TOPQC 2019







An International Advanced Research Workshop


June 10-12, 2019, Cetraro, Italy










The next generation of techniques for quantum computing in continuous time


Nicholas Chancellor

Department of Physics, Durham University, Durham, UK


Currently quantum machines for computing in continuous time, in particular quantum annealers, represent the most technologically mature quantum information processing devices. As continuous time devices continue to mature, the time has come to think about new techniques so that the potential of real world applications can be realized as soon as possible. I will discuss what this next generation of techniques may look like. In particular, they are likely to include hybrid quantum classical algorithmic tools, like reverse annealing which will bring with it more sophisticated algorithms. While on one hand algorithms are likely to become more complicated, on the other hand, the end goals of these new techniques are likely not to be simply to reach the most optimal solution of a problem faster, but to meet a whole host of other more complicated and subtle criteria. As an example I summarize a recent experimental study I have carried out on how reverse annealing along with other advanced features on the quantum annealers produced by D-Wave Systems Inc. can be used along with an initial guess to search for solutions which are not more optimal, but rather more robust. I will also quickly summarize how this study has lead to a more efficient encoding of integer variables in quantum annealing. Finally, in closing, I will discuss the bigger picture of future continuous time algorithms on machines which could be more coherent than the devices of today, and in particular how multiple quantum mechanisms may work together to solve problems.

Benchmarking NISQ-era quantum processors


Jerry Chow

Experimental Quantum Computing IBM, New York, NY, USA


As the field marches towards quantum advantage with near-term quantum processors, it becomes imperative to characterize, verify, and validate performance. An outstanding scientific challenge in the community is a scalable  set of metrics or experiments which can shed light on the usability of a device for near-term algorithms. We propose a device-independent metric called the quantum volume and use it to characterize recent systems built at IBM. Moreover, it becomes critical to explore techniques to extend the computational reach of noisy systems, be it through understanding underlying physics, or more efficient circuit compilation.

Advances in Quantum Annealing


Edward Dahl

D-Wave Research, Canada


Users have programmed a variety of optimization, machine learning and material simulation applications on the four generations of D-Wave's Chimera architecture.  Their feedback guides D-Wave's development of new and practical advances in the annealing architecture.  Features either recently introduced or planned for the near future include advanced annealing controls, lower noise QPU, Pegasus topology and non-stoquastic couplers.  We describe these features and give use cases for each.

Simulation of universal quantum computers


Hans De Raedt

Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4,

NL-9747 AG Groningen, The Netherlands


First, I review the features of the massively parallel simulator which has been used to simulate ideal universal quantum computers up to 48 qubits [1] and physical models of transmon qubits in contact with a heat bath [2].  Then, I address the fundamental issue of quantum measurements. I show how discrete-event simulation [3] allows to circumvent the quantum measurement problem and permits the simulation the outcome of real “quantum” experiments on an event-by-event basis. Finally, I compare discrete-event simulations results with experimental results produced by the IBM Q processor [4]


1.      H. De Raedt, F. Jin, D. Willsch, M. Willsch, N. Yoshioka, N. Ito, S. Yuan, K. Michielsen. “Massively parallel quantum computer simulator, eleven years later”, 237, 47-61 (2019).

2.      D. Willsch, M. Willsch, F. Jin, H. De Raedt, and K. Michielsen, “Testing quantum fault tolerance on small systems” Phys. Rev. A 98, 052348 (2018).

3.      K. Michielsen, K. De Raedt, and H. De Raedt, “Simulation of Quantum Computation: A deterministic event-based approach”, J. Comp. Theor. Nanoscience 2, 227 - 239 (2005).

4.     K. Michielsen, M. Nocon, D. Willsch, F. Jin, Th. Lippert, and H. De Raedt, “Benchmarking gate-based quantum computers”, Comp. Phys. Comm. 220, 44-55 (2017).

Quantum Architecture

Runyao Duan

BAIDU Quantum Computing Institute, Beijing, CHINA


Quantum Architecture aims to provide a platform to transform “Quantum infrastructure as a Service (QaaS)” into reality. It plays a crucial role in connecting classical programming and quantum hardware, and will actually implement quantum algorithms and quantum AI applications. With the recent progresses on quantum algorithms and technologies in experimental quantum computing, the need of researching quantum architecture has become very urgent. In this talk, we will outline key challenges in this field from the following five topicsUnified programming interface, Quantum hardware interface, Distributed information processing, Quantum network and internet, and Quantum & post-quantum crypto. Finally we will briefly introduce Baidu Quantum Program (BQP) to show our strategies in tackling these challenges.

Quantum, Beyond Computing


Robert Ewald

Cold Quanta, USA


This talk surveys some possible applications of quantum mechanics in three areas beyond quantum computing:


·       Quantum positioning, navigation and timing where it may be possible to create portable devices to give very accurate GPS-like timing and location information without the use of GPS satellites.

·       Quantum communications which may enable a new form of secure communication

·       Quantum sensing which could lead to more accurate successors to a collection of new devices like airport scanners and eventually radar.

Closing loopholes for a quantum advantage with short-time Hamiltonian dynamics


Jonas Haferkamp

nstitut fur Theoretische Physic, Freie Universitat, Berlin, Germany


"A near-term goal in quantum computation and simulation is to realize a quantum device showing a quantum computational advantage (or “supremacy”).

This refers to performing a quantum experiment whose outcome cannot be efficiently predicted on a classical computer. Candidate quantum devices for this task include boson samplers and Google-AI’s random circuit sampling. These proposals identify simple quantum tasks for which there is strong (complexity-theoretic) evidence” that they cannot be efficiently classically simulated.


In this talk, we will consider and develop a recent quantum advantage proposal based on constant-time evolutions under translation-invariant, nearest-neighbour Hamiltonian [Bermejo-Vega et. al. PRX 2018]. We will prove two key complexity theoretic conjectures needed for this proposal: the anticoncentration of the expected output distributions, and the average-case hardness of exactly evaluating the output probabilities. Our average-case result builds upon recently developed techniques for random circuit sampling. Our anticoncentration result exploits and develops connections between random quantum processes, Hamiltonian gaps, approximate 2-designs and anticoncentration. Specifically, we prove anticoncentration by showing that translation-invariant, constant-time Hamiltonian evolution in 2D forms approximate 2-designs in a specific sense. Our work brings the strongest complexity theoretic evidence to date that simple quantum simulations can provide a quantum advantage.

Paths to Supremacy with Quantum Annealers


Itay Hen

University of Southern California, Information Sciences Institute,

Los Angeles, CA, USA


Experimental large-scale quantum annealers are already starting to show increasingly enhanced capabilities on the verge of exhibiting true quantum supremacy.

I will discuss the current paths to achieving the much coveted and yet very elusive first demonstration of quantum speedup with these devices as well as the remaining obstacles towards that goal.

Establishing the Quantum Supremacy Frontier using qFlex


Salvatore Mandra

NASA Ames Research Center, Quantum Artificial Intelligence Lab (QuAIL),

Moffet Field, CA, USA


In the race to show quantum advantage, early Noisy Intermediate-Scale Quantum (NISQ) devices must be compared to the state-of-the-art classical technology currently available. To this end, in collaboration with Google and ORNL, we have developed qFlex, an high-performance simulator for random quantum circuits (RQCs) able to reach a sustained 281 PFlop/s on Summit, the fastest supercomputer in the world. In my talk, I will present our latest numerical simulations of large RQCs, including simulations of the Google Bristlecone of 72 qubits.

Using quantum computers to simulate molecules and solids


Michael Marthaler

HQS Quantum Simulations, Karlsruhe, KIT – Karlsruhe Institut fur Technologie, Karlsruhe, Germany


Quantum computers offer tantalizing possibilities, but are currently strongly limited by their intrinsic sensitivity to errors. We discuss the prospects of using a near term processor containing 50 to 100 qubits to perform ab-initio simulations of materials. At present, the overhead for quantum error correction is so large that it cannot be implemented for near term quantum computers. This means applications have to be planned with the limitations imposed by errors in mind. Material simulations seam to be the most promising near term applications. We discuss how simulations would be performed on quantum computers and how this relates to existing methods in quantum chemistry.

Optimization with Quantum Computers: QAOA and Quantum Annealing


Kristel Michielsen

Institute for Advanced Simulation, Jülich Supercomputing Centre, Forschungszentrum Jülich, D-52425 Jülich, Germany


A quantum computer (QC) is a device that performs operations according to the rules of quantum theory. There are various types of QCs of which nowadays the two most important ones considered for practical realization are the gate-based QC and the quantum annealer (QA). Both types can be used for optimization, a task QCs can potentially perform better than conventional computers.


We present results of solving small 2-SAT problems, which have a unique ground state and a highly-degenerate first excited state,  by the quantum approximate optimization algorithm (QAOA) executed on a QC simulator and on an IBM Quantum Experience device with 16 qubits, and by QA on the D-Wave 2000Q QA with more than 2000 qubits.



K. Michielsen, M. Nocon, D. Willsch, F. Jin, T. Lippert, H. De Raedt, Benchmarking gate-based quantum computers, Comp. Phys. Comm. 220, 44 (2017)


D. Willsch, M. Nocon, F. Jin, H. De Raedt, K. Michielsen,  Gate error analysis in simulations of quantum computers with transmon qubits, Phys. Rev. A 96, 062302 (2017)


H. De Raedt, F. Jin, D. Willsch, M. Nocon, N. Yoshioka, N. Ito, S. Yuan, K. Michielsen, Massively parallel quantum computer simulator, eleven years later, Comp. Phys. Comm. 237, 47-61 (2019)


D. Willsch, M. Nocon, F. Jin, H. De Raedt, K. Michielsen, Testing quantum fault tolerance on small systems, Phys. Rev. A 98, 052348 (2018)


M. Willsch, D. Willsch, F. Jin, H. De Raedt, K. Michielsen, Benchmarking the Quantum Approximate Optimization Algorithm (in preparation)

Performance Analysis of Quantum Algorithms for Trapped Ion NISQ Hardware


Kevin Obenland

Senior Staff, MIT Lincoln Laboratory


Noisy Intermediate-Scale Quantum (NISQ) hardware will be available in the near future. Understanding the ability of this hardware to perform useful computations is an important problem. There are competing drivers in the design and use of these near-term quantum devices. A simple physical qubit architecture is easy to optimize and replicate to produce a device with a high-qubit count. However, simple qubits may require a large number of basic operations to implement the operations required in an algorithm. However, employing more complex qubit and gate designs may decrease the number of operations, but the more complex qubits and gates may be noisier. Additionally, many quantum algorithms achieve increased fidelity through repetition. Additional repetitions of a core circuit will decrease the algorithmic error, but will also accumulate error due to the noisy gates. Understanding how the tradeoffs generated because of these competing design drivers is the subject of this talk.

We focus our study on trapped ion hardware running the Quantum Signal Processing (QSP) algorithm. The QSP algorithm is an efficient method for solving problems such as Hamiltonian simulation. The efficiency of the algorithm results from its additive scaling as a function of algorithmic error and is therefore a good candidate for near-term quantum demonstrations. We evaluate the impact of different trapped-ion configurations through classical gate-level simulation of the physical hardware. We decompose the QSP algorithm into a sequence of gates native to the hardware. We use a defined error model for each of the physical gates and measure the impact of this error on the fidelity of the resulting computation.

Quantum Computing Is Here


Marco Pistoia

Quantum Computing Software, IBM Watson Research Center, Yorktown Heights, N.Y., USA


Quantum computing is the use of quantum mechanical phenomena, such as superposition and entanglement, to perform computation. A quantum computer is used to perform such computation, which can be implemented theoretically or physically. In a conventional computer, doubling the number of bits doubles its processing power. But thanks to entanglement, adding extra qubits to a quantum machine produces an exponential increase in its computational ability. For years, physicists, mathematicians and computer scientists have worked on building the theoretical foundation of quantum computing, along with the first simple quantum computers and classical simulators. Although current quantum computers are still in the so called NISQ era, where NISQ stands for Noisy Intermediate-Scale Quantum, experiments on quantum computers have proven that quantum computations are feasible and can lead to high-fidelity results with the application of error-mitigation techniques. In this keynote, I will present an overview of quantum computing. I will also discuss how quantum computing is a cross-disciplinary technology, allowing people with different skills and research interests to perform research and contribute to this exciting field.

Quantum Computing: Near-term Algorithms, Error Correction and Hybrid Computing


Avadh Saxena

Grup d’Informacio Quantica, Universitat Autonoma de Barcelona, Spain


The main focus of this talk will be on significant recent efforts devoted to quantum computing at Los Alamos involving both gate-based quantum computing and using a quantum computer as an annealer for optimization problems.  Novel quantum simulation algorithms, quantum machine learning as well as hybrid quantum-classical computing techniques and error correcting codes to tame defects are being developed for near-term quantum computers.  These important advances address a variety of real problems such as those involving linear solvers, differential equations, sampling, graph partitioning, efficient combinatorial optimization, many-body physics, quantum chemistry, motif learning, stochastic applications, among others.  Fundamental aspects, e.g. entanglement (spectroscopy) and decoherence will also be discussed. In addition, I will touch upon some aspects of hardware, e.g. superconducting qubits vs trapped-ion qubits, etc.  Finally, quantum computing workforce is being developed via Quantum Summer Schools at Los Alamos.

Trapped neutral atoms and linear optics platforms for quantum computing


M.Yu. Saygin, S.S. Straupe, I.V. Dyakonov, A.A. Kalinkin, S.P. Kulik

Quantum Technology Centre, Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russia


Nowadays, several physical platforms are considered perspective for practical realization of quantum computing algorithms. This talk is devoted to two experimental approaches to building a quantum computer:  computation using trapped neutral atoms and linear-optical quantum computation, both of which are being developed in the recently established Quantum Technology Centre at Moscow State University.


Neutral atoms can be the carriers of qubits, that are well isolated from the environment being placed in specifically tailored microtraps created by a laser. Nowadays, reliable methods for arranging atoms into regular individually addressable arrays and high-fidelity quantum operations have been devised and demonstrated. The budget of the number of physical qubits conveniently operated on amounts to several thousand, making the system a strong contender in the field of applied quantum computing.


Linear optical quantum computation derives its power from photon interference. In addition, it leverages ways to encode qubit states into the multiple photon’s degrees of freedom and perform quantum logic operations with conventional optical elements. In turn, many of the optical elements are used already in the classical fields of telecom and computing, yet their improvements are usually necessary to perform a quantum task.


In this talk, we will review recent experimental achievements in these two systems, and we will outline their perspectives for the development of practical quantum computing devices.

Applications for Quantum Computing


Damian Steiger

Microsoft, USA


Leveraging quantum superposition and entanglement, the capabilities of quantum computers can exceed those of their classical counterparts and provide a route to increasing computational power beyond Moore’s law. With the realization of small quantum computers, it is timely to identify important application problems that quantum computers could solve better than classical supercomputers. In this talk I will discuss how to identify and develop such candidate applications for quantum computers. Moreover, I will give brief overview of Microsoft’s approach to quantum computing.

State of the Art and Challenges of Future Advanced Computing


Thomas Sterling

School of Informatics and Computing and CREST Center for Research in Extreme Scale Technologies, Indiana University, Bloomington, IN, USA


Since 1990 and over the last three decades high performance computing (HPC) has successfully and dramatically advance in performance through the exploitation of Moore’s Law and the exponential progress of semiconductor feature size, no entering the region of nanoscale. This culmination of technology and design improvements brings computing to the edge of exascale. Through the adoption of VLSI microprocessors in massive numbers, augmented by multi/many core processor (sockets) starting in 2005, and ultimately incorporating heterogeneity in the form of GPU accelerators in the last decade, the HPC industry has followed a proven formula for performance gain for real-world applications. However, this strategy is challenged by the end of Moore’s Law and Dennard scaling, flat-lining of clock rated due to power constraints, and the limited effectiveness of instruction level parallelism. While industry will eek out systems into the single digit exaflops regime at enormous cost, HPC will ultimately and soon begin to adopt revolutionary alternatives for future advanced computing. New concepts in the domains of non von Neumann architecture, neuromorphic computing, and quantum computing among others will replace the venerable multiprocessor and its six decade heritage. This presentation will describe in detail the state of the art of HPC as it is currently achieved and the fundamental challenges that must be overcome to aggressively progress across the trans-exascale performance regime. A specific example of one important possibility will be discussed demonstrating the opportunities of employing non von Neumann concepts. Questions and comments will be welcome throughout the presentation.

Quantum Computing for Aerospace Research and Beyond


Tobias Stollenwerk

German Aerospace Center (DLR), Simulation and Software Technology,

High Performance Computing Department, Linder Höhe Cologne, Germany


We will report on our investigation of the applicability of quantum computing to aerospace research and other problems. This includes planning and scheduling problems for quantum annealers as well as gate based quantum computers.

Hybrid quantum-classical computation and Clifford Magic circuits


Sergii Strelchuk

Department of Applied Mathematics and Theoretical Physics and Centre for Quantum Information and Foundations University of Cambridge, UK


Given the difficulty of building and controlling large numbers of qubits, early applications of quantum algorithms are likely to involve both quantum and classical ingredients. One of the most active areas of quantum computing investigates the fundamental limits of computation which could be carried out in these systems by studying the trade-off possibilities between classical and quantum resources. Inspired by a Pauli-based computing model by Bravyi et al. we will introduce a computational model of unitary Clifford circuits with solely magic state inputs (‘Clifford Magic’ circuits) supplemented by classical efficient computation, as well as an extended Gottesman-Knill theorem. I will further discuss the implications of achieving quantum advantage using Clifford Magic circuits and possible extensions to other gate sets. This talk is based on

Digital Annealer Technology for Large-Scale Combinatorial Optimization Problems


Kazuya Takemoto

Technology Development Project, Digital Annealer Unit, Fujitsu Laboratories Ltd., Kawasaki, Japan


Combinatorial optimization problems are decision-making problems in which the best among a given set of combinations must be found within a finite amount of time, based on various constraints. Many problems are ultimately combinatorial optimization problems, from everyday tasks like deciding a schedule or the shortest route in a navigation application, to large-scale social issues such as planning recovery after a disaster, eliminating traffic congestion, or investing in a financial asset. However, most combinatorial optimization problems belong to the class of problems called NP-hard, and for these problems, computing time to solve a problem increases exponentially with the size of the problem. Quantum annealing has been proposed as a method for solving combinatorial optimization problems quickly, and related research has been advancing actively in recent years. However, with real quantum annealing machine hardware, connections between quantum bits have a sparsely connected graph structure, and this greatly constrains the scale of problems that can be handled.

Given these conditions, Fujitsu has developed digital-circuit-based computing architecture called the Digital Annealer (DA). From an application point of view, DA is designed to incorporate a fully-connected structure so that users can handle practical combinatorial optimization problems formulated as Ising models as-is. A prototype was first announced in 2016, followed by the release of a product version. In 2018, we have released the second version of DA in which the bit number was increased from 1,024 to 8,192 bit and the maximum resolution of the correlation strength between each bit was increased to 16 to 64 bits.

In this talk I will introduce the overview of our latest Digital Annealer system for large-scale combinatorial optimization. I will also describe some examples to employ DA to solve real combinatorial optimization problems including drug discovery, delivery scheduling problem, and so on.

Quantum computing with silicon and germanium


Menno Veldhorst

QuTech Research Center, Delft University of Technology, Delft, The Netherlands


Semiconductor quantum dots constitute a promising platform for quantum computation. More than two decades of intensive research has led to impressive demonstrations of single-shot initialization and readout, coherent control, and coupling of single electron spins. Group IV materials have emerged as leading platforms, since they can be isotopically enriched to remove nuclear spin Decoherence to provide excellent quantum coherence.


I will present our efforts on silicon and germanium quantum devices and discuss the opportunities in these systems. In silicon, these include operation at elevated temperatures for hot-qubit operation, enabling to integrate classical electronics on the same chip for scalability and superior control. In germanium, I will show fast single and two qubit logic, and semiconductor-superconductor devices for novel hybrid quantum hardware. Moving forward, I will discuss our vision to increase the number of qubits toward practical quantum information.

Developments in Quantum Computing within South Africa


Shaun Weinberg

University of South Africa, Pretoria, South Africa


South Africa has the potential to globally participate in the quantum technology sector within the fields of quantum key distribution (cryptography), development of the potential of a pure state controlled atom/electron and coherency of a superposition pure state system, data storage solutions for a high-end data requirements of a molecular model and algorithms on coherency and molecula/atomic level modelling. This paper will discuss the advances in these areas, including some of the key areas that define South Africa's quantum technology roadmap, especially on the areas of:


-A quantum network to connect to international systems, potentially using existing infrastructure


-The investment in key research and development groups to further South Africa's quantum physics expertise


-Potential development of its own quantum computers after the dechorance and other infancy issues been resolved and


-Possible applications areas such as medical, environmental and molecular level modelling

Cross-cutting engineering of quantum computers



Saarland University, Saarbrücken, Germany


The development of quantum computing hardware is curently in a stage of prototyping and early adoption. For optimal functionality, the layers of the soft- and hardware stack are still closely intertwined and not completely abstracted. This is visible, i.e., in qubits-specific error mechanisms and retricted connectivity. This opens the door for co-design of software and hardware. I will outline this concept along examples of constructing quantum computers for quantum chemistry applications and highlight the importance of quantum firmware, specifically calibration, control, and benchmarking tools for quantum gates, based on methods of optimal control and randomization. This reflects the approach of the OpenSuperQ flagship project.

Topics of Quantum Computing for the Near Future


Man-Hong Yung

HUAWEI Quantum Computing Software and Algorithm and Department of Physics, Southern University of Science and Technology of China, Shenzhen, CHINA


In the near future, it is possible that quantum devices with 50 or more high-quality qubits can be engineered. On one hand, these quantum devices could potentially perform specific computational tasks that cannot be simulated efficiently by classical comptuers. On the other hand, the number of qubits would not be enough for implementating textbook quantum algorithms. An immediate question is how one might exploit these near-term quantum devices for really useful tasks? In addition, one may also expect that these powerful quantum devices are accessible only through cloud services over the internet, which imposes the question of how might one verify the server, behind the internet, does own a quantum computer instead of a classical simulator? In this talk, I will share my thoughts over these questions based on my recent works.