TOPQC 2019
QUANTUM COMPUTING PROMISES AND REALISTIC
DEPLOYMENTS: STATE OF THE ART AND FUTURE SCENARIOS
An International Advanced Research Workshop
June 1012, 2019, Cetraro, Italy


ABSTRACTS
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 DWave 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
NISQera quantum processors Jerry Chow Experimental
Quantum Computing IBM, New York, NY, USA As the field marches
towards quantum advantage with nearterm 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 nearterm algorithms. We propose
a deviceindependent 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. 
Edward Dahl DWave
Research, Canada Users have programmed
a variety of optimization, machine learning and material simulation
applications on the four generations of DWave's Chimera architecture. Their feedback guides DWave'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 nonstoquastic
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, NL9747 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 discreteevent simulation [3] allows to
circumvent the quantum measurement problem and permits the simulation the
outcome of real “quantum” experiments on an eventbyevent basis. Finally, I
compare discreteevent 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,
4761 (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 eventbased 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 gatebased
quantum computers”, Comp. Phys. Comm. 220, 4455 (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 topics：Unified programming interface, Quantum hardware
interface, Distributed information processing, Quantum network and internet,
and Quantum & postquantum crypto. Finally we will briefly introduce
Baidu Quantum Program (BQP) to show our strategies in tackling these
challenges. 
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 GPSlike 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 shorttime Hamiltonian dynamics Jonas Haferkamp nstitut fur Theoretische Physic, Freie Universitat, Berlin, Germany "A nearterm
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 GoogleAI’s random circuit sampling. These proposals
identify simple quantum tasks for which there is strong
(complexitytheoretic) evidence” that they cannot be efficiently classically
simulated. In this talk, we will
consider and develop a recent quantum advantage proposal based on
constanttime evolutions under translationinvariant, nearestneighbour
Hamiltonian [BermejoVega 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 averagecase hardness of
exactly evaluating the output probabilities. Our averagecase result builds
upon recently developed techniques for random circuit sampling. Our anticoncentration result exploits and develops
connections between random quantum processes, Hamiltonian gaps, approximate
2designs and anticoncentration. Specifically, we
prove anticoncentration by showing that
translationinvariant, constanttime Hamiltonian evolution in 2D forms
approximate 2designs 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
largescale 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 IntermediateScale Quantum (NISQ) devices must
be compared to the stateoftheart classical technology currently available.
To this end, in collaboration with Google and ORNL, we have developed qFlex, an highperformance 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 abinitio 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, D52425
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 gatebased 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 2SAT problems, which have a unique ground state and a
highlydegenerate 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 DWave 2000Q QA with more than 2000 qubits. References: K. Michielsen, M. Nocon, D. Willsch, F. Jin, T. Lippert, H. De Raedt,
Benchmarking gatebased 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, 4761 (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 IntermediateScale
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 nearterm
quantum devices. A simple physical qubit architecture is easy to optimize and
replicate to produce a device with a highqubit 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 nearterm quantum demonstrations. We evaluate the impact of
different trappedion configurations through classical gatelevel 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 IntermediateScale Quantum, experiments on quantum computers
have proven that quantum computations are feasible and can lead to
highfidelity results with the application of errormitigation techniques. In
this keynote, I will present an overview of quantum computing. I will also
discuss how quantum computing is a crossdisciplinary technology, allowing
people with different skills and research interests to perform research and
contribute to this exciting field. 
Quantum Computing:
Nearterm 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 gatebased quantum computing and using a quantum
computer as an annealer for optimization
problems. Novel quantum simulation
algorithms, quantum machine learning as well as hybrid quantumclassical
computing techniques and error correcting codes to tame defects are being
developed for nearterm 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, manybody 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 trappedion 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 linearoptical 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 highfidelity 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 realworld applications. However, this strategy is challenged by the end
of Moore’s Law and Dennard scaling, flatlining 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 transexascale
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
quantumclassical 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 tradeoff possibilities between classical and quantum
resources. Inspired by a Paulibased 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 GottesmanKnill
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 https://arxiv.org/abs/1806.03200. 
Digital Annealer Technology for LargeScale Combinatorial
Optimization Problems Kazuya Takemoto Technology
Development Project, Digital Annealer Unit, Fujitsu
Laboratories Ltd., Kawasaki, Japan Combinatorial optimization
problems are decisionmaking 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 largescale 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 NPhard, 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 digitalcircuitbased computing
architecture called the Digital Annealer (DA). From
an application point of view, DA is designed to incorporate a fullyconnected
structure so that users can handle practical combinatorial optimization
problems formulated as Ising models asis. 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 largescale 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
singleshot 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 hotqubit 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
semiconductorsuperconductor 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 highend 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 
Crosscutting engineering of quantum computers WilhelmMauch 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 qubitsspecific error mechanisms and retricted connectivity. This opens the door for codesign
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 ManHong 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 highquality 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 nearterm 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. 