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By Tim Palmer, University of Oxford 

We all have to make hard decisions from time to time. The hardest of my life was whether or not to change research fields after my PhD, from fundamental physics to climate physics. I had job offers that could have taken me in either direction – one to join Stephen Hawking’s Relativity and Gravitation Group at Cambridge University, another to join the Met Office as a scientific civil servant.

I wrote down the pros and cons of both options as one is supposed to do, but then couldn’t make up my mind at all. Like Buridan’s donkey, I was unable to move to either the bale of hay or the pail of water. It was a classic case of paralysis by analysis.

Since it was doing my head in, I decided to try to forget about the problem for a couple of weeks and get on with my life. In that intervening time, my unconscious brain decided for me. I simply walked into my office one day and the answer had somehow become obvious: I would make the change to studying the weather and climate.

More than four decades on, I’d make the same decision again. My fulfilling career has included developing a new, probabilistic way of forecasting weather and climate which is helping humanitarian and disaster relief agencies make better decisions ahead of extreme weather events. (This and many other aspects are described in my new book, The Primacy of Doubt.)

But I remain fascinated by what was going on in my head back then, which led my subconscious to make a life-changing decision that my conscious could not. Is there something to be understood here not only about how to make difficult decisions, but about how humans make the leaps of imagination that characterise us as such a creative species? I believe the answer to both questions lies in a better understanding of the extraordinary power of noise.

Imprecise supercomputers

I went from the pencil-and-paper mathematics of Einstein’s theory of general relativity to running complex climate models on some of the world’s biggest supercomputers. Yet big as they were, they were never big enough – the real climate system is, after all, very complex.

In the early days of my research, one only had to wait a couple of years and top-of-the-range supercomputers would get twice as powerful. This was the era where transistors were getting smaller and smaller, allowing more to be crammed on to each microchip. The consequent doubling of computer performance for the same power every couple of years was known as Moore’s Law.

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There is, however, only so much miniaturisation you can do before the transistor starts becoming unreliable in its key role as an on-off switch. Today, with transistors starting to approach atomic size, we have pretty much reached the limit of Moore’s Law. To achieve more number-crunching capability, computer manufacturers must bolt together more and more computing cabinets, each one crammed full of chips.

But there’s a problem. Increasing number-crunching capability this way requires a lot more electric power – modern supercomputers the size of tennis courts consume tens of megawatts. I find it something of an embarrassment that we need so much energy to try to accurately predict the effects of climate change.

That’s why I became interested in how to construct a more accurate climate model without consuming more energy. And at the heart of this is an idea that sounds counterintuitive: by adding random numbers, or “noise”, to a climate model, we can actually make it more accurate in predicting the weather.

A constructive role for noise

Noise is usually seen as a nuisance – something to be minimised wherever possible. In telecommunications, we speak about trying to maximise the “signal-to-noise ratio” by boosting the signal or reducing the background noise as much as possible. However, in nonlinear systems, noise can be your friend and actually contribute to boosting a signal. (A nonlinear system is one whose output does not vary in direct proportion to the input. You will likely be very happy to win £100 million on the lottery, but probably not twice as happy to win £200 million.)

Noise can, for example, help us find the maximum value of a complicated curve such as in Figure 1, below. There are many situations in the physical, biological and social sciences as well as in engineering where we might need to find such a maximum. In my field of meteorology, the process of finding the best initial conditions for a global weather forecast involves identifying the maximum point of a very complicated meteorological function.

Figure 1

However, employing a “deterministic algorithm” to locate the global maximum doesn’t usually work. This type of algorithm will typically get stuck at a local peak (for example at point a) because the curve moves downwards in both directions from there.

An answer is to use a technique called “simulated annealing” – so called because of its similarities with (annealing), the heat treatment process that changes the properties of metals. Simulated annealing, which employs noise to get round the issue of getting stuck at local peaks, has been used to solve many problems including the classic travelling salesman puzzle of finding the shortest path between a large number of cities on a map.

Figure 1 shows a possible route to locating the curve’s global maximum (point 9) by using the following criteria:

• If a randomly chosen point is higher than the current position on the curve, then the new point is always moved to.
• If it is lower than the current position, the suggested point isn’t necessarily rejected. It depends whether the new point is a lot lower or just a little lower.

However, the decision to move to a new point also depends on how long the analysis has been running. Whereas in the early stages, random points quite a bit lower than the current position may be accepted, in later stages only those that are higher or just a tiny bit lower are accepted.

The technique is known as simulated annealing because early on – like hot metal in the early phase of cooling – the system is pliable and changeable. Later in the process – like cold metal in the late phase of cooling – it is almost rigid and unchangeable.

How noise can help climate models

Noise was introduced into comprehensive weather and climate models around 20 years ago. A key reason was to represent model uncertainty in our ensemble weather forecasts – but it turned out that adding noise also reduced some of the biases the models had, making them more accurate simulators of weather and climate.

Unfortunately, these models require huge supercomputers and a lot of energy to run them. They divide the world into small gridboxes, with the atmosphere and ocean within each assumed to be constant – which, of course, it isn’t. The horizontal scale of a typical gridbox is around 100km – so one way of making a model more accurate is to reduce this distance to 50km, or 10km or 1km. However, halving the volume of a gridbox increases the computational cost of running the model by up to a factor of 16, meaning it consumes a lot more energy.

Here again, noise offered an appealing alternative. The proposal was to use it to represent the unpredictable (and unmodellable) variations in small-scale climatic processes like turbulence, cloud systems, ocean eddies and so on. I argued that adding noise could be a way of boosting accuracy without having to incur the enormous computational cost of reducing the size of the gridboxes. For example, as has now been verified, adding noise to a climate model increases the likelihood of producing extreme hurricanes – reflecting the potential reality of a world whose weather is growing more extreme due to climate change.

The computer hardware we use for this modelling is inherently noisy – electrons travelling along wires in a computer move in partly random ways due to its warm environment. Such randomness is called “thermal noise”. Could we save even more energy by tapping into it, rather than having to use software to generate pseudo-random numbers? To me, low-energy “imprecise” supercomputers that are inherently noisy looked like a win-win proposal.

But not all of my colleagues were convinced. They were uncomfortable that computers might not give the same answers from one day to the next. To try to persuade them, I began to think about other real-world systems that, because of limited energy availability, also use noise that is generated within their hardware. And I stumbled on the human brain.

Noise in the brain

Every second of the waking day, our eyes alone send gigabytes of data to the brain. That’s not much different to the amount of data a climate model produces each time it outputs data to memory.

The brain has to process this data and somehow make sense of it. If it did this using the power of a supercomputer, that would be impressive enough. But it does it using one millionth of that power, about 20W instead of 20MW – what it takes to power a lightbulb. Such energy efficiency is mind-bogglingly impressive. How on Earth does the brain do it?

An adult brain contains some 80 billion neurons. Each neuron has a long slender biological cable – the axon – along which electrical impulses are transmitted from one set of neurons to the next. But these impulses, which collectively describe information in the brain, have to be boosted by protein “transistors” positioned at regular intervals along the axons. Without them, the signal would dissipate and be lost.

The energy for these boosts ultimately comes from an organic compound in the blood called ATP (adenosine triphosphate). This enables electrically charged atoms of sodium and potassium (ions) to be pushed through small channels in the neuron walls, creating electrical voltages which, much like those in silicon transistors, amplify the neuronal electric signals as they travel along the axons.

With 20W of power spread across tens of billions of neurons, the voltages involved are tiny, as are the axon cables. And there is evidence that axons with a diameter less than about 1 micron (which most in the brain are) are susceptible to noise. In other words, the brain is a noisy system.

If this noise simply created unhelpful “brain fog”, one might wonder why we evolved to have so many slender axons in our heads. Indeed, there are benefits to having fatter axons: the signals propagate along them faster. If we still needed fast reaction times to escape predators, then slender axons would be disadvantageous. However, developing communal ways of defending ourselves against enemies may have reduced the need for fast reaction times, leading to an evolutionary trend towards thinner axons.

Perhaps, serendipitously, evolutionary mutations that further increased neuron numbers and reduced axon sizes, keeping overall energy consumption the same, made the brain’s neurons more susceptible to noise. And there is mounting evidence that this had another remarkable effect: it encouraged in humans the ability to solve problems that required leaps in imagination and creativity.

Perhaps we only truly became Homo Sapiens when significant noise began to appear in our brains?

Putting noise in the brain to good use

Many animals have developed creative approaches to solving problems, but there is nothing to compare with a Shakespeare, a Bach or an Einstein in the animal world.

How do creative geniuses come up with their ideas? Here’s a quote from Andrew Wiles, perhaps the most famous mathematician alive today, about the time leading up to his celebrated proof of the maths problem (misleadingly) known as Fermat’s Last Theorem:

When you reach a real impasse, then routine mathematical thinking is of no use to you. Leading up to that kind of new idea, there has to be a long period of tremendous focus on the problem without any distraction. You have to really think about nothing but that problem – just concentrate on it. And then you stop. [At this point] there seems to be a period of relaxation during which the subconscious appears to take over – and it’s during this time that some new insight comes.

This notion seems universal. Physics Nobel Laureate Roger Penrose has spoken about his “Eureka moment” when crossing a busy street with a colleague (perhaps reflecting on their conversation while also looking out for oncoming traffic). For the father of chaos theory Henri Poincaré, it was catching a bus.

And it’s not just creativity in mathematics and physics. Comedian John Cleese, of Monty Python fame, makes much the same point about artistic creativity – it occurs not when you are focusing hard on your trade, but when you relax and let your unconscious mind wander.

Of course, not all the ideas that bubble up from your subconscious are going to be Eureka moments. Physicist Michael Berry talks about these subconscious ideas as if they are elementary particles called “claritons”:

Actually, I do have a contribution to particle physics … the elementary particle of sudden understanding: the “clariton”. Any scientist will recognise the “aha!” moment when this particle is created. But there is a problem: all too frequently, today’s clariton is annihilated by tomorrow’s “anticlariton”. So many of our scribblings disappear beneath a rubble of anticlaritons.

Here is something we can all relate to: that in the cold light of day, most of our “brilliant” subconscious ideas get annihilated by logical thinking. Only a very, very, very small number of claritons remain after this process. But the ones that do are likely to be gems.

In his renowned book Thinking Fast and Slow, the Nobel prize-winning psychologist Daniel Kahneman describes the brain in a binary way. Most of the time when walking, chatting and looking around (in other words when multitasking), it operates in a mode Kahneman calls “system 1” – a rather fast, automatic, effortless mode of operation.

By contrast, when we are thinking hard about a specific problem (unitasking), the brain is in the slower, more deliberative and logical “system 2”. To perform a calculation like 37×13, we have to stop walking, stop talking, close our eyes and even put our hands over our ears. No chance for significant multitasking in system 2.

My 2015 paper with computational neuroscientist Michael O’Shea interpreted system 1 as a mode where available energy is spread across a large number of active neurons, and system 2 as where energy is focused on a smaller number of active neurons. The amount of energy per active neuron is therefore much smaller when in the system 1 mode, and it would seem plausible that the brain is more susceptible to noise when in this state. That is, in situations when we are multitasking, the operation of any one of the neurons will be most susceptible to the effects of noise in the brain.

Berry’s picture of clariton-anticlariton interaction seems to suggest a model of the brain where the noisy system 1 and the deterministic system 2 act in synergy. The anticlariton is the logical analysis that we perform in system 2 which, most of the time, leads us to reject our crazy system 1 ideas.

But sometimes one of these ideas turns out to be not so crazy.

This is reminiscent of how our simulated annealing analysis (Figure 1) works. Initially, we might find many “crazy” ideas appealing. But as we get closer to locating the optimal solution, the criteria for accepting a new suggestion becomes more stringent and discerning. Now, system 2 anticlaritons are annihilating almost everything the system 1 claritons can throw at them – but not quite everything, as Wiles found to his great relief.

The key to creativity

If the key to creativity is the synergy between noisy and deterministic thinking, what are some consequences of this?

On the one hand, if you do not have the necessary background information then your analytic powers will be depleted. That’s why Wiles says that leading up to the moment of insight, you have to immerse yourself in your subject. You aren’t going to have brilliant ideas which will revolutionise quantum physics unless you have a pretty good grasp of quantum physics in the first place.

But you also need to leave yourself enough time each day to do nothing much at all, to relax and let your mind wander. I tell my research students that if they want to be successful in their careers, they shouldn’t spend every waking hour in front of their laptop or desktop. And swapping it for social media probably doesn’t help either, since you still aren’t really multitasking – each moment you are on social media, your attention is still fixed on a specific issue.

But going for a walk or bike ride or painting a shed probably does help. Personally, I find that driving a car is a useful activity for coming up with new ideas and thoughts – provided you don’t turn the radio on.

When making difficult decisions, this suggests that, having listed all the pros and cons, it can be helpful not to actively think about the problem for a while. I think this explains how, years ago, I finally made the decision to change my research direction – not that I knew it at the time.

Because the brain’s system 1 is so energy efficient, we use it to make the vast majority of the many decisions in our daily lives (some say as many as 35,000) – most of which aren’t that important, like whether to continue putting one leg in front of the other as we walk down to the shops. (I could alternatively stop after each step, survey my surroundings to make sure a predator was not going to jump out and attack me, and on that basis decide whether to take the next step.)

However, this system 1 thinking can sometimes lead us to make bad decisions, because we have simply defaulted to this low-energy mode and not engaged system 2 when we should have. How many times do we say to ourselves in hindsight: “Why didn’t I give such and such a decision more thought?”

Of course, if instead we engaged system 2 for every decision we had to make, then we wouldn’t have enough time or energy to do all the other important things we have to do in our daily lives (so the shops may have shut by the time we reach them).

From this point of view, we should not view giving wrong answers to unimportant questions as evidence of irrationality. Kahneman cites the fact that more than 50% of students at MIT, Harvard and Princeton gave the incorrect answer to this simple question – a bat and ball costs $1.10; the bat costs one dollar more than the ball; how much does the ball cost? – as evidence of our irrationality. The correct answer, if you think about it, is 5 cents. But system 1 screams out ten cents.

If we were asked this question on pain of death, one would hope we would spend enough thought to come up with the correct answer. But if we were asked the question as part of an anonymous after-class test, when we had much more important things to spend time and energy doing, then I’d be inclined to think of it as irrational to give the right answer.

If we had 20MW to run the brain, we could spend part of it solving unimportant problems. But we only have 20W and we need to use it carefully. Perhaps it’s the 50% of MIT, Harvard and Princeton students who gave the wrong answer who are really the clever ones.

Just as a climate model with noise can produce types of weather that a model without noise can’t, so a brain with noise can produce ideas that a brain without noise can’t. And just as these types of weather can be exceptional hurricanes, so the idea could end up winning you a Nobel Prize.

So, if you want to increase your chances of achieving something extraordinary, I’d recommend going for that walk in the countryside, looking up at the clouds, listening to the birds cheeping, and thinking about what you might eat for dinner.

So could computers be creative?

Will computers, one day, be as creative as Shakespeare, Bach or Einstein? Will they understand the world around us as we do? Stephen Hawking famously warned that AI will eventually take over and replace mankind.

However, the best-known advocate of the idea that computers will never understand as we do is Hawking’s old colleague, Roger Penrose. In making his claim, Penrose invokes an important “meta” theorem in mathematics known as Gödel’s theorem, which says there are mathematical truths that can’t be proven by deterministic algorithms.

There is a simple way of illustrating Gödel’s theorem. Suppose we make a list of all the most important mathematical theorems that have been proven since the time of the ancient Greeks. First on the list would be Euclid’s proof that there are an infinite number of prime numbers, which requires one really creative step (multiply the supposedly finite number of primes together and add one). Mathematicians would call this a “trick” – shorthand for a clever and succinct mathematical construction.

But is this trick useful for proving important theorems further down the list, like Pythagoras’s proof that the square root of two cannot be expressed as the ratio of two whole numbers? It’s clearly not; we need another trick for that theorem. Indeed, as you go down the list, you’ll find that a new trick is typically needed to prove each new theorem. It seems there is no end to the number of tricks that mathematicians will need to prove their theorems. Simply loading a given set of tricks on a computer won’t necessarily make the computer creative.

Does this mean mathematicians can breathe easily, knowing their jobs are not going to be taken over by computers? Well maybe not.

I have been arguing that we need computers to be noisy rather than entirely deterministic, “bit-reproducible” machines. And noise, especially if it comes from quantum mechanical processes, would break the assumptions of Gödel’s theorem: a noisy computer is not an algorithmic machine in the usual sense of the word.

Does this imply that a noisy computer can be creative? Alan Turing, pioneer of the general-purpose computing machine, believed this was possible, suggesting that “if a machine is expected to be infallible then it cannot also be intelligent”. That is to say, if we want the machine to be intelligent then it had better be capable of making mistakes.

Others may argue there is no evidence that simply adding noise will make an otherwise stupid machine into an intelligent one – and I agree, as it stands. Adding noise to a climate model doesn’t automatically make it an intelligent climate model.

However, the type of synergistic interplay between noise and determinism – the kind that sorts the wheat from the chaff of random ideas – has hardly yet been developed in computer codes. Perhaps we could develop a new type of AI model where the AI is trained by getting it to solve simple mathematical theorems using the clariton-anticlariton model; by making guesses and seeing if any of these have value.

For this to be at all tractable, the AI system would need to be trained to focus on “educated random guesses”. (If the machine’s guesses are all uneducated ones, it will take forever to make progress – like waiting for a group of monkeys to type the first few lines of Hamlet.)

For example, in the context of Euclid’s proof that there are an unlimited number of primes, could we train an AI system in such a way that a random idea like “multiply the assumed finite number of primes together and add one” becomes much more likely than the completely useless random idea “add the assumed finite number of primes together and subtract six”? And if a particular guess turns out to be especially helpful, can we train the AI system so that the next guess is a refinement of the last one?

If we can somehow find a way to do this, it could open up modelling to a completely new level that is relevant to all fields of study. And in so doing, we might yet reach the so-called “singularity” when machines take over from humans. But only when AI developers fully embrace the constructive role of noise – as it seems the brain did many thousands of years ago.

For now, I feel the need for another walk in the countryside. To blow away some fusty old cobwebs – and perhaps sow the seeds for some exciting new ones.

About the Author:

Tim Palmer, Royal Society Research Professor, University of Oxford

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Amlan Ganguly, Rochester Institute of Technology and Nalini Venkatasubramanian, University of California, Irvine 

A digital twin is a virtual representation of a real system – a building, the power grid, a city, even a human being – that mimics the characteristics of the system. A digital twin is more than just a computer model, however. It receives data from sensors in the real system to constantly parallel the system’s state.

A digital twin helps people analyze and predict a system’s behavior under different conditions. The systems being twinned are typically very complex and require significant effort to model and track.

Digital twins are useful in a wide variety of domains, including supply chains, health care, buildings, bridges, self-driving cars and retail customer personas to improve efficiency and reliability. For example, a warehouse operator can optimize a warehouse’s performance by exploring the response of its digital twin to various material handling policies and equipment without incurring the cost of making actual changes.

Even a wildfire can be represented by a digital twin. Government agencies can predict the spread of the fire and its impact under different conditions such as wind velocity, humidity and proximity to habitats, and use this information to guide evacuations.

Why digital twins matter

Digital twins are often used to model, understand and analyze complex systems where performance, reliability and security of the system are critical. In such systems it is paramount to test any changes, whether planned or unplanned.

In order to accurately test changes to the state of the actual system and the effects of any possible stimulus, the digital twin must accurately represent the physical system in its current state. This requires the digital twin to receive continuous updates from the physical system via fast and reliable communications channels.

Creating and maintaining digital twins often involves vast amounts of data to represent various features of the real system. Collecting and processing this data requires advanced communication and computing technologies. Communication support typically involves high-speed internet connections and wireless networks such as Wi-Fi and 5G. Computational support is typically in the form of servers, either in the cloud or closer to the physical system.

We and other faculty members at Rochester Institute of Technology and the University of California, Irvine are starting the Center for Smart Spaces Research, a research center sponsored by the National Science Foundation. One of the primary ongoing projects within this center is building the basic technologies for creating digital twins in a variety of applications.

Read other short, accessible explanations of newsworthy subjects written by academics in their areas of expertise for The Conversation U.S. here.

About  the Author:

Amlan Ganguly, Associate Professor of Computer Engineering, Rochester Institute of Technology and Nalini Venkatasubramanian, Professor of Computer Science, University of California, Irvine

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Russ Schumacher, Colorado State University and Aaron Hill, Colorado State University 

A century ago, English mathematician Lewis Fry Richardson proposed a startling idea for that time: constructing a systematic process based on math for predicting the weather. In his 1922 book, “Weather Prediction By Numerical Process,” Richardson tried to write an equation that he could use to solve the dynamics of the atmosphere based on hand calculations.

It didn’t work because not enough was known about the science of the atmosphere at that time. “Perhaps some day in the dim future it will be possible to advance the computations faster than the weather advances and at a cost less than the saving to mankind due to the information gained. But that is a dream,” Richardson concluded.

A century later, modern weather forecasts are based on the kind of complex computations that Richardson imagined – and they’ve become more accurate than anything he envisioned. Especially in recent decades, steady progress in research, data and computing has enabled a “quiet revolution of numerical weather prediction.”

For example, a forecast of heavy rainfall two days in advance is now as good as a same-day forecast was in the mid-1990s. Errors in the predicted tracks of hurricanes have been cut in half in the last 30 years.

There still are major challenges. Thunderstorms that produce tornadoes, large hail or heavy rain remain difficult to predict. And then there’s chaos, often described as the “butterfly effect” – the fact that small changes in complex processes make weather less predictable. Chaos limits our ability to make precise forecasts beyond about 10 days.

As in many other scientific fields, the proliferation of tools like artificial intelligence and machine learning holds great promise for weather prediction. We have seen some of what’s possible in our research on applying machine learning to forecasts of high-impact weather. But we also believe that while these tools open up new possibilities for better forecasts, many parts of the job are handled more skillfully by experienced people.

Predictions based on storm history

Today, weather forecasters’ primary tools are numerical weather prediction models. These models use observations of the current state of the atmosphere from sources such as weather stations, weather balloons and satellites, and solve equations that govern the motion of air.

These models are outstanding at predicting most weather systems, but the smaller a weather event is, the more difficult it is to predict. As an example, think of a thunderstorm that dumps heavy rain on one side of town and nothing on the other side. Furthermore, experienced forecasters are remarkably good at synthesizing the huge amounts of weather information they have to consider each day, but their memories and bandwidth are not infinite.

Artificial intelligence and machine learning can help with some of these challenges. Forecasters are using these tools in several ways now, including making predictions of high-impact weather that the models can’t provide.

In a project that started in 2017 and was reported in a 2021 paper, we focused on heavy rainfall. Of course, part of the problem is defining “heavy”: Two inches of rain in New Orleans may mean something very different than in Phoenix. We accounted for this by using observations of unusually large rain accumulations for each location across the country, along with a history of forecasts from a numerical weather prediction model.

We plugged that information into a machine learning method known as “random forests,” which uses many decision trees to split a mass of data and predict the likelihood of different outcomes. The result is a tool that forecasts the probability that rains heavy enough to generate flash flooding will occur.

We have since applied similar methods to forecasting of tornadoes, large hail and severe thunderstorm winds. Other research groups are developing similar tools. National Weather Service forecasters are using some of these tools to better assess the likelihood of hazardous weather on a given day.

Researchers also are embedding machine learning within numerical weather prediction models to speed up tasks that can be intensive to compute, such as predicting how water vapor gets converted to rain, snow or hail.

It’s possible that machine learning models could eventually replace traditional numerical weather prediction models altogether. Instead of solving a set of complex physical equations as the models do, these systems instead would process thousands of past weather maps to learn how weather systems tend to behave. Then, using current weather data, they would make weather predictions based on what they’ve learned from the past.

Some studies have shown that machine learning-based forecast systems can predict general weather patterns as well as numerical weather prediction models while using only a fraction of the computing power the models require. These new tools don’t yet forecast the details of local weather that people care about, but with many researchers carefully testing them and inventing new methods, there is promise for the future.

The role of human expertise

There are also reasons for caution. Unlike numerical weather prediction models, forecast systems that use machine learning are not constrained by the physical laws that govern the atmosphere. So it’s possible that they could produce unrealistic results – for example, forecasting temperature extremes beyond the bounds of nature. And it is unclear how they will perform during highly unusual or unprecedented weather phenomena.

And relying on AI tools can raise ethical concerns. For instance, locations with relatively few weather observations with which to train a machine learning system may not benefit from forecast improvements that are seen in other areas.

Another central question is how best to incorporate these new advances into forecasting. Finding the right balance between automated tools and the knowledge of expert human forecasters has long been a challenge in meteorology. Rapid technological advances will only make it more complicated.

Ideally, AI and machine learning will allow human forecasters to do their jobs more efficiently, spending less time on generating routine forecasts and more on communicating forecasts’ implications and impacts to the public – or, for private forecasters, to their clients. We believe that careful collaboration between scientists, forecasters and forecast users is the best way to achieve these goals and build trust in machine-generated weather forecasts.

About the Author:

Russ Schumacher, Associate Professor of Atmospheric Science and Colorado State Climatologist, Colorado State University and Aaron Hill, Research Scientist, Colorado State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Jarryd Pla, UNSW and Andrew Dzurak, UNSW 

Vaccine and drug development, artificial intelligence, transport and logistics, climate science — these are all areas that stand to be transformed by the development of a full-scale quantum computer. And there has been explosive growth in quantum computing investment over the past decade.

Yet current quantum processors are relatively small in scale, with fewer than 100 qubits — the basic building blocks of a quantum computer. Bits are the smallest unit of information in computing, and the term qubits stems from “quantum bits”.

While early quantum processors have been crucial for demonstrating the potential of quantum computing, realising globally significant applications will likely require processors with upwards of a million qubits.

Our new research tackles a core problem at the heart of scaling up quantum computers: how do we go from controlling just a few qubits, to controlling millions? In research published today in Science Advances, we reveal a new technology that may offer a solution.

What exactly is a quantum computer?

Quantum computers use qubits to hold and process quantum information. Unlike the bits of information in classical computers, qubits make use of the quantum properties of nature, known as “superposition” and “entanglement”, to perform some calculations much faster than their classical counterparts.

Unlike a classical bit, which is represented by either 0 or 1, a qubit can exist in two states (that is, 0 and 1) at the same time. This is what we refer to as a superposition state.

Demonstrations by Google and others have shown even current, early-stage quantum computers can outperform the most powerful supercomputers on the planet for a highly specialised (albeit not particularly useful) task — reaching a milestone we call quantum supremacy.

Google’s quantum computer, built from superconducting electrical circuits, had just 53 qubits and was cooled to a temperature close to -273℃ in a high-tech refrigerator. This extreme temperature is needed to remove heat, which can introduce errors to the fragile qubits. While such demonstrations are important, the challenge now is to build quantum processors with many more qubits.

Major efforts are underway at UNSW Sydney to make quantum computers from the same material used in everyday computer chips: silicon. A conventional silicon chip is thumbnail-sized and packs in several billion bits, so the prospect of using this technology to build a quantum computer is compelling.

The control problem

In silicon quantum processors, information is stored in individual electrons, which are trapped beneath small electrodes at the chip’s surface. Specifically, the qubit is coded into the electron’s spin. It can be pictured as a small compass inside the electron. The needle of the compass can point north or south, which represents the 0 and 1 states.

To set a qubit in a superposition state (both 0 and 1), an operation that occurs in all quantum computations, a control signal must be directed to the desired qubit. For qubits in silicon, this control signal is in the form of a microwave field, much like the ones used to carry phone calls over a 5G network. The microwaves interact with the electron and cause its spin (compass needle) to rotate.

Currently, each qubit requires its own microwave control field. It is delivered to the quantum chip through a cable running from room temperature down to the bottom of the refrigerator at close to -273℃. Each cable brings heat with it, which must be removed before it reaches the quantum processor.

At around 50 qubits, which is state-of-the-art today, this is difficult but manageable. Current refrigerator technology can cope with the cable heat load. However, it represents a huge hurdle if we’re to use systems with a million qubits or more.

The solution is ‘global’ control

An elegant solution to the challenge of how to deliver control signals to millions of spin qubits was proposed in the late 1990s. The idea of “global control” was simple: broadcast a single microwave control field across the entire quantum processor.

Voltage pulses can be applied locally to qubit electrodes to make the individual qubits interact with the global field (and produce superposition states).

It’s much easier to generate such voltage pulses on-chip than it is to generate multiple microwave fields. The solution requires only a single control cable and removes obtrusive on-chip microwave control circuitry.

For more than two decades global control in quantum computers remained an idea. Researchers could not devise a suitable technology that could be integrated with a quantum chip and generate microwave fields at suitably low powers.

In our work we show that a component known as a dielectric resonator could finally allow this. The dielectric resonator is a small, transparent crystal which traps microwaves for a short period of time.

The trapping of microwaves, a phenomenon known as resonance, allows them to interact with the spin qubits longer and greatly reduces the power of microwaves needed to generate the control field. This was vital to operating the technology inside the refrigerator.

In our experiment, we used the dielectric resonator to generate a control field over an area that could contain up to four million qubits. The quantum chip used in this demonstration was a device with two qubits. We were able to show the microwaves produced by the crystal could flip the spin state of each one.

The path to a full-scale quantum computer

There is still work to be done before this technology is up to the task of controlling a million qubits. For our study, we managed to flip the state of the qubits, but not yet produce arbitrary superposition states.

Experiments are ongoing to demonstrate this critical capability. We’ll also need to further study the impact of the dielectric resonator on other aspects of the quantum processor.

That said, we believe these engineering challenges will ultimately be surmountable — clearing one of the greatest hurdles to realising a large-scale spin-based quantum computer.

About the Author:

Jarryd Pla, Senior Lecturer in Quantum Engineering, UNSW and Andrew Dzurak, Scientia Professor in Quantum Engineering, UNSW

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