In one of the previous blog posts, David DiVincenzo reviewed his criteria. Here we will follow this theme and look how these criteria translate onto a physical system. Currently, there are a few qubit implementations that look quite promising. The most prominent examples are superconducting qubits, ion traps and spin qubits. We will focus on the latter one, since that’s the one I’m working on. All the platforms mentioned above fulfill the so called DiVincenzo criteria. These criteria, defined in 2000 by David DiVincenzo, need to be fulfilled for any physical implementation of a quantum computer:

- A scalable physical system with well characterized qubits.
- The ability to initialize the states of the qubits to a simple state, such as |000⟩.
- Long relevant coherence times, much longer than the gate operation time.
- A “universal” set of quantum gates.
- A qubit-specific measurement capability.

In this article we will go through all these criteria and show why spin qubits fulfill these criteria, but before doing that, let’s first introduce spin qubits.

Spin qubits are qubits where the information is stored in the spin momentum of an electron. A spin of a single electron in a magnetic field can either be in the spin down (low energy) or in the spin up (high energy) state. Comparing to a classical bit, the spin down state will be the analogue to a zero and spin up to a one.

One of the first steps in a spin qubit experiment is to obtain a single electron that is isolated from its environment. The isolation from the environment is needed to make sure that the electron does not undergo unwanted interactions that will affect its quantum state in an uncontrollable manner.

Isolated single electrons are obtained by shaping a two dimensional electron gas (2DEG).

When stacking different materials on top, one can get, under certain conditions, a 2DEG. A two dimensional electron gas can be seen as a plane where electrons are free to move wherever they want. One could compare the 2DEG with a very thin metal layer.

Once we have a plane of electrons, the trick is actually to push away from or attract electrons to this plane by using gate electrodes placed on top of the 2DEG. This will allow you to make a row of electrons (= isolated electrons). Here each electron will form one spin qubit on the chip.

Now we covered the basic concepts of spin qubits, we can return to the discussion why spin qubits can form a good platform for quantum computation. In the following I will go through the DiVincenzo criteria, one by one, and show why and how spin qubits satisfy these criteria.

*A scalable physical system with well characterized qubits.*

This criteria consists of two parts, a scalable and characterized system. The characterized part means that you know how to simulate the qubit (e.g. you know the Hamiltonian). This knowledge is needed to design the required gate operations. In case of spin qubits, the system is described well by the Fermi-Hubbard model.

The second requirement for this criterion was the argument of scalability. Where scalable means that you can extend your qubit control to millions of qubits.

A first thing to look at could be the size of one qubit. For a spin qubit this will correspond to an area of 70nm × 70nm. This means one could easily put a billion of qubits on a chip of 1cm × 1 cm.

However, this is not the big challenge. Problems arise when you start thinking about the control of the qubits. In the lab we observe that each qubit has its own personality. Examples are, different gate voltages for every qubit, the different amount of power required to operate a qubit, different energy differences between spin up and spin down…

The reason that every qubit has its own character has to do with the fact that we work with imperfect materials and an imperfect fabrication process. This is one of the big reasons we are collaborating with industrial chip manufacturers (e.g. Intel). Aside from the fabrication issues, there are also very practical problems, for instance how to connect all the control electronics to the qubits. For example, to have full control of a 5 qubit system, you need 40+ connections. 14 of these connections are high bandwidth connections. This means that very fast signals need to be sent through these lines to the qubits. Therefore they need to be connected by dedicated coaxial cables (which are relatively large). The picture below shows what a chip carrier for a 5 qubit chip looks like and where the coaxial cables need to be connected. Note also that such a chip needs to operate at extremely low temperatures, which also means you have a limited thermal ‘budget’ (e.g. μW range). A typical operating point for spin qubits is 10mK, which is close to absolute zero.

So are spin qubits scalable? The answer is, we don’t know. Until now, people have only focused on small systems (a few qubits). The same is true for superconducting qubits and ion traps.

*The ability to initialize the states of the qubits to a simple**state, such as |000⟩.*

When working with a quantum computer, one could imagine that, before running any algorithm, the user should be able to start from a well known quantum state. Typically, the lowest energy state is taken (e.g. 3 qubits in 0, |000⟩). For spin qubits one can achieve this using the Elzerman method. The idea is to load one electron with spin down from the reservoir in the quantum dot. The reservoir is an area next to the qubits filled with a lot of electrons. Note that when you put a lot of electrons together, there will be no difference anymore in the energy between spin up and down (electrons strongly interact with each other).

Now the idea is to align the energy level of the spin qubit (yes, you can control those!) with the reservoir in such a way that only an electron with spin down can tunnel in.

*Long relevant coherence times, much longer than the gate operation time.*

In any quantum system there is a characteristic time that tells you how long a quantum state will be maintained if left alone. This time is called the coherence time. It is important that the coherence time is much longer than a typical gate time (e.g. changing a qubit from 0 to 1 and vice versa). When this is not the case, all the quantum information will be lost before the algorithm is done. The loss of quantum information is mainly due to the interaction of the qubits with the environment.

The coherence time of spin qubits in mainly limited by the nuclear spins in silicon. When considering a spin qubit, one can imagine the electron as a charge cloud that is sitting at the Si/SiGe interface. This charge cloud overlaps with the nuclei of the silicon atoms. These nuclei can also possess a spin. When one of the spins of the nuclei flips (even at low temperatures there is enough energy to make this happen at random), the energy of the spin qubit will change a bit. This causes random energy fluctuations and will affect the coherence time of the qubit. In practice, we try to minimize this effect by using isotropically purified Silicon (e.g. ^{28}Si has no nuclear spin).

The coherence time of spin qubits can be quite long (e.g. 100μs, with ^{28}Si). The time for a single quantum operation will take about 100ns or less. This means there is a factor 1000 difference between gate time and coherence time, meaning that you can run 1000 operations before your information is lost.

*A “universal” set of quantum gates.*

A quantum algorithm typically consists of a set of operations, let’s call them U1, U2, … .These operations represent changes to the state of the qubits. One can think of this as the quantum mechanical equivalent of classical AND, NAND, OR, … gates. In the quantum mechanical case, you have two kinds of gates, single qubit gates and two qubit gates.

Single qubit gates, as the name suggest are used to manipulate a single qubit. For spin qubits, this is done using microwave photons. The energy of these photons corresponds to the energy difference of the spin up and down state. When both energies are exactly matched, one can controllably change the spin from up to down or any combination in between.

Two qubit gates are gates that entangle two qubits. This means that both qubits get a kind of awareness of each other. In practice, making two qubit gates is quite straightforward. This can be done by pulsing the barrier gate which is in between the two qubits. One could visualize this interaction as pushing the electrons close together, so that they interact with each other. One of the topics I’m actively working on, is making this kind of gates very reliable. At the moment these gates are often plagued by a lot of noise. In practice, this means that your gate operation is not reliable anymore. To solve this, we are exploring different regimes where you can make a two qubit gate. The interaction between the qubits is, for example, highly dependent on the energy difference between the two qubits you want to entangle.

A gate set is universal when you can perform single qubit gates and two qubit gates on all the qubits. As just described, this is the case for spin qubits.

*A qubit-specific measurement capability.*

When a quantum algorithm is done, you want to know what the outcome is. To know the outcome, you need to measure the state of the system (e.g. all or a part of the qubits that were used for the experiment). When performing a quantum measurement, the wavefunction of the qubit is collapsed. A wavefunction of one qubit could look like:

This function describes the probabilities of the electron being in 0 or 1. In this case there would be 10% chance of a 0 and 90% chance of a 1. The collapse of the wavefunction means that, after a measurement, it will become 1 or 0 depending on which state you measured. A quantum algorithm typically starts by making a full superposition. When you have a lot of qubits, this would mean that you are in all the possible states at the same time (e.g. for 2 qubits, ). Then some computation is done, which should yield one result. The power of quantum computation is that you can evaluate all the possibilities at the same time. The hard thing is to describe your problem in such a way that you have a high likelihood of measuring the right outcome and a low probability of measuring bad outcomes.

Measurements for spin qubits can be done using the Elzerman method. It is quite similar to the initialization. When manipulating your qubits there is a big barrier between the reservoir and the spin qubit (single electron). When an electron is in spin up, it will be able to tunnel out into the reservoir. When this happens, there is for a short while no net charge in the place where the qubit was. After this, a spin down will tunnel back in, as we saw before. Presence or absence of charge is a property we can measure. A charge detector is placed close by the place where the electron can tunnel out. This sensor will give a change in signal whenever you have a spin up. In case of spin down, this charge sensor will not respond since no electron jumps out. Note that this readout method also directly initializes the qubit in spin down.

In conclusion, going through these 5 criteria shows that spin qubits have good potential for quantum computation. However, spin qubits are currently not the front runner in the field. That honour belongs to superconducting qubits, which are being pushed hard by IBM and Google. Superconducting qubits are running ahead because they are easier to make, tune and interconnect. The fact that superconducting qubits can be “easily” interconnected, allows for convenient schemes for quantum error correction, which is essential for bigger quantum computers (e.g. >> 10 qubits). But as for spin qubits, the real challenge for superconducting qubits is also the scale up. For superconducting qubits, the control, wiring and cooling of bigger devices becomes much harder, which might make it hard to go beyond a few thousands of qubits.

I suspect in the coming year(s), that spin qubits will reach a quality (e.g. fidelity of operation) similar to one demonstrated for the transmons, today. This will be partially due to the availability of better devices (e.g. Intel made) and improvements in the way we do gate operations.

In most experimental realisations of spin qubits, the qubits are placed in a row. This is not ideal for large scale operation, as it is not convenient to make two qubits interact at the ends of the spin chain. One of the next big steps for the spin qubit community would be to investigate the feasibility of fabrication and operation of 2D arrays of spin qubits.

Stephan is an experimentalist in the Vandersypen group working towards the first logical qubit in silicon. In his free time he is passionate about eating/making food and bouldering.

]]>**by Helsen, Kroll, Rol and van Dam**

The phone was ringing in the lab, Sophie let it ring a few times before looking up from her experiment. She was a bit annoyed until she realized what time it was, almost midnight and she had only just got the experiment running. She picked up the phone, half expecting to hear her advisor when she heard her mother’s voice:

“Sophie, we were worried about you, you didn’t pick up your phone and it has been almost four hours!”.

“Don’t worry Mom, I’ll be right there, I just have to set up a measurement run overnight, otherwise the experiment will be doing nothing over Christmas!”

“But dear, like you said, it’s Christmas, you promised you’d be here! You know how Granny has been looking forward to seeing you.”

“OK, OK I’ll just start what I have now, but I still have to refill the traps. I’ll be there in 10 minutes”, she said as she hung up the phone.

As the nitrogen traps overflowed, a haze covered the floor and she started to feel a bit dizzy…

Suddenly she felt of a cold touch on her shoulder. When she looked behind her the lab was gone and she was standing in a large lecture hall. She saw a man with a lion’s mane standing in front of a blackboard.

“On the program it says this is a keynote speech–and I don’t know what a keynote speech is.”

He then proceeded to lay out the idea of using physics to simulate physics, an idea so obvious in retrospect that it seems surprising that it took decades before people realized how extremely useful this could be. Sophie couldn’t help but notice how inspired all these theorists were with these ideas, but also how far removed this idea was from her daily experiences in the lab. She was still struggling to get her individual qubits to do what she wanted, let alone put them all together to simulate a room temperature superconductor.

She raised her hand to ask a question and to her surprise he looked straight through her. He continued talking about quantum mechanics, about how trying to control individual particles used to be absurd, a philosophical curiosity, but today people are experimenting with methods that can sense individual atoms. There is nothing fundamental that prevents us from choreographing the dance of large ensembles of individual particles in minute detail, he stressed. Annoyed, she raised her hand and waved at him.

“Annoying isn’t it, when they can’t see you.”

Startled she looked aside and saw three ghosts. The first was Richard Feynman, who at the same time was delivering the keynote that was not a keynote.

“The reason I took you here Sophie, is to show you where it all started and to share with you the dreams we had.”

“But also, to introduce you to Andrea and Steve. ”

Andrea looked a bit too stiff for her tastes, an obvious academic not entirely comfortable with her transition to industry, quite reminiscent of her own advisor.

She had a bit more trouble placing Steve, who seemed only a few years older than her but looked nothing like a typical PhD student with his beanie and his goggles.

A dark haze quickly filled the room, and Sophie felt some hands gently land on her shoulders and guide her away. A voice beside her said,

“I am the ghost of the quantum present. My name is Andrea, and if you don’t recognize me that is not so surprising. I’m not real, more of an amalgam of your subconscious – regardless, I am here to try and show you something you know but have not yet realised.”

A new scene faded into view. It was Sophie’s very own lab, just a few days ago. People were milling about giant refrigerators while racks of computers merrily bleeped away. It was right before the Christmas break so people were working feverishly trying to get as much work done as possible before taking some well needed rest. However, this wasn’t the only source of activity, in the back Sophie’s professor was showing a group of important looking people in suits his lab space. The people looked impressed as he expounded on how quantum computing was going to change the world forever.

“See this is exactly what I mean,” said Sophie. “We are promising these people the world, and they are buying every second of it but none of it is there yet, and we don’t know for sure if these quantum computers will even work as advertised.”

Sophie had said it more angrily than she meant to, luckily the people around here couldn’t hear nor see her.

“But you see” said the Ghost of Quantum Present, “It’s like this with any new technology. First people dream of something new, something grand. And then when the time comes to actually build it, they sometimes get a little ahead of themselves. But it usually turns out alright in the end.”

“But it’s all just hype!” Sophie sneered, “Think about it. We don’t even know if the error correction schemes we have right now will actually work. Sure they work in theory, but there are all kinds of assumptions that may not be validated in a real quantum computer. And then even if they do eventually work, how do we know that we’ll actually be able to solve all of the problems we say we can solve? Making high temperature superconductors is going to be complicated even with a real quantum computer”

“Sure”, answered the Ghost, “But this is always the case with new technologies. When people started building classical computers half a century ago they couldn’t possibly foresee where they would end up, and what applications would actually turn out practical. We didn’t end up getting flying cars, but we got the internet instead. And it’ll be the same with quantum computers.”

Sophie grumbled. “Color me skeptical” she said.

She fell silent and stared intently at a particularly complicated looking rack of equipment. Suddenly she felt another cold touch on her shoulder.

“Maybe I can be of help”, said the Ghost of Quantum Future, with the uncharacteristic beanie and goggles. “Allow me to lift up the curtain a little bit and show you a glimpse of the future.”

Sophie saw her own lab disappearing momentarily in wave of mist. Two big doors appeared in front of her. She took a deep breath and moved to push against the doors; they slided open immediately before she had the chance. Quietly she entered the room, hoping not to be noticed by the many people working there, staring at their screens.

In the background hulked what was probably the biggest cryostat refrigerator she had ever seen. A quiet hissing emanates from the giant machine and a bewildering variety of lights flickered on and off.

“Pretty cool eh?” said the Ghost of Quantum Future, walking up behind her.

“Cool? This is amazing, this is so much more advanced than anything we’ve ever dreamed of.”

Sophie let her hand run over an exposed cable the size of her thigh. She was genuinely impressed.

“Yeah, it turns out that people can build some pretty crazy stuff if you give them the time and the resources,” said the Ghost.

“So what kind of calculations do they do here?” Sophie’s inner scientist tore through her previous cynicism and she spoke with unabashed enthusiasm. “Have they learned how to make high temperature superconductors? What about curing cancer?”

The Ghost snickered a little. “Oh no,” he said, “those turned out to be way harder than people originally thought. But they are doing some really great other stuff with it! You should see the size of the cables that connect this thing to the rest of the internet.”

“You mean this thing runs on the net?” Sophie was really excited now.

“Oh yeah, people are logging in all the time. Sending Schrodinger cat pictures to each other. Okay that was *mostly* a joke.” He chuckled: “Turns out that if you give people a big enough quantum computer, a lot of them come up with uses for it you wouldn’t believe! For instance they made this…” as he wildly gestured towards one of the computer screens where a complicated visualisation showed an intricate dance of coloured shapes arranging and rearranging themselves.

“Ahem…” coughed the Ghost of Quantum Past loudly. “Oh right, I’m not supposed to tell you about that. Time paradoxes and stuff, you know how it is” said the Ghost of Quantum Future. “But anyway, you should probably be getting back to your time! There are some people who are waiting for you!”

He started waving goodbye and the room around Sophie started filling with a now familiar haze.

“But wait” she yelled,” I have so many questions!” But as she spoke she felt the world dissolving around her and she started falling through the newly created darkness.

She woke up lying flat on the floor. Her notes were scattered all about her and a pen was painfully pressing into her left arm. “I must have fallen asleep” she thought, “was it all just a dream then?” She started gathering her notes, sighing at the work that still needed to be done when she heard a ghostly voice coming seemingly from nowhere.

“It wasn’t a dream Sophie, I hope you have learned something from our little trip. Also, please call your grandmother and tell her you’ll be right there. Your work can wait until after Christmas. Gooodbyeeeee.”

From all of us in the QuTech blog we wish you all a Merry Christmas and a Happy New Year!

]]>**By James Kroll**

Research in academic is a tough, gruelling but ultimately rewarding job (otherwise we wouldn’t work so hard at it!). Usually if you ask a scientist about what it is like to work in research, you will be subjected to a coffee fuelled rant about tiresome data analysis, demanding students and endless paper preparation. Unless you catch us in an unusually good mood we won’t take the time to talk about the many things about our job that we genuinely enjoy.

Perks of the job

On that topic, there are a few major perks to being a research scientist. A large amount of business travel being one of them. This can be a double-edged sword, as not everyone is in a position where they can travel frequently, but if like myself you are a young, energetic person who wants to see the world a job in academic can be a great way to go about this – in the last year alone I was able to visit seven countries!

This post is about my most recent and longest academic trip, where I circumnavigated the world in just over 40 days (beating Phineas Fogg himself, although unfortunately I was not awarded £20,000 for doing so).

The journey started with a 10 day trip to the APS March Meeting (QuTech’s experiences were summarised here), the largest physics conference in the world. It takes place every year at a different venue in the US and aims to bring together many of the best physicists in our field from around the world. This year it was held in New Orleans, Louisiana – a city that really surprised me with its architecture, diversity and rich history. I was in attendance to present my research to as large an international audience as I could muster.

It proved to be an intense but amazing five day experience, attending presentations all day and spending the evenings experiencing New Orleans fantastic night life, cuisine and culture.

Trying to parallelise conference attendance, cultural experience and preparation for my talk on the final day proved to be pretty difficult!

The best part of the conference was that due to the sheer scale of it, with almost 8000 physicists in attendance it proved easy to constantly find interesting talks to attend. It also proved to be a surprisingly social event as I bumped into old friends by coincidence and made new ones at talks and evening events. It may also have been embarrassingly exciting to finally meet the authors of some of my favourite papers.

My talk was in the final session on the final day – not only am I impressed I managed to survive that long, but my fears that no-one would attend were unfounded – the room was packed with many of the most respected scientists in my field, and I received a number of tough questions from scientists who I have read about for years and deeply respect. Overall it was a great experience and I hope to be able to attend next year! I was even lucky enough to get a few days to see the real Louisiana and the Deep South.

The next leg of my trip required me to fly for another 20 hours in the opposite direction from home for some international research collaboration. Where to? And with whom? For that you will have to wait till next time, on the Exciting Adventures of James: Research Candidate.

**James Kroll **– I am James, an experimental physicist hailing from Scotland. I work in the topological quantum computing roadmap of Leo Kouwenhoven, as it requires an exciting mix of condensed matter physics theory, experimental cryogenics, electrical engineering and computer programming – all things that I somehow enjoy. If I’m not in the lab, you will most likely find me cycling somewhere or reading. Or eating. That’s a pretty important part of my life.

**By Christian Dickel**

In a series of blog posts, I want to introduce the bread and butter of the DiCarlo group within QuTech: Studying quantum effects in superconducting electrical circuits. In the title, I suggest that we are building artificial atoms, but that depends on the definition of “atomness”. I hope to give the reader some insight to judge for him or herself whether our work comes short of this or goes beyond it. Also, I want to convey some of the amazement I feel working on a subject that brings together electrical engineering, superconductivity, and quantum mechanics in its purest form.

This blog post is rather long, but I have marked non-essential sections with a *.

Most people know what an electrical circuit is. Many people even understand roughly what the diagrams that we use to represent them contain: resistors, capacitors and inductors. Here, it is important that for scientists and engineers, some diagrams are not merely illustrations but really have a precise mathematical meaning. The circuit, together with the parameters, corresponds to a mathematical object. In the case above, the circuit is made of an inductor and a capacitor.

Many people also have some image, that they associate with atoms. The image I chose here already contains some information about a specific atom: the oxygen atom. However, it does not have a precise mathematical meaning like a circuit diagram. It shows eight electrons (small black circles), eight protons (white circles) and eight neutrons (grey circles). The negatively charged electrons orbit around the positively charged nucleus like planets around a sun, only with electromagnetism instead of gravity as the attractive force.

The notion that everything is made of atoms is mainstream today, unchallenged even by religious fanatics or rogue scientists. Atoms are tiny (around 0.000 000 000 1 m in diameter) and behave according to the rules of quantum mechanics – supposedly a hard to understand theory. How can an electrical circuit behave like an atom?

What is “atomness”? Certainly, electrical circuits are big compared to atoms and they are not the elementary building blocks of our world. What circuits and atoms have in common is that they both are governed by the laws of electrodynamics. But the artificial atom analogy has to do with quantum behavior, which in case of atoms first manifested in spectral lines and the fact that atoms exist at all. Electrodynamics would predict that there is no stable way that electrons can orbit the nucleus. Moving charge has to radiate electromagnetic waves (light) and this would cause the electrons to lose energy and spiral into the nucleus. This caused great confusion and unease amongst the physicists around 1910. At this point, they had found that different chemical elements emit and absorb light at specific frequencies (or colors, for the layman). Below the diagram representing oxygen, is the spectrum of oxygen, whose lines at certain colors are frequencies that oxygen atoms interact more strongly with.

Scientists knew these spectra for many different elements and knew atoms were stable, but there was no theory explaining it. This changed in 1913, when Niels Bohr postulated that the electron could have stable orbits around the nucleus when the angular momentum of the electron was equal to multiples of – bringing in the Planck constant . Planck had introduced this constant to solve another big problem at the time: the thermal radiation of blackbodies. “Classical” theories (we use the word classical as the opposite of quantum in this context) predicted this to have infinite energy. Planck postulated that energy is not exchanged in arbitrary amounts but in packages he called “quanta”. The new constant was related to the size of these energy packets. Bohr extended the “quantization” from light to matter. He used and the known masses and charges of the electron and nucleus to calculate the spectrum of hydrogen and got it right to impressive precision. This was the start of explaining the spectra and the stability of atoms and kicked off a golden age of physics.

The spectral lines of atoms are only part of the story. At the time Planck and Bohr just postulated it, but the discrete exchange of energy is an essential feature of quantum mechanics. With it also emerge the more counterintuitive quantum effects, such as superposition and entanglement mentioned in previous blog posts. The idea of pushing electrical circuits into the quantum regime is really aiming for a new platform where quantum effects can be engineered and studied with current nanotechnology.

Long before quantum mechanics, the theory of electrical circuits was already known – Kirchhoff had layed out the rules to calculate voltages and currents in an electrical circuit in 1845. The circuit diagram above shows the circuit of a harmonic oscillator. It is the electrical equivalent of a pendulum where potential energy (height) and kinetic energy (speed of the pendulum) are replaced by the energy in the capacitor (potential) and inductor (kinetic). The resonance frequency is given by . At that frequency, the circuit will be more prone to energy exchange, a principle we use in for example antennas. This is similar to a spectral line where the atom exchanges energy, but it is not the same: most electrical circuits behave classically; currents and voltages do not show quantization. We do not notice our antennas sending and receiving tiny packages. Pushing circuits into the quantum regime is not easy, but another stunning experimental discovery would ultimately lead to the artificial atoms I work with: superconductivity.

Why do we not see quantum mechanical effects when we measure the resistance of a wire? In Bohr’s hydrogen atom, there is a single electron spinning around the nucleus in a vacuum. In the electrical circuits described by Kirchhoff’s Laws, charge is treated more like a fluid. While the total amount of charge is conserved, charge, in this theory does not come in discrete chunks. The reason is of course that there are generally so many electrons flowing through a circuit that their discrete nature disappears. In the limit of many interacting particles or degrees of freedom, quantum mechanical features can be hidden. One of the ways to make them appear is to work with circuits that have very few electrons.

The second key parameter that can hide quantum behavior is high temperature. You might ask: High compared to what? Atoms show quantum behavior at room temperature because their spectral lines correspond to very high temperatures. Remember, the light we use to see is emitted by the sun which burns at 5500° C and most atoms around us are in their ground states. The electrical circuits in our lab have energy transitions that lie much lower. Therefore, to see the quantum behavior, we have to cool our systems down to a fraction of a degree above absolute zero (−273.15° C). This might seem ironic to people who think of quantum mechanics as random, because temperature is related to energy stored in the random movement of atoms. However, this classical randomness related to temperature is quite distinct from quantum randomness. It can simply smear out the clear spectral lines of quantum mechanics such that they will look more like a classically expected continuum.

Lastly, there is energy loss. Electrical resistivity (which generally can strongly depend on temperature), is due to the electrons who carry currents losing some of their energy to the atomic lattice of the material and heating the material in the process. The electrons do not really move through metals like fish through water but rather like a ball through a pinball machine. Loss of energy can also hide quantum effects. If an electron, an atom or a circuit is in a superposition and loses its energy, it usually falls into the ground state. The energy has to go somewhere, for example as light or heat into the environment. In fact, the three points I make here are quite interrelated and together form the big picture of the transition between classical and quantum physics.

As if to solve all our problems at once, in 1911 Heike Kammerlingh Onnes, then a professor in Leiden, discovered that the electrical resistance of metals can drop to zero at very low temperatures. This phenomenon was puzzling, because it is not a gradual reduction in resistance but a sudden jump at a temperature that depended on the material. Some metals like aluminum, mercury, titanium, and lead show this behavior, while others such as iron, copper, gold, and silver do not. The theoretical explanation for this phenomenon took almost 50 years and again involved quantum mechanics.

The hand-wavy explanation is that there is effectively an attractive force between the electrons which conduct the electricity, instead of the repelling force one would expect because negative charges repel each other. This is due to the Coulomb repulsion being weakened by the many conducting electrons actually shielding each other from it. At the same time, the lattice vibrations in the crystal made of the nuclei can lead to an attractive force and under certain conditions the attraction wins. The attractive interaction between the electrons makes them bond together in pairs named Cooper pairs after one of the theoretical physicists who developed this particular theory of superconductivity.

The Cooper pairs behave different from the original electrons, again because of quantum mechanics. While the electrons were bumping into the crystal lattice (specifically defects in that lattice), the Cooper pairs do not scatter on these defects. That is because the two electrons of the Cooper pair are not actually spatially close to each other which they would need to be to both scatter on a given defect or lattice vibration. Quantum mechanics does not allow the Cooper pairs to scatter without breaking up, which requires a minimum energy cost being payed. As long as this energy is not available they cannot scatter. They therefore do not experience energy loss while still transferring charge from A to B, thus leading to zero electrical resistance.

As soon as the temperature, which is the mean random energy available in the degrees of freedom of a system, exceeds the energy of the Cooper pair bond – also called the superconducting gap, they can be broken up into their constituent electrons and the electrical resistance will come back. That’s why superconductivity has a threshold temperature. But the threshold is sharper than would be explained by that alone.

In addition to the zero electrical resistance, superconductivity has additional consequences, which I will struggle to illustrate here. The electrons themselves are particles called fermions (protons and neutrons are also fermions by the way, all building blocks of everyday-matter are fermions), which have the weird property that two of them cannot be in exactly the same state. This realization together with postulating another internal degree of freedom – the spin of the electron – ultimately explained the periodic table of elements and as such might be held amongst the highest scientific achievements of all time. The fermionic nature of electrons is also essential to describe semiconductors and metals.

However, two electrons bound by an attractive force form something much like an atom (think hydrogen atom and substitute the proton with another electron). This atom doesn’t behave like a fermion, but like a different type of particle, the boson. While two fermions cannot be in the same state, bosons can be, in fact due to quantum statistics they prefer it. The textbook example of a boson is the photon. Photons will bunch together into the same quantum state in phenomena such as lasing. In the superconductor, all Cooper pairs collapse into one massive ground state forming a so-called Cooper pair condensate. Basically the electrical conductivity is mediated by one collective quantum state of all Cooper pairs. This solves the problem of having many degrees of freedom because the Cooper pair condensate, while consisting of many particles only has two degree of freedom. An island of superconductor is characterized by two parameters: The number of cooper pairs on the island and the superconducting phase of the condensate. Writing down the state of a normal-metal island is quite a bit more complicated. Probably this blog article is too long and confusing but this is one of the most amazing features of quantum mechanics in my opinion and I wanted to share it!

In an atom the transition from one state of the other is actually the transition of an electron from one orbital to another. Electrons are fermions as mentioned above meaning that two of them cannot occupy exactly the same state. Our Cooper pairs do not behave like that and the states of our circuits are not fermionic excitations like in atoms. This actually means they exhibit less complex behavior, because in an atomic multi level system the transitions from one state to the other depends on if this state is already occupied. In our case this hardly matters, because – while I always talked about artificial atoms here – we usually only use two-levels of a quantum system – qubits. That is because we generally build devices that resemble digital information processors. But the higher levels do play a role in a multi-qubit context.

One can also make traps for single electrons in semiconductors, which are closer to artificial atoms. Essentially this is just making a box which can be occupied by one or more electrons. In QuTech, these boxes for electrons, called quantum dots, are also studied . One can also model them as circuits. They are operated in the few-electron regime and show fermionic behavior. The electronic cloud of an atom usually extends about 0.1 nm, while quantum dots can be on the order of 100 nm, so they are already larger. In comparison, our superconducting circuits are usually well in the regime – most features of our chips can be seen with the bare eye. Superconductivity is essential for pushing these huge circuits into the quantum regime. To conclude, while the excitations in our systems show quantum behavior, they are arguably a bit different from the excitations in atoms or quantum dots. However, in superconducting circuits parameter regimes can be achieved, that can be difficult to reach with atoms or quantum dots. The different ways of engineering quantum systems are really quite complementary.

Using superconducting materials, we can now make a dissipation-less electrical circuit. The degrees of freedom of this circuit will behave quantum mechanically, if we chose their energy scales to be above the energy scale associated with temperature. But superconductors require very low temperatures anyway. In our case, most of our experiments take place around 20 mK. A mind-blowing 20 milli-degrees above absolute zero. This is a factor 50 below the transition temperature of aluminum, our workhorse superconductor. We’re safely in the superconducting regime and quantum mechanics will only allow photons with energies above (twice) the superconducting gap to cause dissipation.

So the energy of our superconducting circuit atoms is bounded by temperature from below and by the superconducting gap from above. The critical temperature of aluminum is about 1.2 K, which corresponds to a gap of about 40 GHz. Transitions close to that gap would be more prone to quasiparticles and radiation close to the gap degrades the superconductors performance. We usually don’t fabricate systems with transitions far past 8 GHz, which is on the safe side. The operating temperature should also be significantly below the superconducting gap. With a Helium 3 dilution refrigerator, which itself relies on quantum effects, we can cool macroscopic systems down to about 20 mK. This corresponds to a frequency of ~400 MHz. At a factor of 10 above this, thermal excitations in the system should be largely frozen out. Usually we do not operate our artificial atoms below 4 GHz.

The 4-8 GHz band is called the C-band by electrical engineers. Coincidentally the higher frequencies of common Wi-Fi also fall in this band. It has been used for a long time in satellite communications, such that there are good coherent sources of radiation available, which we use to manipulate our atoms. Also, at these frequencies there are still very good amplifiers available. At higher frequencies, amplifiers tend to become more noisy, equipment becomes more expensive and residual dielectric losses increase.

Within these constraints, we can make our artificial atoms. There are some additional constraints due to the materials we use. Mainly it is smart to avoid small charge islands where the fluctuating charges in the environment can have a big impact. But largely we get about one octave in frequency where we can aim our atoms’ transition energies. We can also engineer coupled atoms and microwave resonators.

This is how capacitors and inductors become the tools of quantum engineers. Another essential circuit element is the Josephson junction, but this is another story and shall be told in my next blog post. Atomic physicist have to live with discrete numbers of electrons, protons and neutrons. They have to find a suitable element and possibly isotope for a given application which can be quite non-trivial. We have to draw a circuit and do some calculations to get the right atomic properties. We don’t only make circuits that resemble atoms but we also make circuits for specific applications: We use amplifiers based on quantum circuits that only add the minimal noise allowed by quantum mechanics, or we make microwave lasers. Below, you can see one of my chips, as a drawing and the finished chip I made in the cleanroom. It is a 7mm by 2 mm chip and serves as a teaser for the next, more concrete blog article on superconducting circuits.

This is a very simplified account. Truth be told, one cannot fully understand all these phenomena without having had years of physics education. While it is a privilege to work on problems that combine so many fascinating effects and that required several generations of ingenuity, the downside is that our work will remain obscure to most people. We usually circumvent this problem by only telling bits and pieces of our work to the general public and presenting them as a complete picture. Here, I tried to give a brute-force account of the essential effects, but I still had to leave many essential logical steps unexplained and probably, I omitted some as well. But don’t be fooled, there is a coherent logic that underlies our scientific work and that ties everything together. If you want to understand these things in more detail, look at hyperphysics for more detailed explanations of different effects that I mentioned. More specific introductions to superconducting circuits can be found here and here. As a take away-message, imagine your job was designing and making your own atoms.

Editors Note [30/06/17]: changed treatment of superconducting island degrees of freedom to be more accurate at request of the author.

Chris came to the Netherlands for the food and the weather but stayed for the quantum computer work. Apart from work he enjoys playing music with friends, ranting and soap-boxing.

]]>**By Jonas Helsen**

One of the things that is often repeated about quantum computing is the idea that a quantum computer is somehow more powerful than regular computers because, when considering a problem it can “try all possible solutions at once”. Let’s get this out of the way first and say that this is not exactly the case. While we would very much love a computer that tries all solutions at once (this would be extremely useful) quantum computers sadly aren’t quite this powerful. Of course, as with all good clichés it does contain a grain of truth. In this blog post I will try to explain in a (sort of) simple way what makes quantum computers more powerful than classical computers.

In order to compare classical and quantum computers I’m going to start by explaining a simple game (for those interested, this game is based on the fabled Deutsch-Josza algorithm, one of the earliest quantum computing algorithms). This game is reminiscent of those street games you can play where you have to guess which cup hides the ball. In this case your friend, who for some reason owns a large amount of cups and red and green balls challenges you to the following: She has lined up a number (let’s call it N) cups and under each cup she has hidden either a red or a green ball. Now she promised you that she did one of two things. Either she has hidden equally as many red and green balls, or she has hidden only red balls (or only green balls). It is your task to figure out in which of these two situations she has put you. To make things even more difficult, you can only lift one cup at a time and you must be certain of your answer (all red, all green, or fully mixed) after lifting as few cups as possible. (The less cups you lift the better you score). Now it won’t be hard to convince yourself (if you feel so inclined you can take a piece of paper and try figuring out some cases) that you must lift at least half of the cups in order to be absolutely sure you are in one of the three cases. Now this isn’t the end of the world but if your friend brings a lot of cups you might spend a lot of time lifting them one by one before you get anywhere. We would like to do better, and with quantum we can!

At this point I would like to get to the core of an important aspect of quantum computing (the one that inspired the “all possible solutions” adage) without bringing in the full complexity of quantum computing. In order to do this I will define a magical machine designed to solve the ball and cup problem in a quantum-y way. This machine might seem a little contrived at first but bear with me. This machine will take the shape of a long tube with N (the amount of cups) holes in the top. In these holes your friend will put her green and red balls, conveniently covering up the holes with lids so you can’t just look inside. It also has a number pad on the side that allows you to punch in numbers from one to N and the machine will then tell you the color of the ball in the corresponding hole. However it does so in a very specific way. If the ball is green it will output the number “1” and if the ball is red it will output the number “-1”. For the sake of the argument your friend will consider a single use of the number pad the same as lifting a cup. Hence the goal of the game is now to solve our friends problem with the smallest possible amount of number pad uses. If this just seems to you like an elaborate way of just lifting a cup and checking the color, you would be right, but we aren’t done describing the machine yet!

Next comes the “quantum” part of the machine. It turns out that the number pad also allows you to ask for multiple numbers in a single use, but only in a very specific way! If you type in multiple numbers ( For instance 1, 2 5 and 7) the machine will tell you the absolute value^{*} value of the sum of the individual numbers it would have generated for those locations. For instance if you asked for locations 1 and 2 and locations one and two contain red balls the machine would output |-1 + (-1)| = |-2| = 2. Note that if both balls are green it would also output 2, whereas if the balls were of different colors (red-green or green-red) the machine would output zero. This means that the machine will never tell you what the color of the balls are! This hardly seems like an improvement but it turns out we can exploit this curious behaviour in a clever way to answer our original problem!

Let’s restate the problem. Our friend has put N red or green balls into the machine with the promise that they are either all green (or all red) or half green-half red (assume that N is even). It is or job to figure out in which situation we are by using only a single query to the machine. This means we get to type in a single sequence of numbers and get a single output number in return. Inspired by the odd properties of the machine we propose the following solution: we simply type in all the numbers from 1 to N! I will leave it to you to convince yourself that if there are equally many red as green balls the machine will always return exactly zero and that if the balls are all green or all red the outcome will be the number N (this is fairly easy to check for yourself by picking N to be a small number, say 4, and trying various ball combinations). This outcome allows us to perfectly distinguish between the two situations using only a single query to the machine! Note also that the machine didn’t actually tell us anything about the colors of the actual balls! We for instance don’t know if ball number seven is red or green. The machine will also never give us that answer unless we specifically ask it to do so (which will cost us a query). Hence, while the machine has in a way “looked” at all balls, it will never tell us what it has seen, it only tells us some global property of all balls (are they all the same or not), and even then only if we ask the right questions. A similar thing is true for actual quantum computers. In a quantum computer we can input quantum superpositions of different states into the computer but if we ask it to produce an answer (this would then be a quantum measurement) it would never give us more than a single number which is related to all the different states we entered in a superposition.

So that should give you a very rough idea of where “trying all solutions at the same time” comes from. But again, that’s not exactly what’s going on. The truth is much more complicated and it is precisely that which makes designing algorithms for quantum computing so challenging.. And so interesting! Quantum computers will probably be able to answer amazing problems, but only if we learn to ask the questions in the right way.

^{*}The absolute value of a number, denoted | |, turns negative numbers into positive numbers but otherwise leaves it unchanged. So for instance |-2| = 2 but also |5| = 5.

Jonas Helsen is an aspiring theorist in the Wehner group where he works on verifying quantum computers. In his free time he enjoys improvisational theater and pretending to be a superhero. He likes the Netherlands but wishes they wouldn’t put peanuts in everything.

]]>*by Filip Rozpedek *

You have probably already heard about entanglement. Entanglement is this fascinating phenomenon, in which two distant objects can manifest correlations, even if they are far far away from each other. You may have also heard that remote entanglement is a necessary ingredient for many quantum information processing tasks. For example, in quantum cryptography, two people who hold entangled particles can use those correlations to obtain shared secret keys, whose security is guaranteed by the laws of quantum mechanics. Today, we will not discuss how to use remote entanglement, but rather, what to do if our entanglement is too weak.

Unfortunately, fully entangled states which are perfectly correlated are a great idealization and from an experimental perspective almost impossible to create. In general, there can be many reasons for this, e.g. our experimental equipment isn’t perfect or we cannot maintain our quantum system long enough. All those things combined lead to various forms of contamination of the entanglement. That is, the correlations become weaker and completely diluted in a mixture of various other quantum states.

So what do we do with those so-called “partially entangled states”? Let us say that two parties working at QuTech, whom we call Alice and Bob, share those partially entangled states and would like to use them to generate shared secret keys. Let us also say that their experimental setup allows them to produce partially entangled states very fast, but the amount of entanglement in each of them is insufficient to generate shared secret keys. It is known from Quantum theory that it is not possible to increase the amount of entanglement in a given quantum state by only performing operations on the entangled particles locally and exchanging classical messages. It seems that there is no choice for Alice and Bob, but to go home without a key.

Fortunately, Alice and Bob have two friends (well maybe they have more, but this remains unknown), called Alessandro and Bruno, who are the experts on decoherence (as can be easily verified in this article). It turns out that Alessandro and Bruno have a special skill that could rescue Alice and Bob. Namely, they can perform distillation.

In Fort William, Alessandro and Bruno distill alcohol for the production of whisky. So what do Alessandro and Bruno exactly do? Well, they start with a form of beer that is stored in the massive stills as the ones on the picture. This beer is a mixture of pure alcohol (8-10%), water and some other substances.

For the high quality whisky Alessandro and Bruno need to extract this pure ethanol from everything else. Fortunately ethanol boils at around 78.5 degrees Celsius and so by heating the stills to this temperature, the alcohol can be evaporated. Later, the alcohol condenses outside of the stills and one is able to separate it from the “contamination” that shouldn’t end up in any high quality whisky. Well, this is a big approximation, because to separate alcohol from the water one requires to perform multiple rounds of distillation.

Alice and Bob became very interested in the work of Alessandro and Bruno because they started to suspect that entanglement is a bit like alcohol. Weakly entangled states are “contaminated”, but it may be possible to use the techniques of Alessandro and Bruno, to distill the entanglement out of the mixture of all those different quantum states. Hence, Alice and Bob invited Alessandro and Bruno to QuTech and asked them to apply their precious skill to their shared partially entangled states. You might be puzzled now. How will Alessandro and Bruno be performing entanglement distillation? Will they heat up the entangled particles? It’s a bit different.

In entanglement distillation Alice and Bob will use multiple copies of the partially entangled states. The hope of Alessandro and Bruno is that although each of these states has some, small amount of entanglement, it is possible to mix those copies together, and eliminate the contamination by extracting only the entangled part, so that at the end they obtain a single highly entangled state. Guided by their experience in Fort William, Alessandro and Bruno expect that for realistic procedures, a single run of such a distillation would not be enough. Similar to alcohol distillation, where the total amount of alcohol cannot be increased, entanglement can be concentrated into a smaller number of copies, using only local operations and classical communication, without increasing the total amount of entanglement. This shows that, unfortunately, Alessandro and Bruno are not superhuman and remain bounded by the laws of nature.

Having absorbed these abstract concepts you might think: “What a story!”. To illustrate these methods we will focus on some simple examples. Alessandro and Bruno are very interested in protocols that can be implemented with current technology. Therefore they will not consider any operations for which Alice and Bob would need a large number of partially entangled states.

Let us look at the methods of entanglement distillation applicable to small systems. Firstly, let’s say that Alice and Bob can only generate two-qubit states in their lab at QuTech. Specifically, there exist simple distillation procedures in which Alice and Bob perform simple local operations on two shared copies of partially entangled states. Those operations include a so-called controlled NOT gate. This quantum gate is very analogous to its classical counterpart. In the classical scenario depending on the value of the first bit, the second bit is flipped or not. Now in the quantum case our first “controlling bit” can be in superposition of 0 and 1, in which case in superposition we flip and not flip the second bit.

In our distillation procedure Alessandro and Bruno instruct Alice and Bob to apply this controlled NOT gate to the two qubits that each of them have. The qubits are chosen such that the controlling qubit of Alice is entangled with the controlling qubit of Bob and the same is true for the target qubits. At the end of the whole procedure both Alice and Bob measure their target qubits. Finally, conditioned on the measurement result, which they report to each other over the phone, they either keep or discard the resulting shared two-qubit state. That is e.g. if they both obtained the same outcome they keep the state and discard it otherwise (the specific details depend on the chosen procedure, that should depend on the type of partially entangled states that Alice and Bob share). The result is that successful measurement output always results in a state that has more entanglement than each of the two individual input states. The cost is that this success is normally only probabilistic, since the measurement outcome must pass the described test. However, since Alice and Bob can generate their states at a very fast rate this is not a problem for them.

Unfortunately, after careful examination of their experimental setup, Alice and Bob realize that they can only maintain one such state at a time. So the ability to concentrate entanglement from multiple copies into a single copy of the state is unfortunately of no use for them at the moment. We’ve said that it is not possible to increase the total amount of entanglement using only local operations and classical communication. Fortunately probabilistic procedures come to the rescue. There is nothing that forbids increasing entanglement only probabilistically, such that on average it does not increase. In fact there exist so called filtering protocols where by just performing a smart measurement on their single qubits, Alice and Bob can from one copy of some specific partially entangled states, obtain one more entangled copy, paying the cost of probabilistically ending up with nothing. Hence we see that Alessandro and Bruno can help Alice and Bob in their great endeavor so that Alice and Bob can go home with a (shared) key.

Filip Rozpedek is a QuTech theorist.

His research is in the topic of quantum networks and quantum communication, where he looks for efficient ways of distributing quantum states and quantum entanglement over long distances.

He is specifically interested in schemes that can be realized with the current technology.

At work he enjoys counting bad jokes of his office colleagues.

Outside of QuTech he normally dresses up as illustrated on the picture and practices his Scottish Country Dancing skills.

*by Christian Dickel*

It is an honor to write the first blog post here and being conscious of that certainly influenced what I was going to write about. They say write what you know, but this is a blog so I’m going to write what I think. The blog will hopefully be a place for opinions and discussions. So I’ll begin with a question:

**Do physics institutes need blogs?** Certainly it is a neat additional way to communicate with other scientists, especially to share more provocative thoughts and give people a chance to discuss in the comments. But science is kind of a gated community and a blog is a nice way to open it more. For communication with the rest of society, journalists often come in whenever some piece of science has an air of general interest. But especially in a field receiving a lot of interest and a lot of funding from the public, we should try to explain what we do directly to anybody who is interested enough to end up on our website. A blog is a chance for us to share and discuss our perspective on the story of quantum computing as it is being written.

There are news article on quantum computing almost weekly somewhere on the internet and one can use them to follow the story of the quantum computer. But the news has a certain inertia and a need to fit complicated arguments into a single sentence or paragraph. Some of the one-liners are productive simplifications, but they can also be misleading. Exploring all the misconceptions about quantum computing requires more than one blog article. I considered going through the list found here and fact-checking it, but this blog article would not have been very serious then. I thought it better for the first blog article to be a link from the past to today and focus on a single aspect that annoys me in the way the quantum computer story is told: I will try to give a more nuanced view on the relationship between the classical and quantum computer. Maybe later there will be more blog articles on other common misconceptions about quantum computers.

The quantum computer is usually introduced with the statement that it can vastly outrun classical or even supercomputers on certain problems. Quantum computation is heralded as a disruptive technology that will revolutionize computation. The classical computer is only mentioned as the boring status quo. Of course I believe that quantum computers will be awesome, otherwise I would not spend so much time helping to make them happen. But having been involved in building quantum computer prototypes for almost three years, I see the classical computers that I use every day with different eyes now. Let me remind you that your computer and the server that contains this blog article exchange bits but you see words on a screen. In my day-to-day work, I look at the results of operations on quantum bits on the screen of a classical computer that controls my experiment. My classical computer in the lab is my window to the quantum world. But the quantum computer prototypes have a few qubits where we engineer all their logic gates by hand. We work at the bottom level of computation. This is in stark contrast with the classical computers we use for design, control of experiments, data processing and simulations. Those computers have operating systems, compilers, high-level programming languages and programs. Without them our quest for the quantum computer would be impossible. That is why I want to highlight the continuity from the classical to the quantum computer rather than the revolution.

In some news stories it is pointed out that the number of possible states of a quantum computer grows exponentially with the number of qubits. The argument goes as follows: A bit can have two states, 0 and 1. Two bits can have four states: 00, 01, 10 and 11. Four bits can have eight. It is true that 10 qubits can have different readout results. But this is true also for the classical bits of a “normal” computer. When this argument is cut short here it is misleading. I think the reason for the misunderstanding is that we cannot grasp exponential growth with our intuition, so it already serves as a buzzword. That’s why we tell stories of rice grains and chess boards. The exponential scaling of the number of possible states with the number of bits is part of the classical information theory championed by Claude Shannon. Quantum information theory has similar building blocks and concepts, however, scientists are still busy understanding all the implications of quantum information theory.

The difference between quantum and classical bits is subtle and cannot be explained without invoking the counter-intuitive notions of superposition and entanglement. These special features of quantum mechanics let quantum information scale differently from classical information. It is even a bit more complicated because there is a difference between the classical information needed to represent a quantum system and the classical information that can be extracted from it. While the state of a classical system with bits can be exactly represented by classical bits, this is not true for a quantum mechanical system. The readout of the quantum mechanical system gives a binary result for each qubit, in that way a qubit is like a classical bit and the measurement of qubits gives a classical bit string of length . In fact, the famous Holevo bound states that the information that can be extracted from qubits cannot exceed classical bits of information. However, during calculations on a quantum computer, the quantum state has to be represented by complex numbers or twice as many real numbers. Storing each of these numbers to double precision would require 64 bits. Thus, the required classical memory to store the state of a quantum system grows exponentially with the number of qubits, which makes simulating quantum computers on classical computers impossible for large system sizes. Currently, simulating a 50 qubit quantum computer would be at the edge of supercomputer capacity, simulating a 100 qubit quantum computer seems classically out of reach. So there is a grain of truth in the “quantum information scales exponentially” argument, even though it is often simplified too much.

This example shows that explaining the difference between quantum information and classical information unambiguously is very difficult. As a community we have to decide how we deal with the many pitfalls of explaining our science to the public. News about our field generate a lot of science-fiction enthusiasm with promises of exponential speedups, solutions to impossible problems and misleading terms such as teleportation. For the record, I am not saying that the promises of the quantum computer are exaggerated. Probably a working quantum computer would exceed the expectations of its creators like the classical computer did, but there are many ways to misunderstand our field. This feeling, in part, lead me to writing this article. This is not a problem I can solve in this article, but it will hopefully be the subject of more blog articles. Here, I want to go on making my case that the classical computer is not the antagonist in the quantum computer story.

The problems we face in trying to scale up our quantum computer prototypes should also sound familiar:

**bringing up the fabrication yield for quantum bits**: Building a ten-qubit chip is harder than building a one-qubit chip. This is especially challenging for nano-scale qubits that run into lithographic limits.**solving the problems of interconnects**: Quantum bits need to be connected to each other as well as to classical control and readout equipment.**managing the heat load of the quantum computer**: Many qubits require very low temperatures at which only limited cooling power is available.**coming up with an architecture**: The growing complexity of the experiments needs to be managed. The level of automation and abstraction needs to increase along with the number of qubits.

All of these problems have been solved for generations of classical computers, but we are still testing different building blocks for quantum computers. We can hopefully solve these problems faster by bringing together academia and the classical computer industry.

Lastly, our current best scheme for a fault-tolerant quantum computer requires a powerful classical computer to run alongside the quantum computer and figure out what goes wrong in each step. This is necessary because quantum hardware is error prone. It is the complex interplay of quantum and classical information that will enable the exponential speedups of the quantum computer. Similarly, the quantum communication schemes that ideally allow for privacy guaranteed by the laws of nature combine the exchange of classical and quantum information. The classical computer parts should be as closely connected to the quantum parts as possible and need to keep up with them in runtime. Note, that the classical computer parts do not perform the same kinds of calculations as the quantum parts, they act more like a control circuit for the quantum hardware. Many qubits are rather short lived, so the classical logic controlling them has to have short latency and fast clock speeds. For this reason our institute is also developing purely classical hardware for qubit control. Ultimately, as many quantum computers operate at cryogenic temperatures, the quantum computer quest might push classical logic into that regime as well. In the process, clock speed and architecture of the classical computer would change alongside its younger quantum brother.

As quantum computer scientists, we would usually say that classical electronics will be part of a quantum computer. But for many applications the quantum computer will be used as a black box with classical information as the input and output. In those cases it might be more honest to introduce the quantum computer as a hardware accelerator technology for the classical computer, like a GPU. Once quantum computers start solving relevant problems, computers will not be judged by how quantum or classical they are but by what they can actually do. Then the news stories will change.

Chris came to the Netherlands for the food and the weather but stayed for the quantum computer work. Apart from work he enjoys playing music with friends, ranting and soap-boxing.

16-8-2016 updated version: expanded explanation of the difference between quantum and classical systems and the interaction between classical and quantum components in a computing system

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