research is to build a noisy variational quantum circuit KNN (QKNN) which mimics the classification methods of a traditional K-nearest neighbors’ classifier. The QKNN utilizes the distance metric of Hamming Distance and is able to outperform the existing KNN on a 10- dimensional Breast Cancer dataset.
Quantum Neural Networks for Analyzing X-Ray Scattering Data
Vinit Kumar Singh: Indian Institute of Technology, Kharagpur, India and Brenda Rubenstein: Brown University, USA
Abstract: Determining the chemical structures of molecules from scattering cross-sections has been a long-standing problem in chemistry. To date, it is accomplished by performing massive molecular dynamics simulations. To avoid such calculations, researchers have developed Classical Machine Learning (CML) methods. But, the sheer complexity of the problem requires vast Neural Networks and enormous datasets. Quantum Machine Learning (QML) on NISQ computers has shown great potential in dealing with such issues while using exponentially smaller resources. Here, we demonstrate the operation of simple Quantum Neural Network (QNN) architectures that can provide insights into the dataset by using just a few qubits and small sample sizes. We explore various Parameterized Quantum Circuits (PQC), state preparation schemes, and circuit decomposition algorithms. Finally, we present a comparison between the QML and CML approaches.
On the Universality of the Variational Quantum Eigensolver Framework
Zak Webb: Oak Ridge National Laboratory (ORNL), USA
Abstract: Hybrid quantum algorithms are a framework in which a quantum process parameterized by classical values is run on a given input. The output of the quantum algorithm is then run through a classical optimizer circuit, which updates the parameters of the quantum circuit. This process is repeated until some limiting behavior of the quantum circuit is achieved, and the framework output some function of the measure quantum output. In this work we show that if the parameterized quantum circuit is sufﬁciently general, then a speciﬁc type of hybrid quantum algorithm known as variational quantum eigensolvers are as powerful as a general quantum computer. In particular, we show how to encode the evolution of a general quantum circuit in the optimal values of a particular variational quantum eigensolver circuit. Moreover, we also show that the classical optimizing circuit is guaranteed to ﬁnd the optimal set of parameters.
Spectral Embedding of Graphs Using Quantum Variational Circuits
Slimane Thabet and Jean-Francois Hullo: EDF Energy, Hove, UK
Abstract: With the development of quantum algorithms, high-cost computations are being scrutinized in the hope of a quantum advantage. While graphs offer a convenient framework for multiple real-world problems, their analytics still comes with high computation and space. By mapping the graph data into a low dimensional space, in which graph structural information is preserved, the eigenvectors of the Laplacian matrix constitute a powerful node embedding, called Laplacian Eigenmaps. Computing these embeddings is on its own an expensive task knowing that using specific sparse methods, the eigendecomposition of a Laplacian matrix has a cost of O(r.n2), r being the ratio of nonzero elements. We propose a method to compute a Laplacian Eigenmap using a quantum variational circuit. The idea of our algorithm is to reach the eigenstates of the laplacian matrix, which can be considered as a hamiltonian operator, by adapting the variational quantum eigensolver algorithm. By estimating the d-first eigenvectors of the Laplacian at the same time, our algorithm directly generates a d-dimension quantum embedding of the graph. We implemented our algorithm on a quantum simulator, demonstrating on 32 vertices graphs the ability of the algorithm to produce structure preserving embeddings. Furthermore, we illustrate how this procedure can be used to encode graph data for a future perspective to use it with other quantum data for machine learning tasks such as clustering and classification. Although the mathematical properties of this approximate approach are not fully understood, this algorithm opens perspectives for the speed-up of graph pre-processing using NISQ.
A Practical Quantum Approach to the k-clique Problem
Sara Ayman Metwalli, Keio University, Francois Le Gall: Nagoya University, and Rodney Van Meter: Keio University, Japan
Abstract: Algorithms for the k-clique problem in general, and the triangle-finding the smallest nontrivial instance of the k-clique problem, have been proposed for quantum computers. Still, those algorithms assume the use of fixed access time quantum RAM (QRAM). We present a practical gate-based approach to both the triangle-finding problem and its NP-hard k-clique generalization. We examine both constant factors for near-term implementation on a Noisy Intermediate Scale Quantum computer (NISQ) device and the problem’s scaling to evaluate the long-term use of quantum computers. Our work aims to decrease the gap between theory and practice by presenting a robust implementation of the k-clique problem, in general, regaining the quantum advantage for more significant problems.
Graph Coloring, Circuit Depth, & Optimality in QAOA
Rebekah Herrman, James Ostrowski: University of Tennessee Knoxville, Phillip Lotshaw, and Travis Humble: Oak Ridge National Laboratory (ORNL), USA
Abstract: The quantum approximate optimization algorithm (QAOA) approximately solves combinatorial optimization problems within the context of near-term intermediate-scale (NISQ) quantum computers. While QAOA may be applied to a variety of combinatorial optimization problems, it is unclear which specific problem classes offer a computational advantage. The relationship between properties of a problem instance and the resulting circuit depth required to implement QAOA is an important measure of this complexity. As error in NISQ devices increases exponentially with circuit depth, identifying lower bounds on circuit depth can provide insights into when quantum advantage is feasible. Here we identify how problem structure may yield lower bounds on the circuit depth for a QAOA iteration, and we examine the specific relationship between problem structure and the circuit depth for a variety of combinatorial optimization problems including MaxCut. We use numerical simulation and analytical optimization methods to evaluate the distribution of QAOA optimality gaps for graphs of varying structures, i.e., how close QAOA gets to the optimal solution. These results connect graph theoretic properties of the problem instances to the instances of combinatorial optimization that are ideally suited for QAOA approaches and those that are not.
Assessment of Alternative Objective Functions for Quantum Variational Combinatorial Optimization
Matias Jonsson, Jason Larkin: Carnegie Mellon University, and Gian Guerreschi: Intel Labs, USA
Abstract: Quantum variational combinatorial optimization is often performed with average energy as the objective function guiding the classical optimizer. However, recent work suggests that an objective function based on additional properties of the distribution can guide the optimization to different and possibly better minima. We assess the ability of these objective functions to improve performance for the combinatorial optimization problem MAXCUT, with numerical experiments ranging over several graph types. We characterize the variational parameter landscape for the various objective functions and show increased regions of gradient in the parameter space, suggesting a higher chance of convergence. By comparing the Cumulative Distribution Function of cut values, we characterize a way in which parameters optimized with a different objective function can yield better distributions.
Distributing Graph States Across Quantum Networks
Alex Fischer and Don Towsley: University of Massachusetts, Amherst, USA
Abstract: Graph states are an important class of multipartite entangled quantum states. We propose a new approach for distributing graph states across a quantum network. We consider a quantum network consisting of nodes—quantum computers within which local operations are free—and EPR pairs shared between nodes that can continually be generated. We prove upper bounds for our approach on the number of EPR pairs consumed, number of timesteps taken, and amount of classical communication required, all of which are equal to or better than that of prior work. We also reduce the problem of minimizing the number of timesteps taken to distribute a graph state using our approach to a network flow problem having polynomial time complexity.
Evaluating Performance of Quantum Computers with Cycle Benchmarking
Megan Lilly and Travis Humble: University of Tennessee, Knoxville and Oak Ridge National Laboratory (ORNL), USA
Abstract: The rapid growth of noisy, intermediate-scale quantum devices, or NISQ devices, has necessitated the development of methods to characterize and benchmark quantum hardware. Currently, most common methods such as quantum tomography processes or randomized benchmarking are not scalable solutions for benchmarking processes larger than a few qubits. Cycle benchmarking is a recently developed protocol that estimates the process fidelity of a noisy quantum process of up to tens of qubits. We demonstrate cycle benchmarking in experiments on superconducting transmon qubit registers. We verify the process fidelity calculation and test its consistency across multiple instances. We use cycle benchmarking to test a variety of quantum processes of varying sizes and gates and compare the process fidelity of these different operations. We report these metrics and determine their predictive capacity by comparing the process fidelity for similar quantum processes. Our results verify that cycle benchmarking is an effective method for benchmarking quantum processes of register size prevalent in the NISQ era.
High Performance Digital Quantum Simulation Through Analog Control Optimization
Paul Kairys and Travis Humble: University of Tennessee and Oak Ridge National Laboratory (ORNL), USA
Abstract: Analog quantum simulation is highly efficient but requires an application that is usually hardware specific, whereas digital quantum simulation is flexible but has low-performance due to abundant logical errors in NISQ devices. We propose a framework for high performance quantum simulation in devices that are closely related, but not exactly mappable, to the application instance, thereby expanding the class of applications NISQ hardware can address reliably. This framework relies on digitizing the time evolution of an application Hamiltonian and using quantum optimal control methods to compile hardware level instructions to implement the digitized evolution. We discuss the various quantum control methods to determine these instructions and we propose to benchmark this framework by simulating dynamics of the Bose-Hubbard model on circuit-QED device architectures. We intend to compare these results to state of the art digital and analog quantum simulations of the same model.
Quantum Interpreted Circuits (QuIC): Rapidly Simulating Quantum Algorithms
Teik Guan Tan and Jianying Zhou: Singapore University of Technology and Design (SUTD), Singapore
Abstract: QuIC (pronounced ”quick”) is a Quantum interpreter and simulator whose design was inspired by Terry Rudolph’s “Q is for Quantum”. The objective for developing QuIC is to promote the understanding of basic Quantum circuits through a hands-on, responsive approach. QuIC is targeted at Computer-Science users wanting to realize Quantum algorithms quickly and conveniently, without needing to consider the underlying physical structure of the Quantum computer. QuIC is written in C POSIX and currently runs as a standalone command-line tool that takes in an ordered list of text strings to represent the gates that operate on the qubits sequentially.
Quantum Algorithms for Event Reconstruction and Simulation in High Energy Physics Experiments
Andrea Delgado and Travis Humble: Oak Ridge National Laboratory (ORNL), Lauren Ice, Gregory Quiroz: Johns Hopkins University, Jim Kowalkowski, Stephen Mrenna: Fermi National Accelerator Laboratory, USA
Abstract: The amount of data currently generated by particle physics experiments presents a challenge to conventional information technologies. The high-energy physics (HEP) community takes and classifies an incredible amount of data for processing with extreme precision, all of which require enormous computing power. Therefore, innovative computing technologies are vital to the HEP community for continuing the search to understand our universe’s fundamental behavior. Quantum computing holds the promise of substantially speeding up computationally challenging tasks such as interpreting the electronic signals produced in a HEP experiment’s detector to determine what original particles passed through, as well as their characteristics. This process is often referred to as event reconstruction. However, we must improve our understanding of quantum computers, how quantum algorithms can be used within the HEP context, and when they can outperform their classical counterparts. In this exploratory work, we investigate the possibility of making HEP event reconstruction and simulation more efficient by using quantum-based algorithms.