Benefits and challenges for unleashing potential of quantum technologies – WS 02 2021
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The EU long-term vision is the development of the Quantum internet all over Europe: quantum computers, simulators and sensors would be interconnected via quantum networks distributing information and quantum resources. By 2030 the EU is expecting to have created quantum-safe networks for connecting all countries in Europe. However from infrastructure, algorithmic and even legal perspectives quantum computing introduces new challenges that require new theoretical frameworks and robust technical development. How can Europe ensure its Quantum vision? What are the potential benefits of Quantum internet for the society and the existing challenges to tackle?
Brief presentations to stimulate the discussion. Presentations will be non-technical for a broad audience by
- Dimitris Angelakis
- From Feynman’s quantum simulators to today’s emerging quantum software and hardware industry
In 1981, at a conference on physics and computation at the Massachusetts Institute of Technology, Richard Feynman suggested the use of controllable quantum systems, quantum simulators, to simulate physical systems. This was a solution to the fact that classical computers simply can not simulate realistic quantum systems, and thus can not be applied to any realistic applications such as the design of new materials, in chemistry or in developing new drugs.
Since then, a series of very important scientific discoveries in basic science, including several Nobel prizes in quantum physics, led to what is today an emerging quantum computing industry. Now numerous private and public institutions are entering a race for developing operational quantum software and hardware, and promise use cases in a variety of areas from healthcare, finance, AI, and the environment. I will briefly review the recent milestones in this journey both in terms of quantum software and hardware, as well as the challenges ahead to avoid a possible quantum winter.
- Massimo Palma
- When machine learning and quantum physics meet.
I will briefly overview how machine learning techniques are finding new applications in the field of quantum technologies while at the same time new paradigms of machine learning techniques are either inspired or make directly use of quantum mechanical features.
- Sabrina Maniscalco
- Emergent phenomena in complex quantum networks
The development of complex network theory, consequent and motivated by the availability of big data sets, has not only provided a theoretical framework to analyse emergent phenomena but, most importantly, has permitted to introduce models explaining their origin. In the quantum realm, however, a similar step has not yet been undertaken, despite the birth and rise of quantum machine learning. Indeed, although machine learning approaches are commonly associated with enormous predictive power in big data scenarios, they oftentimes lack descriptive power.
Until 20 May 2021.
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Links to relevant websites, declarations, books, documents:
At the invitation of the Commissioner for Digital Society and Economy, a working group of European scientists worked on the "Quantum Manifesto", with the aim of outlining a common strategy for Europe to continue to be at the forefront of the development of this so-called quantum revolution. This is among the largest investments that Europe has planned in research. This massive intervention is carried out on land made fertile by constant attention born in previous years. It can itself be seen as a second phase with respect to what was planned in the period starting around the beginning of the third millennium. A detailed description of the various actions connected to the Quantum Flagship can be found in https://qt.eu and https://iopscience.iop.org/article/10.1088/1367-2630/aad1ea.
Needless to say that all over the world there is a massive investment and attention to the future of quantum technologies. A quantum is forseen to be within reach over the next decade as analysed in this document of the US Department of Energy https://www.energy.gov/sites/prod/files/2020/07/f76/QuantumWkshpRpt20FINAL_Nav_0.pdf or from the roadmap illustrated in the website of the Quantum ICT Advanced Development Center in Japan https://www.nict.go.jp./en/quantum/roadmap.html.
A broad description on how a quantum Internet is getting closer to reality, through a series of key experimental results can be found in this article in Scientific American https://physicstoday.scitation.org/doi/10.1063/PT.3.4612.
In this world-wide race towards the realization of quantum information protocols, an important role is played by private companies. Over the last decade, the different roles played by the public and private sectors have emerged clearly. An interesting overview of this aspects together with a description of the current situation can be found here https://www.scientificamerican.com/article/the-quantum-internet-is-emerging-one-experiment-at-a-time/.
Until 20 May 2021.
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- Rosario Fazio
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Subject Matter Expert (SME)
- Polina Malaja
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- André Melancia
- Roberto Gaetano, EURALO
- Alessandro Tavecchio
- Amali De Silva-Mitchell, Dynamic Coalition on Data Driven Health Technologies / Futurist
- Velimira Nemiguentcheva-Grau
- Dimitris Angelakis, Centre for Quantum Technologies, Singapore
- is a Principal Investigator at Centre for Quantum Technologies at Singapore, leading the Quantum Computing and Simulation Group. He is also Assoc. Professor of Quantum Physics in the Technical University of Crete. He did his PhD at Imperial College supported by the Greek State Scholarship Foundation, and was then elected a research fellow in St Catharines College, University of Cambridge and the Cambridge Center for Quantum Computing. He is the recipient of the Google Quantum Innovation Award 2018, the Valerie Myerscough Award from University of London 2000, and UK Quantum Electronics and Photonics PhD Prize in 2002. He is serving at the QCN board of the EU Quantum Flagship Project in Quantum Technologies, and also as the Cloud Quantum Computing Coordinator of the Singapore Quantum Engineering Program. In parallel to his academic research, he consults different industries on the potential applications of quantum computing. He has recently founded AngelQ Quantum Computing, a consulting and quantum software startup in stealth mode, where he is the Chief Scientist.
- Sabrina Maniscalco, University of Helsinki
- is the Professor of Quantum Information, Computing and Logic at the University of Helsinki, an Adjunct Professor at Aalto University, the vice Director of the Finnish Centre of Excellence on Quantum Technology, and co-founder and CEO of Algorithmiq Ltd, a startup focussing on quantum algorithms for life sciences. She obtained her PhD from the University of Palermo in 2004 and subsequently worked as postdoctoral researcher in several groups around the world (Bulgaria, South Africa, Finland). In 2011 she was appointed as Associate Professor at Heriot-Watt University in Edinburg (UK) where she worked until 2014 when she moved back to Finland as the Chair of Theoretical Physics at the University of Turku. She has coordinated a number of European and national projects and is member of several scientific advisory boards of world-leading quantum institutions. She is also actively involved in several education, outreach, and science & art projects, such as the online platform QPlayLearn.
- Gioacchino Massimo Palma, University of Palermo, Italy
- Professor of Quantum Optics and Quantum Information Theory at the University of Palermo. Formerly research fellow at the Clarendon Laboratory, Oxford and associate professor at the University of Milan. Working in the field of Quantum Information and Quantum Technologies since the early 90s. Current research area: open quantum system dynamics, quantum thermodynamics, reservoir computing.
- Rosario Fazio, The Abdus Salam International Centre for Theoretical Physics, Trieste
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- Quantum technology will allow us to solve very complex problems. Possible applications include optimisation of operations, simulations in chemistry, biology and physics, design of advanced materials, machine learning, and complex quantum networks (‘quantum internet’), as well as breaking (traditional) encryption.
- There is increasing global competition and investment in developing quantum computing for practical use. Current state of the art technology is still very limited and there are no broadly useful applications yet.
- Classical machine learning can help solve complex quantum problems and describe quantum systems. Future steps could include hybrid quantum-classical machine learning, as well as quantum machine learning. Yet quantum supremacy is not so likely soon; to avoid quantum bubble burst (quantum hype), we need to identify the real areas where quantum machine learning outperforms classical machine learning.
- Social challenges due to quantum computing include geopolitical misuse and some sort of ‘armed race’, endangering privacy (due to high ability to break traditional encryption) and disrupting the job market. Society should ‘democratise’ access to quantum technology by all.
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>> SABRINA MANISCALCO: Hi. I don’t know if you hear us, but there is no sound.
>> It’s a problem. I communicated with the – with the others.
We seem to have a problem.
>> SABRINA MANISCALCO: Okay.
>> DIMITRIS ANGELAKIS: We cannot hear you.
>> But we can hear each other.
>> Okay. Since we can all hear each other, I would propose to start the session. Just a second.
>> ROBERTO GAETANO: So good morning, we are here in Studio Trieste and let’s start with the first workshop. And let me just say a couple of introductory words. And so the workshop is about quantum technology, and the moderator is Professor Rosario Fazio, to whom I will give the floor in just a minute, after having reminded some of the rules that we have for the session.
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So this said, let me give the floor to Professor Rosario Fazio, that will be the moderator of this session.
>> ROSARIO FAZIO: Well, thank you very much. So good morning, everybody. I’m a scientist at international center for theoretical physics in Trieste and it’s a great pleasure for me to welcome the three key participants which will be the seed for the discussion today, about quantum technologies and from Singapore, Sabrina Maniscalco, and Dimitris Angelakis, and Massimo Palma.
First of all, something like 40 years or 35 years ago, I mean, the whole discussion about quantum and super positional, this stuff was just relegated. It’s quantum mechanics. It was even within physics, basic physics – basic science. It was considered kind of an exotic subject. Probably more close to philosophy and now after a few decades we are discussing about how to do technologies with all of this – with all of these effects.
We are talking about quantum information, essentially how to do quantum – I mean, I’m sorry, manipulation of information, and the communication and the organization, whatever. But not using classical dates but quantum level systems. By using this two level quantum, two level systems, essentially the whole paradigm changes. New rules will apply and these new rules offer new possibilities.
And today, we will get the point of view the Dimitris, Sabrina and Massimo. So I would propose to go – I mean, even also the topic they want to touch, to go first Dimitris and then Massimo, and then the third speaker, Sabrina and then we have some discussion.
So Dimitris, if you want to share the screen.
>> DIMITRIS ANGELAKIS: Yes. I just hope that we are not talking parallel in Leipzig.
>> ROSARIO FAZIO: I was continuing to talk.
>> DIMITRIS ANGELAKIS: It’s a quantum session so maybe that’s what is expected.
So quantum populations. So thanks for the invitation. Glad to be here. You can see my screen, I assume?
>> ROSARIO FAZIO: Yes.
>> DIMITRIS ANGELAKIS: So I will give a brief introduction in lay man’s terms. A couple of words about me, I am quantum scientist here in Singapore, but I also have a professorship back home in Crete, which I come from, in the University of Crete. I have studied in England, and spent some time in Cambridge and now split any time in these two islands, Singapore and Crete.
So let’s go back. The idea was to g a very introductory talk. And so I will as for the forgiveness for the expert colleagues. When we talk about bits in classical information theory, within about zeros and ones. Now I would like the audience to think in terms of, let’s say we have an atom now and as long as it has two different states and it could be an electron in a certain orbit or another orbit, then I can make also a bit of information and store it in this sense.
If I have three atoms in this orbit, I can store this number. However in quantum mechanics says that we can have superpositions. We can have things in two different physical states at the same time. And this is a very kind of artistic interpretation of how you could imagine the zero plus one in the terms of information theory. Of course, this is not really what’s happening in nature. Quantum superpositions exist and that has been improved over the last 110 years.
What I would like to think for a moment, let’s take the superpositions now and think of them in terms of information and computation. You can see in a very kind of rough way that you can have with three bits of information, you could in principle store eight numbers, two to the three.
Of course, it is more to it here, but what I would like to point of here, if we are trying to keep track of these in the classical computer, as we have more and more quantum bits here in this atopic picture, very, very fast, you run out of very memory and you run out of possibilities for classical computers.
With 5 qubits, you have 32 possible states. And 50 you start reaching the limit of classical supercomputers, which, you know, they operate at, you know, petabytes of memory and 300 is more bits than atoms in the universe, basically. So this is obviously impossible to keep track, and that’s what kind of calls – this is one of the two reasons that causes the generational difficulties that quantum computers are very hard to classify with cluster computers.
Computers are getting slower and slower, because we are getting to the – you may remember this computer being Intel 486 and younger guys if they are seeing this, they might be able to recognize more recent machines but the point is, we’re really reaching the limit that we cannot get the – the minimum – the minimal connections at the microchip levels less than a few nanometers. It’s just in noisy and quantum effects are very, very important at that level.
However, this is what causes the problem but also the solution at the same time. That’s what Feynman grasped back 40 years ago. He said nature isn’t classical, damn it and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy.
He was one first guys to grasp this tool, especially the hardness of classical computation of classical systems and classical computers. There’s what a famous work at MIT, a conference exactly 40 years ago, Feynman is here at the back and a few other fathers in the need, Charlie Bennett from IBM and others are here. This continued as a milestone. It’s interesting what happened the last 40 years and I will try to give you a little bit of a feeling about that.
So what we have now, that we didn’t have 40 years ago is that we have the technology to go inside matter and manipulate particles at the individual one by one level. This is really there the key that brought the field from the more theoretical fundamental aspect in the ’80s and ’90s to quantum technology to computer sensing, and communication. So these exotic notions play a vital role.
Where we are now, 40 years later, we do have machines and I will talk a little bit more about the computing parts. My colleagues will probably talk about the communication as well and others. We do have machines that can outperform classical computers but in no practical applications in what I call mathematical useless tasks but quite exciting results. One result I would like to mention is the result by Google that you might have heard of back in 2019. They managed fab Richard Feynman case, 53 qubits in this chip. I will talk a little bit about this later on. And then run what is named in the – it was technical terms a random circuit and basically, they did what is known as a sampling problem. It’s a mathematical problem, however, it would take many years, if not 10,000 years, definitely many years to do on a classical machine.
This is a fact. This is an experiment.
It has proved that quantum computers actually exist and they can do something that classical computers cannot do, however, not for practical applications yet.
As you can imagine, there’s a big race globally about that. Just this is 2020. We are not in the future yet. A group of colleagues in China led by Jiuzhang, they demonstrated a similar kind of resolve but using a very different technology based on photons.
What I want to highlight here is that this kind of applications are possible because a lot of basic science, a lot of important results due to support and excellent people working in the field, the last 20 years, have allowed us to do this – this – I would say, transient motion from fundamental physics to applications now. That’s what we call quantum hardware. That’s what was experimental, including many Nobel Prizes, just indicatively, I call here, in ion trapping, and also Dave Wieland.
What did you do these quantum machines? The first one that was mentioned 20 years ago by almost now by Peter Shor is breaking RSA encryption, and the theme is breaking communications over the – the communications over the Internet.
Peter Shor proved that if you have a large enough quantum computer with let’s say, 2thousand or 4,000 roughly quantum bits, you would be able to break this 1,024 keys, bits, keys that is used. And now there’s 2,048, based on this RSA protocol, which I don’t have time to go into detail.
Of course, this generated a big interest in the field and that was the key to – to basically make quantum science and technology what it is today.
This is not where we are. I will highlight this is definitely not at the moment possible with hardware. We have 50 or around 60 operational qubits. You knee 4,000 fully controllable qubits however, the way hardware is evolving, if you have data that you want to protect for the next five or ten years, you better act now because it can happen.
I’m sure many governments and many institutions, private and public ones does have secrets. Everybody has secrets.
What can we do with near term devices? There are many different suggestions. The main axis according to my personal opinion is around optimization problems for applications in logistics in banking, financial portfolios, shipping/delivery routes. There’s a lot of working happening at chemistry and machine learning and we will hear more later on by Massimo, I think.
So one slide on chemistry. What we hope to be able to achieve with chemistry is to really simulate with higher precision, the hard problems which have to do with quantum correlated entanglement properties at atomics scale. If we can do, that then we can actually have a design for it happening faster and we can simulate small proteins and slowly virtually to biology. This is a vision and a map. We are roughly, let’s say around 100 qubits in quantum hardware as a name now has verified small molecule simulations that are still within the rates of classical computers, but we hope at some point as the hardware gets bigger and bigger to enter this type of regime.
Another area which is popular is material simulation. This is an area we’re also kind of active in my group. We have done some work in – that relates to conductivity properties of materials and there are all of these other interactions. This could be applied to, for example, if we could push this further towards conducting superconducting material, and designing special materials, special properties.
There’s also other work in machine learning that we do in my group, especially on financial applications, ongoing through data and figuring out customers’ behaviors and predicting different, you know, trends and market trends. This can be Doppler radar also with quantum – fully quantum or quantum-inspired algorithms which is basically algorithms on quantum computers. There’s something with near term quantum.
There is a big effort worldwide. We have all the big countries are putting a lot of support. There’s a lot of competition worldwide. Definitely China, Singapore has a program for some years. America is coming in strong. There was always quite a big transition there. And that’s not only on the public sector. I would like to highlight that a lot of private efforts. If you look at the start-ups and the spinoffs in the field, it’s almost at exponential rate.
One has to highlight that we have to be careful to avoid – when you go to this exponential type of growth, one has to be careful not to create a bubble that can burst. Maybe we can discuss this later on at the panel discussion.
A quick look at patents, that kind of correspond to the state. China is pushing very strongly in the quantum communication and the quantum distribution systems. The United States is leading on the quantum computing applications ,and everybody is into this big race.
Where we started so hardware, there’s roughly around 50 to 60 qubits operational. I want to highlight this. Anything else, sometimes companies’ announcements is not really there yet. These are numbers from published research and papers. 50 qubits different levels, ranging from photonics and trapped ions in the game.
One word about our efforts here at Singapore, we are pursuing different technologies. This is a picture of the 10 qubits chip that’s being fabricated as we speak. This is out of the fabrication now with one of the experimental groups and this is what the bridge looks like.
>> ROSARIO FAZIO: Dimitris.
>> DIMITRIS ANGELAKIS: So thank you for your attention and I want to finish with. This I think that there is making predictions is a risky thing and we can discuss it later, maybe how far things are.
>> ROSARIO FAZIO: Thanks a lot Dimitris. Go next very nicely to the contribution of Massimo about quantum technologies and machine learning. So Massimo.
>> MASSIMO PALMA: I will try to – okay. Let me share the screen.
>> ROSARIO FAZIO: A few minutes.
>> MASSIMO PALMA: I’m trying to share my presentation. Okay.
Oh, here it is. Okay.
I must apology, as soon as I started talking, I decided to connect at my office. At the same time, they are starting redecoration inside. So you may hear some background noise which will be annoying.
Okay. I start basically from when Dimitris ended his presentation. He presented a nice discussion of what is the present technologies and what is the historical background which led to the emergence of quantum technology.
I will look at the same thing in a slightly different angle. In particular, there’s the other emerging techniques which is the broad name of machine learning and artificial intelligence. I will try to see how the two things we meet one another and in particular, I will try to focus my attention on how classical machine learning techniques can – can be helpful in a quantum context. The other way – and the err way around, I mean how quantum computing can provide different perspective in machine learning.
I think that Sabrina at the end will basically carry on with further perspective in this direction. What Dimitris makes clear is quantum theories are complex. The first one was Feynman, but it took several years, basically something of the order of 5 to 10 years before the idea that quantum systems are complex. It’s difficult to simulate and led to the idea that quantum – since quantum systems are complex, they can do jobs that classical systems cannot do.
And this is like a different kind of – a different kind of – a different quantum computation. The quantum Turing machine, in which the working principles of classic Turing machine which is a universal computing device which can do any task, it’s in the quantum dynamics in which your computer in parallel can perform different calculations and interfere with what the decide outcome or solutions of your problems.
But learning parallel, they have been developed – a quantum gate paradigm computation. So in full analogy of what you can do in a classical scenario, in which you can have both the Turing machine and the Gates paradigm focus and computation, in between the ’90s and the early 2000s, we developed these two types of paradigms, quantum Turing machines and quantum Gates paradigm. And you can split down your computer protocol in terms of elementary Gates, allowed – they develop the algorithm, like the can Shor algorithm, or other types of algorithms that can be implemented on a quantum device.
On the other hand, physicists became to be interested in different ways of doing computation. In particular, open the way, quantum simulators and on the other hand, quantum annealers you can have a quantum processing or quantum protocol in a way – you can try to find a way to solve complex quantum problems by mapping them that the dynamics of a quantum system, not necessarily in terms of Gates or not necessarily in terms of a Turing machine. I mean it’s sort of a broader approach.
And similar approach, was done in the classical sense in which they are developing algorithms in terms of Gates and the move towards other kind of computation paradigm, in particular, machine learning and so the idea now – the way I see the scenario and not just me, but the community sees the scenario is you have complex quantum systems and quantum systems led to quantum computing and different types of quantum computing, either by quantum Gates or quantum Turing machine but also in quantum annealers, you map one into the another one. And parallel, enthusiasm have classical machine learning techniques and you have abandoned the standard algorithm description in terms of Gates and you move towards paradigms in terms if you like of neural networks or any way of recognition algorithms or clustering and different types of just simple quantum Gates sequence of operations.
Now, at the moment, people have been interested in the following two things. On the one hand whether classical machine learning can provide some help, some sort of help to solve quantum systems which are difficult to simulate on classical computers and on the other hand, whether the quantum system dynamics and classical machine learning can merge together towards the development – across the machine learning algorithms in which three ingredients match together and you have quantum machines which fit into an algorithms which are by themselves basically classical, okay.
And what are the moment, the thing in which machine learning solves complex quantum problems? Again, Feynman original statement came out from the – emerged from the difficulty of analyzing the quantum system, including the many body system. It’s in many qubits, typically many body systems which the system is strongly interacting with each other and therefore you cannot restrict yourself to just a few – there is a freedom, but you must look at the whole dynamics, the whole spectrum of your system.
And a fate in which the machine learning protocols help to provide an interesting tool parallel system, is the ground state of complex quantum many body system. They can undergo phase transitions and the ground state property which means the property of the state with its lowest energy, can very strongly, according to how to change the external control parameter of the – of your system, and to identify – not anything else, but just looking at your system and to tell which phase it is, I mean to classify the phases of your system is by itself a complex problem.
And classical machine learning has provided useful tools on solving – to classify the properties of a complex quantum system to identify the temperature in which phase transitions apply and it basically changes structure. And on the other hand, I mean, people have tried to understand which are the – which class of problems can be usefully – which kind of problem can classical machine learning provide useful tools to solve categorization problems and apparently, the situation – it depends on how many entanglement in your system. The presence of strong collection which cannot be accounted by classical computing. You have many interacting qubits and you can ask yourself, what is the entanglement, the degree of correlation between the path of the system that you are looking at and typically the rest, and the environment.
And whenever this type of entangle like the surface of the border, the boundary which separates your system from the rest which is called a system, which are said to have abate the area, it’s called area law, I mean these kind of problems can be characterized and categorized rather efficiently by classical machine learning algorithms, but when the – these kind of entanglements, the degree of complexity grows like the volume but in this case probably classical machine learning algorithm cannot be useful, they are not useful in handling these kind of algorithms.
On the other hand, another area in which complex quantum system can merge with a classical machine learning algorithms is quantum reservoir computing. You have an input state and your reservoir is a complex quantum system, which is intrinsically open. You don’t have the incoherent of fragile interaction of the system with the environment, because if you like, the – your quantum computer is itself rather noisy, is a way to vitalize, and it’s a system which is interact – it’s large enough to be in an environment itself and then you try to extract some sort of information about your input states by making a suitable measurements of your quantum reservoir.
Apparently, this has been efficient in identifying some complex quantum feature of your input states like your entanglements or you have been able to categorize your input state.
And then I will rapidly because I think Sabrina will basically compliment my discussion by giving a perspective on which kind of problems can actually efficiently solve, I mean which are the arena in which you can combine classical and quantum ingredients and what are the perspective in this direction.
I mean, the future direction is you are going towards hybridization between a classical and quantum machine learning technique.
What I think is promising is algorithms where you have quantum machine learning and at the same time, you are using an open quantum hardware, okay? Which you are not worried about the fact that the system is open, because actually, the fact that the system is in the proper environment is in itself is a resource.
However, it’s not yet clear which kind of problems can be more efficiently solved with – either fully quantum or hybrid quantum classical approach. And this has been – there’s a lot of quantum high, however, my new point is that it’s still not clear which areas in particular – the areas in which you use quantum algorithms to solve a classical problem can be more efficient. There’s’ quantum agreement in which there’s quantum machine learning.
I’m skeptical whether in the short-term areas where optimization of classical machines is more efficient. The areas in which we have shown quantum supremacy is artificial problems. More solid ground are eras in which you find quantum chemistry or material simulations, but I would be more cautious in claiming that quantum supremacy is something you can achieve in a short term basically everywhere. So we need to be – in order to avoid the burst of the quantum bubble, you must first clearly identify when quantum resources are really needed to outperform the classical one. You have been witnessing strong progress in classical machine learning and in terms of algorithm.
Okay. So this is basically what I have to say. And it is very, if you like, not very specific. It’s sort of broad and I don’t think it’s specifically answered but if you like, more of my personal feeling of what people should look at and how the system will evolve the next few years.
And having said this, I will stop sharing my screen.
>> ROSARIO FAZIO: Thank you, Massimo. I think Sabrina presentation and we can continue with you, Sabrina and then just open the –
>> SABRINA MANISCALCO: Sure. I will now share my screen and my presentation. I hope you see.
>> ROSARIO FAZIO: Yes.
>> SABRINA MANISCALCO: Let me know could you see it well?
>> ROSARIO FAZIO: We see a color table.
>> SABRINA MANISCALCO: Okay. This is always – it happen. Just a second. I need just to close it from here. Now it should be working. Is this right?
>> ROSARIO FAZIO: It’s okay. Yes.
>> SABRINA MANISCALCO: It’s okay. Very nice. So, yes, I will – in my title it a seem a bit strange, actually. What I will do is to continue along the lines of quantum and classical, and the differences between the two, and how this is important in quantum technologies. So it will have to do with both quantum methods, characterization algorithms and classical ones and how we can learn from classical towards quantum and the other way around. The hybridization between these two approaches.
And I will begin by very briefly introducing myself. I’m a professor at the University of Helsinki and Aalto University. I’m also cofounder and CEO of our start-up Algorithmq. And my group generally, Helteq is the name of the group, is focusing on three different areas, the first one is more related to quantum algorithms and some of these approaches like quantum benchmarking in collaboration with the IBM researchers and in collaboration also with the computer science department at my university. But we are also working on most – most fundamental – (No audio).
>> ROSARIO FAZIO: Sabrina?
I think there is no connection and I see Sabrina frozen, but I don’t know what to do.
So I will try to send an email. I don’t know. I will try to contact Sabrina.
>> Maybe we can ask if there are any questions?
>> ROSARIO FAZIO: Yes, that’s a good idea. We can go on with some questions. If any, and I will try – yeah, so I have a question that I will read. I don’t know if everybody sees, but it was written in by Vladimir. This is a question for both.
>> SABRINA MANISCALCO: I’m sorry.
>> ROSARIO FAZIO: Sorry.
>> SABRINA MANISCALCO: I don’t know why my computer restarted. And I don’t know.
>> ROSARIO FAZIO: Don’t worry. Meanwhile there was a question. So let me read the question and then we continue.
>> SABRINA MANISCALCO: Yes.
>> ROSARIO FAZIO: So the question for three of you. What do you see as the main societal risk that quantum computer is bringing?
I don’t know.
>> MASSIMO PALMA: Dimitris, you go first or –
>> DIMITRIS ANGELAKIS: Yeah, yeah, I can say. Societal risks? I mean two things. One is any new technology that is opening new areas and giving edges to certain countries or groups of people that have it, I think, it’s very important to avoid having some sort of nuclear race or space race. We are far from that yet because the technology is still at the basic level, but if – if quantum computers happen and they seem they will happen, I would like to – I would like to – society to think and governments to think how to, you know, make it – democratize access to quantum computers. I think this is an important aspect. I’m not sure what is the optimal way to do that, but I would – I will say that it’s important to keep in mind.
>> ROSARIO FAZIO: Massimo.
>> MASSIMO PALMA: I agree that whenever there’s an emerging technology, the access – it’s something that the government should particularly look at with care.
There are two things which I think deserve particular attention. On the one hand, you should not forget that quantum technology started from quantum cryptograph on the one thing and quantum code breaking on the other hand. The security is an issue. On top of looking at implementing a quantum computing devices, most of the races have been going towards quantum communication about satellite – I mean, people – the governments have been talking about quantum Internet and so the access to privacy, and access to Internet is an important problem to address.
On the other hand, the possible – the possible society risk on the job market are the same that are posed by things like artificial intelligence, okay? So the job markets will strongly change over the – over the few years because of the impact of artificial intelligence and machine learning algorithms.
My feeling is that quantum computing will basically go in parallel, so basically the risk of quantum computing and algorithms will be the same. That’s not much to add, I think.
>> ROSARIO FAZIO: So there is another question. I propose the following. So Sabrina, probably you can start by answering this question, and then continue with your presentation.
>> SABRINA MANISCALCO: Sure.
>> ROSARIO FAZIO: The question is from Amali: How soon will applications for the public become evident?
>> SABRINA MANISCALCO: I think it depends on the definition of public. In some sense, certain applications, for example in quantum cryptography are already available to buy, really, to companies. Applications for the public in the sense that, you know, you could buy a machine like a computer which contains some elements or is connected to some quantum computers nowadays, everyone can connect to – to quantum devices on the cloud, for example, IBM and many other offers – offers compute access to their small time quantum devices to the cloud, to the public.
The question is whether this is useful is another story. Clearly. This can be used for educational purposes and learning. And it can be used for citizen science project and it can be used for everyone with an interest in quantum computing and it’s important to form a community that is aware and well-educated on these topics and, again, as we were talking before to give access to information and education on quantum technologies to everyone, but, on the other hand this does not mean that applications that are immediately used to change our society exist yet. So it is a question of the useful quantum a vantage, potential – the potential is great.
Commercially, it’s very difficult to think of an answer like very definitely or very precise. It’s very difficult to predict these things it could be in five years or three years or 20 years. It really depends. Perhaps Massimo will have a more precise answer to this. I wouldn’t be ready to say now, you know, a useful device that uses quantum computers, you know, providing advantage with respect to the classical one would be available in a year or five. It’s hard to say.
>> ROSARIO FAZIO: Okay. So I think we can –
>> SABRINA MANISCALCO: Yes, I will go back. I was briefly introducing my group. And I will go to the core of miles an hour presentation. Besides the fundamental problems of quantum science, we are very much interested in education and we developed a platform, and qplaylearn. We have different levels, even for the general public on the topic of quantum science and technologies and then we have some interesting quantum biology. Sorry, this is the wrong presentation. I’m sorry about this, but I had two presentations open and they – oh, and the previous one is – yes. For some reason it didn’t open and now – sorry for this difficulty. I really didn’t know what happened. Yes, it’s here. To my computer, it just decided to restart from zero.
Yes. So what I’m talking about, what I will begin talking about, is some sort of gap between what we called microscopic and macroscopic description of many particles quantum systems. So we definitely historically when we were with the birth really of quantum technologies we began manipulating individual quantum systems only and isolating them, individual items and individual molecules and even what Dimitris was mentioning.
We are able to have such single atoms within the framework of many body systems. And so here are some pictures really of showing the manipulation of individual atoms in lattice, in systems which contain many, many body – I’m sorry, many quantum atoms, many systems. The small dots you can I be imagine that they will be used as qubits as in presentation of Dimitris’s. We could engineer the state of many body systems. So we can experiment with this system, and we can create experiments for these systems but at the same time, we are also able to develop numerical approaches like tensor network methods, which allows us to simulate the classics.
Why am I talking about this both experimentally and numerically deal with the data which describes systems composed of many qubits or of many particles, because we are starting to have really in our hands a vast number of data. It could be quantum computers made of 100 qubits or quantum simulators. So we have in our hands the ability of both making experiments and obtaining simulations from classical computers up to a certain level, of course.
The issue is as this amount of information grows, as these data sets grow, how do we extract relevant information from the analysis of such a large amount of data? Now the reason why this question is interesting, because in many aspects of quantum theory, one of the most let’s say yet unexplored but interesting sets of if phenomenology, is collective structures, behaviors and phenomena, whenever we have to deal with a large number of possibly interactive quantum systems.
So what happens is while we can describe few systems microscopically as we increase the system, properties related to their collective behavior in a very nontrivial way emerge and becomes extremely interesting and relevant.
This is an impact in quantum biology and quantum materials and quantum thermodynamics and quantum dynamics and I’m mentioning some more fundamental and applicable related topics.
Now, in the classical world, sorry in the world of technology or in the description of microscopic objects, what we have learned in the last 20 years is that complex network theory is a new emerging science that provides the theoretical framework to analyze emerging phenomena and they are able to describe their origin and I will have here just one example which is the most example which is here you see on the right-hand side of this picture, the backbone of the Internet. This is a graph, which shows links, that represents connections in the existing Internet structure. And you see how this has evolved from the initial the network that you can see on the left-hand side versus current structure in the Internet. And the way this has been growing is characterized by properties that are properties of so-called real network that characterize and they are similar for technological networks and our networks like our brain or biological networks. And so it looks for classical system. A number of properties are in common. And they are related no this concept of emerging – emergence that I mentions before. How about the quantum case?
As we have larger and larger amounts of quantum systems how can we use in some ways properties or how can we characterize the properties like emergence in the quantum case? Are they different from the classical case?
And this is relevant, relevant for what I call as official networks. These will be the quantum version of the Internet, where we would have links transmitting quantum information across nodes where the information is stored and the natural networks. So existing biological networks where there is evidence of actually some quantum properties to be used in order, to toughly transmit certain excitations across the network. And this is one of the most studied and complex systems.
And a way of summarizing the scenarios and the main message that I would like to give is the following. So we do have a number of developed theory, that is the theory of complex networks that could be used or could be in a way inspired or merged or could be further developed in the direction of the analysis of quantum systems and we can think of both a bottom up and a top down approach. We can think of starting from the building blocks, starting from just again between two qubits, starting from individual components of a network, and building more and more, quantum networks as we think in terms of scalability and design until we reach, for example, so the so-called quantum Internet.
But we can also think of the opposite idea. We can start from the properties of real complex networks and we can think can we use these properties, for example, more world properties which in a way allows to reach different nodes with different states, and so the Internet is showing this property which is very useful for transmission of information, as well as properties like the resilience to link breakage of – exhibited by all real networks like the real existing Internet.
How can we use this knowledge that we have, to engineer quantum networks that might have properties that are useful like resilience to link breakages one of the subject samples?
So we can think of going in the other direction. So what we have been doing is thinking of ways of describing or extending a theory to reach the complex network perspective. There is a lot of work other than what we have been doing, but I just want to mention some of the directions one could go, and one could go in particular, we have developed a way of very efficiently build the networks of pairwise quantities. They mentioned the entanglement between the qubits and you can bill the networks that represent the entanglement between each pair of qubits of a quantum computers and any quantum systems and this would allow you to build complexes that are borrowed electricity classical complex network theory for the analysis of many body systems and for example, we have demonstrated that if you go in this direction, you can discover new physics and in particular emergent properties which was unknown which was hidden which was in a way unexplored and which is difficult to characterize without such tools that we borrowed.
So in general we believe that a complex quantum network theory is really a suitable framework to describe this emergent properties, both in artificial and in natural complex networks. And then I just want to conclude with – with the importance of stressing the great importance of literacy in quantum, especially in a moment, when both Dimitris, there is a risk of a quantum hype and a quantum winter or a quantum bubble, and so we need to Ed gate to the proper message of quantum. Thank you for listening.
>> ROSARIO FAZIO: Thank you, Sabrina. I don’t know if there are questions, additional questions. There were already some questions.
I do not see and then I will pass the word to Vladimir for – oh, sorry. There’s a question. A layperson wondering if they can help connect – I will just read it. As a layperson, wondering if quantum can help connect the last mile or remote locations better.
>> SABRINA MANISCALCO: I don’t think. Strictly speaking I don’t think there’s any way that – demonstration that in the quantum Internet, for example, you would be able to implement or to prove that you can connect to different locations better, but then again, it depends on what is better.
I mean, for certain, quantum technologies, it’s important, for example, to be able to share quantum information. And if you want to share quantum information, you have to use means that are appropriate to transmission of quantumness and entanglement and you need to use quantum tools but in materials of an advantage, it would be very important to understand for what? And this is something which is still a topic of research, by all means. Clearly what has been demonstrates is a quantum is useful. It’s useful for a number of purposes and for a number of activities but it has to be – for me, it is extremely important to specify always what is the purpose itself. So in itself if you tell me to connect the most remote locations, then I would say it could be. It depends.
>> ROSARIO FAZIO: Okay. Thank you.
I think I will pass the floor to Vladimir. To the panel report. Please.
>> VLADIMIR: Thank you Rosario. I will try to share the screen with some of the messages. As some of you might know the Geneva Internet Platform provides some sort of messages at the end of each session and we are on to them, and I will ask you if anyone has any general sort of pushback but we will have time to fine tune the wording. We start that quantum technology would allow us to solve very complex problems, possible applications include optimization but also breaking traditional encryption. So that’s the first message if anyone has any big objection on that one, please post to the chat. And I will continue in the meantime with the second message.
The second message is there an increasing global competition and investment in developing quantum computing for practical use. Current state-of-the-art is still very limited and doesn’t allow real breakthroughs. There are no broadly useful applications yet.
Again, if there’s any general pushback on this message, please write in the chat. Fine tuning we can do later.
The third message is classical machine learning can help solve complex quantum problems and describe quantum systems. Future steps can include hybrid quantum classical machine learning, as well as quantum machine learning. Yet quantum supremacy is not likely soon, to avoid quantum bubble burst or quantum hype, we need to identify the real areas where quantum machine learning outperforms classical machine learning.
And social challenges due to quantum computing includes geopolitical misuse and some sort of armed race and endangering privacy due to high ability to break traditional encryption and disruption of the job market. Society should democratize access to quantum technology by all. Let me stop sharing and check if there’s any particular comment in the chat, if there are no particular pushbacks on these messages, we will consider that they are sort of a rough consensus. And they will be posted on the EuroDIG website. We will polish the text if there’s other inputs.
Back to you, Rosario.
>> ROSARIO FAZIO: Thank you. I would like to thank everybody, participants, key participants, speakers for the contribution and I pass the word to Roberto in Studio Trieste for the conclusion of the session. Please.
>> ROBERTO GAETANO: I would like to thank all the speakers and all the participants to this session, and also, the rapporteur, who at least in my opinion has captured the essential of the debate.
So this session is now closed. And the next session will start, I think at quarter past 12:00. So have a nice day, everybody and bye.
>> ROSARIO FAZIO: Bye-bye. Thank you very much. Bye-bye.