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A quantum fridge used for quantum computing experiments at NIWC Pacific

Quantum computing

How can you get more and more out of less and less? The smaller computers get, the more powerful they seem to become: there's more number-crunching ability in a 21st-century cellphone than you'd have found in a room-sized, military computer 50 years ago. Yet, despite such amazing advances, there are still plenty of complex problems that are beyond the reach of even the world's most powerful computers—and there's no guarantee we'll ever be able to tackle them. One problem is that the basic switching and memory units of computers, known as transistors, are now approaching the point where they'll soon be as small as individual atoms. If we want computers that are smaller and more powerful than today's, we'll soon need to do our computing in a radically different way. Entering the realm of atoms opens up powerful new possibilities in the shape of quantum computing, with processors that could work millions of times faster than the ones we use today. Sounds amazing, but the trouble is that quantum computing is hugely more complex than traditional computing and operates in the Alice in Wonderland world of quantum physics, where the "classical," sensible, everyday laws of physics no longer apply. What is quantum computing and how does it work? Let's take a closer look!

Photo: A shiny new quantum refrigerator—a piece of laboratory equipment used for cooling atoms to near absolute zero, slowing them down so qubits (the chunks of information in a quantum computer) are easier to work with. Photo courtesy of Naval Information Warfare Center (NIWC) Pacific and DVIDS.

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  1. What is conventional computing?
  2. What is quantum computing?
  3. Quantum + computing = quantum computing
  4. What would a quantum computer be like in reality?
  5. What's inside a quantum computer?
  6. What can quantum computers do that ordinary computers can't?
  7. Why is it so hard to make a quantum computer?
  8. How far off are quantum computers?
  9. Find out more

What is conventional computing?

You probably think of a computer as a neat little gadget that sits on your lap and lets you send emails, shop online, chat to your friends, or play games—but it's much more and much less than that. It's more, because it's a completely general-purpose machine: you can make it do virtually anything you like. It's less, because inside it's little more than an extremely basic calculator, following a prearranged set of instructions called a program. Like the Wizard of Oz, the amazing things you see in front of you conceal some pretty mundane stuff under the covers.

A transistor on a printed circuit board from a radio.

Photo: This is what one transistor from a typical radio circuit board looks like. In computers, the transistors are much smaller than this and millions of them are packaged together onto microchips.

Conventional computers have two tricks that they do really well: they can store numbers in memory and they can process stored numbers with simple mathematical operations (like add and subtract). They can do more complex things by stringing together the simple operations into a series called an algorithm (multiplying can be done as a series of additions, for example). Both of a computer's key tricks—storage and processing—are accomplished using switches called transistors, which are like microscopic versions of the switches you have on your wall for turning on and off the lights. A transistor can either be on or off, just as a light can either be lit or unlit. If it's on, we can use a transistor to store a number one (1); if it's off, it stores a number zero (0). Long strings of ones and zeros can be used to store any number, letter, or symbol using a code based on binary (so computers store an upper-case letter A as 1000001 and a lower-case one as 01100001). Each of the zeros or ones is called a binary digit (or bit) and, with a string of eight bits, you can store 255 different characters (such as A-Z, a-z, 0-9, and most common symbols). Computers calculate by using circuits called logic gates, which are made from a number of transistors connected together. Logic gates compare patterns of bits, stored in temporary memories called registers, and then turn them into new patterns of bits—and that's the computer equivalent of what our human brains would call addition, subtraction, or multiplication. In physical terms, the algorithm that performs a particular calculation takes the form of an electronic circuit made from a number of logic gates, with the output from one gate feeding in as the input to the next.

The trouble with conventional computers is that they depend on conventional transistors. This might not sound like a problem if you go by the amazing progress made in electronics over the last few decades. When the transistor was invented, back in 1947, the switch it replaced (which was called the vacuum tube) was about as big as one of your thumbs. Now, a state-of-the-art microprocessor (single-chip computer) packs hundreds of millions (and up to 30 billion) transistors onto a chip of silicon the size of your fingernail! Chips like these, which are called integrated circuits, are an incredible feat of miniaturization. Back in the 1960s, Intel co-founder Gordon Moore realized that the power of computers doubles roughly 18 months—and it's been doing so ever since. This apparently unshakeable trend is known as Moore's Law.

Memory chip from a USB flash memory stick

Photo: This memory chip from a typical USB stick contains an integrated circuit that can store 512 megabytes of data. That's roughly 500 million characters (536,870,912 to be exact), each of which needs eight binary digits—so we're talking about 4 billion (4,000 million) transistors in all (4,294,967,296 if you're being picky) packed into an area the size of a postage stamp!

It sounds amazing, and it is, but it misses the point. The more information you need to store, the more binary ones and zeros—and transistors—you need to do it. Since most conventional computers can only do one thing at a time, the more complex the problem you want them to solve, the more steps they'll need to take and the longer they'll need to do it. Some computing problems are so complex that they need more computing power and time than any modern machine could reasonably supply; computer scientists call those intractable problems.

As Moore's Law advances, so the number of intractable problems diminishes: computers get more powerful and we can do more with them. The trouble is, transistors are just about as small as we can make them: we're getting to the point where the laws of physics seem likely to put a stop to Moore's Law. Unfortunately, there are still hugely difficult computing problems we can't tackle because even the most powerful computers find them intractable. That's one of the reasons why people are now getting interested in quantum computing.

What is quantum computing?

Things on a very small scale behave like nothing you have any direct experience about... or like anything that you have ever seen.”

Richard Feynman

Quantum theory is the branch of physics that deals with the world of atoms and the smaller (subatomic) particles inside them. You might think atoms behave the same way as everything else in the world, in their own tiny little way—but that's not true: on the atomic scale, the rules change and the "classical" laws of physics we take for granted in our everyday world no longer automatically apply. As Richard P. Feynman, one of the greatest physicists of the 20th century, once put it: "Things on a very small scale behave like nothing you have any direct experience about... or like anything that you have ever seen." [1]

If you've studied light, you may already know a bit about quantum theory. You might know that a beam of light sometimes behaves as though it's made up of particles (like a steady stream of cannonballs), and sometimes as though it's waves of energy rippling through space (a bit like waves on the sea). That's called wave-particle duality and it's one of the ideas that comes to us from quantum theory. It's hard to grasp that something can be two things at once—a particle and a wave—because it's totally alien to our everyday experience: a car is not simultaneously a bicycle and a bus. In quantum theory, however, that's just the kind of crazy thing that can happen. The most striking example of this is the baffling riddle known as Schrödinger's cat. Briefly, in the weird world of quantum theory, we can imagine a situation where something like a cat could be alive and dead at the same time!

What does all this have to do with computers? Suppose we keep on pushing Moore's Law—keep on making transistors smaller until they get to the point where they obey not the ordinary laws of physics (like old-style transistors) but the more bizarre laws of quantum mechanics. The question is whether computers designed this way can do things our conventional computers can't. If we can predict mathematically that they might be able to, can we actually make them work like that in practice?

People have been asking those questions for several decades. Among the first were IBM research physicists Rolf Landauer and Charles H. Bennett. Landauer opened the door for quantum computing in the 1960s when he proposed that information is a physical entity that could be manipulated according to the laws of physics. [2] One important consequence of this is that computers waste energy manipulating the bits inside them (which is partly why computers use so much energy and get so hot, even though they appear to be doing not very much at all). In the 1970s, building on Landauer's work, Bennett showed how a computer could circumvent this problem by working in a "reversible" way, implying that a quantum computer could carry out massively complex computations without using massive amounts of energy. [3] In 1980, physicist Paul Benioff from Argonne National Laboratory tried to envisage a basic machine that would work in a similar way to an ordinary computer but according to the principles of quantum physics—in other words, a quantum Turing machine. [4] The following year, Richard Feynman sketched out roughly how a machine using quantum principles could carry out basic computations. [5] A few years later, Oxford University's David Deutsch (one of the leading lights in quantum computing) outlined the theoretical basis of a quantum computer in more detail. [6] How did these great scientists imagine that quantum computers might work?

Quantum computing clip-art concept: an atom superimposed on a laptop

Artwork: Quantum computing means storing and processing information using individual atoms, ions, electrons, or photons. On the plus side, this opens up the possibility of faster computers, but the drawback is the greater complexity of designing computers that can operate in the weird world of quantum physics.

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Quantum + computing = quantum computing

The key features of an ordinary computer—bits, registers, logic gates, algorithms, and so on—have analogous features in a quantum computer. Instead of bits, a quantum computer has quantum bits or qubits, which work in a particularly intriguing way. Where a bit can store either a zero or a 1, a qubit can store a zero, a one, both zero and one, or an infinite number of values in between—and be in multiple states (store multiple values) at the same time! If that sounds confusing, think back to light being a particle and a wave at the same time, Schrödinger's cat being alive and dead, or a car being a bicycle and a bus. A gentler way to think of the numbers qubits store is through the physics concept of superposition (where two waves add to make a third one that contains both of the originals). If you blow on something like a flute, the pipe fills up with a standing wave: a wave made up of a fundamental frequency (the basic note you're playing) and lots of overtones or harmonics (higher-frequency multiples of the fundamental). The wave inside the pipe contains all these waves simultaneously: they're added together to make a combined wave that includes them all. Qubits use superposition to represent multiple states (multiple numeric values) simultaneously in a similar way. Only when you try to find out what state it's actually in at any given moment (by measuring it, in other words) does it "collapse" into one of its possible states—and that gives you the answer to your problem.

Just as a quantum computer can store multiple numbers at once, so it can process them simultaneously—in very surprising ways. That's because qubits enjoy another weird quantum property called entanglement, which is a bit like the spooky intuition twin siblings claim to enjoy even when they're apart. Just as one twin (supposedly) knows when the other is in distress, even when they're many miles away, so entangled qubits can "talk" to and "cooperate" with one another even when they're some distance apart. Cutting to the chase, entanglement effectively amplifies the power of superposition, allowing a quantum computer to do far more things, far more quickly than a conventional machine, Instead of grinding along in serial (doing a series of things one at a time in a sequence), it can work in "massive parallel" (doing many things at the same time). Estimates suggest a quantum computer's ability to work in parallel would make it millions of times faster than any conventional computer... if only we could build it! So how would we do that?

What would a quantum computer be like in reality?

In reality, qubits would have to be stored by atoms, ions (atoms with too many or too few electrons), or even smaller things such as electrons and photons (energy packets), so a quantum computer would be almost like a table-top version of the kind of particle physics experiments they do at Fermilab or CERN. Now you wouldn't be racing particles round giant loops and smashing them together, but you would need mechanisms for containing atoms, ions, or subatomic particles, for putting them into certain states (so you can store information), knocking them into other states (so you can make them process information), and figuring out what their states are after particular operations have been performed.

An atom confined in an optical cavity by lasers and mirrors

Photo: A single atom or ion can be trapped in an optical cavity—the space between mirrors—and controlled by precise pulses from laser beams.

In practice, there are lots of possible ways of containing atoms and changing their states using laser beams, electromagnetic fields, radio waves, and an assortment of other techniques. One method is to make qubits using quantum dots, which are nanoscopically tiny particles of semiconductors inside which individual charge carriers, electrons and holes (missing electrons), can be controlled. Another method makes qubits from what are called ion traps: you add or take away electrons from an atom to make an ion, hold it steady in a kind of laser spotlight (so it's locked in place like a nanoscopic rabbit dancing in a very bright headlight), and then flip it into different states with laser pulses. In another technique, the qubits are photons inside optical cavities (spaces between extremely tiny mirrors). Don't worry if you don't understand; not many people do. Since the entire field of quantum computing is still largely abstract and theoretical, the only thing we really need to know is that qubits are stored by atoms or other quantum-scale particles that can exist in different states and be switched between them.

Vacuum chamber from an ion trap experiment with four trapped ytterbium ions

Photo: A quantum-computing ion trap experiment. Left: A vacuum chamber used for holding the ion trap. Upper right: The ion trap itself. Lower right: Four ytterbium (Yb+) ions caught in the trap! Photo courtesy of US Air Force Research Lab and DVIDS.

What's inside a quantum computer?

How does all this "quantum stuff" fit inside a real computer? On the one hand we've got traditional computers—input, output, processing, storage; on the other hand, we have all this weird quantum stuff: ion traps and cavities, and qubits doing their thing with entanglement and superposition. How do these two very different things work together?

Here's an illustration showing one potential setup ("architecture") designed by Chris Monroe and Jungsang Kim, Duke University professors who are also founders of IonQ, one of the leading quantum computing startups focusing on ion-trap technology, which is taken from a 2021 patent application. I'm not going to explain in detail how it works (because it's way too complex and detailed for this quick overview), but I think you can get a flavor of how the quantum and conventional parts of the machine mesh together just by looking at the key parts, which I've colored for simplicity.

Fault-tolerant scalable modular quantum computer architecture by Chris Monroe and Jungsang Kim of IonQ

Artwork: Quantum meets computing in this proposed architecture designed by Chris Monroe and Jungsang Kim. Artwork from US20210365827A1: Fault-tolerant scalable modular quantum computer architecture with an enhanced control of multi-mode couplings betwen trapped ion qubits, filed July 23, 2021.

In this diagram, the "quantum stuff" is at the bottom and the "conventional stuff" is at the top. Here a few of the key parts:

  1. The elementary logic units (left, red) are essentially where the qubits live. They comprise quantum memory modules, in this case based on ion-traps, which form the basis of the computer's qubit memories/registers, and logic gates.
  2. The optical interconnect switch (middle, orange) links the quantum memory modules together. In this design, any qubit can interact with any other qubit.
  3. The beam splitter (turqoise, bottom right) links optical fibers from the interconnect switch to photon detectors.
  4. The laser subsystem (yellow, middle) drives the qubits into different states.
  5. The detection system (purple, right) detects photons that signal entanglement between the qubits. The states of the qubits are measured and fed back to the CPU.
  6. The CPU (green, top) does broadly the same thing as it does in a traditional computer, receiving input, communicating with the quantum part of the machine (using a "quantum algorithm"), and ultimately turning what the qubits do into output.
  7. The fault-tolerance mechanism (gray, top) helps to minimize computing errors when qubits "interact."

For much more detail, check out the patent application.

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What can quantum computers do that ordinary computers can't?

Although people often assume that quantum computers must automatically be better than conventional ones, that's by no means certain. So far, just about the only thing we know for certain that a quantum computer could do better than a normal one is factorisation: finding two unknown prime numbers that, when multiplied together, give a third, known number. In 1994, while working at Bell Laboratories, mathematician Peter Shor demonstrated an algorithm that a quantum computer could follow to find the "prime factors" of a large number, which would speed up the problem enormously. [7] Shor's algorithm really excited interest in quantum computing because virtually every modern computer (and every secure, online shopping and banking website) uses public-key encryption technology based on the virtual impossibility of finding prime factors quickly (it is, in other words, essentially an "intractable" computer problem). If quantum computers could indeed factor large numbers quickly, today's online security could be rendered obsolete at a stroke. But what goes around comes around, and some researchers believe quantum technology will lead to much stronger forms of encryption. (In 2017, Chinese researchers demonstrated for the first time how quantum encryption could be used to make a very secure video call from Beijing to Vienna.)

Does that mean quantum computers are better than conventional ones? Not exactly. Apart from Shor's algorithm, and a search method called Grover's algorithm, hardly any other algorithms have been discovered that would be better performed by quantum methods. Given enough time and computing power, conventional computers should still be able to solve any problem that quantum computers could solve, eventually. In other words, it remains to be proven that quantum computers are generally superior to conventional ones, especially given the difficulties of actually building them. Who knows how conventional computers might advance in the next 50 years, potentially making the idea of quantum computers irrelevant—and even absurd.

Colorful quantum dots

Photo: Quantum dots are probably best known as colorful nanoscale crystals, but they can also be used as qubits in quantum computers). Photo by Dennis Schroeder courtesy of NREL (photo id#101359).

Why is it so hard to make a quantum computer?

We have decades of experience building ordinary, transistor-based computers with conventional architectures; building quantum machines means reinventing the whole idea of a computer from the bottom up. First, there are the practical difficulties of making qubits, controlling them very precisely, and having enough of them to do really useful things. Next, there's a major difficulty with errors inherent in a quantum system—"noise" as this is technically called—which seriously compromises any calculations a quantum computer might make. There are ways around this ("quantum error correction"), but they introduce a great deal more complexity. There's also the fundamental issue of how you get data in and out of a quantum computer, which is, itself, a complex computing problem. Some critics believe these issues are insurmountable; others acknowledge the problems but argue the mission is too important to abandon.

How far off are quantum computers?

Three decades after they were first proposed, quantum computers remain largely theoretical. Even so, there's been some encouraging progress toward realizing a quantum machine. There were two impressive breakthroughs in 2000. First, Isaac Chuang (now an MIT professor, but then working at IBM's Almaden Research Center) used five fluorine atoms to make a crude, five-qubit quantum computer. The same year, researchers at Los Alamos National Laboratory figured out how to make a seven-qubit machine using a drop of liquid. Five years later, researchers at the University of Innsbruck added an extra qubit and produced the first quantum computer that could manipulate a qubyte (eight qubits), later bumping the number up to 14 qubits.

These were tentative but important first steps. Over the next few years, researchers announced more ambitious experiments, adding progressively greater numbers of qubits. By 2011, a pioneering Canadian company called D-Wave Systems announced in Nature that it had produced a 128-qubit machine [8]; the announcement proved highly controversial and there was a lot of debate over whether the company's machines had really demonstrated quantum behavior. Three years later, Google announced that it was hiring a team of academics (including University of California at Santa Barbara physicist John Martinis) to develop its own quantum computers based on D-Wave's approach. In March 2015, the Google team announced they were "a step closer to quantum computation," having developed a new way for qubits to detect and protect against errors. In 2016, MIT's Isaac Chuang and scientists from the University of Innsbruck unveiled a five-qubit, ion-trap quantum computer that could calculate the factors of 15; one day, a scaled-up version of this machine might evolve into the long-promised, fully fledged encryption buster.

There's no doubt that these are hugely important advances. and the signs are growing steadily more encouraging that quantum technology will eventually deliver a computing revolution. In December 2017, Microsoft unveiled a complete quantum development kit, including a new computer language, Q#, developed specifically for quantum applications. In early 2018, D-wave announced plans to start rolling out quantum power to a cloud computing platform. A few weeks later, Google announced Bristlecone, a quantum processor based on a 72-qubit array, that might, one day, form the cornerstone of a quantum computer that could tackle real-world problems. In October 2019, Google announced it had reached another milestone: the achievement of "quantum supremacy" (the point at which a quantum computer can beat a conventional machine at a typical computing task), though not everyone was convinced; IBM, for example, disputed the claim. Google's work, it argued, was "really impressive... [but] not interesting for any applications." IBM has put more emphasis on real-world utility, recently using a 127 qubit processor nicknamed Eagle to tackle practical physics problems.

Colorful quantum dots

Chart: Quantum computing might look like a race to see who can notch up most qubits, most quickly—but it's much more about who can turn theoretical lab science into real world practical applications most effectively. Please note that this chart is an illustration of rough progression and the x-axis (year) is not linear.

Whether quantum supremacy has been achieved or not, one thing is beyond dispute: quantum computing is very exciting—and you can find out just how exciting by tinkering with it for yourself, In 2019, Amazon's AWS Cloud Computing offshoot announced a service called Braket, which gives its users access to quantum computing simulators based on machines being developed by three cutting-edge companies (D-wave, IonQ, and Rigletti). Microsoft's Azure cloud platform offers a rival service called Azure Quantum, while Google's Quantum AI website offers access to its own research and resources. Take your pick—or try them all.

Despite all this progress, it's early days for the whole field, and most researchers agree that we're unlikely to see practical quantum computers appearing for some years—and more likely several decades. The conclusion reached by an influential National Academies of Sciences, Medicine and Engineering report in December 2018 was that "it is still too early to be able to predict the time horizon for a practical quantum computer" and that "many technical challenges remain to be resolved before we reach this milestone."

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Introductions to quantum theory

Introductions to quantum computing

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Research groups

These sites are useful if you want a glimpse at cutting-edge quantum research. Some have good educational resources and simulations you can play about with.


  1.    Six Easy Pieces by Richard Feynman. Basic Books, 1998, p.116.
  2.    Irreversibility and Heat Generation in the Computing Process by Ralph Landauer, IBM Journal of Research and Development, 1961, Vol 5, Issue 3, pp.183–191.
  3.    Logical reversibility of computation by Charles Bennett, IBM Journal of Research and Development, 1973, Vol 17, Issue 6, pp.525–532. Also worth a look is the more recent Notes on Landauer's principle, reversible computation, and Maxwell's Demon by Charles Bennett, Studies in History and Philosophy of Modern Physics , 2003, Number 34, pp.501–510.
  4.    The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines by Paul Benioff, Journal of Statistical Physics, 1980, Vol 22, pp.563–591.
  5.    Simulating physics with computers by Richard Feynman, International Journal of Theoretical Physics, Vol 21, No 6/7, 1982.
  6.    Quantum theory, the Church-Turing principle and the universal quantum computer by David Deutsch, Proceedings of the Royal Society of London A 400, 1985, pp.97–117
  7.    Algorithms for quantum computation: discrete logarithms and factoring by Peter Shor, Proceedings 35th Annual Symposium on Foundations of Computer Science, 1994, pp.124–134.
  8.    Quantum annealing with manufactured spins by M.W. Johnson et al, Nature, Vol 473, 2011, pp.194–198.

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