The activity around quantum computing has sparked a high degree of interest. How much money is behind quantum computing? Who is providing it?

Where does the technology stand compared with AI or blockchain? What regions and entities are leading in publications and IP? The bulk of the private quantum computing deals over the last several years took place in the US, Canada, the UK, and Australia. See Exhibit 3. A regional race is also developing, involving large publicly funded programs that are devoted to quantum technologies more broadly, including quantum communication and sensing as well as computing.

Many other countries, notably Australia, Canada, and Israel are also very active. The money has been accompanied by a flurry of patents and publishing.

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See Exhibit 4. North America and East Asia are clearly in the lead; these are also the regions with the most active commercial technology activity. Europe is a distant third, an alarming sign, especially in light of a number of leading European quantum experts joining US-based companies in recent years.

Australia, a hotspot for quantum technologies for many years, is striking given its much smaller population. The country is determined to play in the quantum race; in fact, one of its leading quantum computing researchers, Michelle Simmons, was named Australian of the Year Two things are noteworthy about the volume of scientific publishing regarding quantum computing since See Exhibit 5.

The first is the rise of China, which has surpassed the US to become the leader in quantity of scientific articles published. The cooperation shows that quantum computing is not dominated by national security interests yet, owing in large part to consensus around the view that cryptographic applications are still further in the future and that effective remedies for such applications are in the making.

The two biggest questions facing the emerging quantum computing industry are, When will we have a large, reliable quantum computer, and What will be its architecture? Hardware companies are pursuing a range of technologies with very different characteristics and properties. As of now, it is unclear which will ultimately form the underlying architecture for quantum computers, but the field has narrowed to a handful of potential candidates.

We use three sets of criteria to assess the current status of the leaders pursuing the mainstream circuit-based approaches and the challenges they still need to overcome. Size of the Quantum System. Size refers to the number of qubits a system uses and is the most common standard for quantum technologies because it is the initial determinant for both the scale and the complexity of potential operations. The number of physical qubits currently ranges from 2 to 20 in machines that have been built and are known to be well-calibrated and performing satisfactorily. Scientists believe that computers with a few hundred physical qubits are within technological reach.

Complexity of Accurate Calculations. See Exhibit 6. Technical Maturity and Scalability. The third set of criteria includes general technology readiness level or maturity on a scale of 1 to 9 , and, equally important, the degree of the challenges for scaling the system. Unfortunately, the comparative performance of algorithms on different hardware technologies cannot be directly determined from these characteristics.

Heuristic measures typically involve some notion of volume, such as the number of qubits times the number of gate operations that can be reliably performed until an error occurs, but the devil is in the details of such concepts. End-to-end software and specialist players are offering services at different levels of sophistication both to assess the performance of specific algorithms on the available hardware and to help with developing the best quantum algorithm based on these assessments.

Exhibit 7 reflects our assessment of the most important current technologies, ordered by an outside-in view of technical maturity, providing the performance along the criteria above. Both technologies we discuss a third, annealers, separately have produced promising results, but most leading large tech companies seem to be betting on superconducting qubits for now.

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We see two reasons for this. One is that superconducting circuits are based on known complementary metal-oxide semiconductor technology, or CMOS, which is somewhat more standardized and easier to handle than ion traps or other approaches. Second, the near-term path to medium scale might be more predictable. Ion traps face a significant hurdle when they reach about 50 qubits, generally considered the limit of a single trap.

Connecting a second trap introduces new challenges and proposed solutions are still under scientific investigation. Superconducting qubits have mastered the short-term architectural challenges by inserting the operational micropulses from the third dimension onto the planar chip and moving toward regular autocalibration of the easily disturbed qubits. In fact, their most immediate scaling challenge may seem somewhat mundane—electric cabling and control electronics.

The current way of addressing a qubit with two to four cables, while also maintaining cryogenic temperatures, triggers severe engineering challenges when the number of qubits runs into the hundreds. All leading players in the superconducting camp have made their smaller chips accessible externally to software and service companies and preferred partners. Some have opened lower-performing versions and simulators to the community at large. This sign of commercial readiness has further strengthened the general expectation that superconducting qubits could lead over other technologies over the next three to four years.

That being said, even superconducting qubit architectures have achieved only about 20 reliable qubits so far, compared with 10 10 bits on a chip for classical computing, so there is still some ways to go. For IBM 50 qubits and Google 72 qubits the next-generation hardware is expected to become publicly accessible shortly, and Rigetti qubits has also announced it will offer access to its next generation by August The roadmaps of all these players extend to about 1 million qubits.

They have a strong grip on what needs to be resolved consecutively along the journey, even if they do not yet have workable solutions for them. Beyond the near-term time frame, the research landscape is more open, with a few promising candidate technologies in the race, all of which are still immature. They face several science questions and quite a few challenging engineering problems, which are particular to each technology. Each approach has its attractive aspects and its challenges.

Photons, for example, could have an advantage in terms of handling because they operate at room temperature and chip design can leverage known silicon technology.

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For instance, PsiQ, a Silicon Valley startup, wants to leapfrog the NISQ period with an ambition to develop a large-scale linear optical quantum computer, or LOQC, based on photons as qubits, with 1 million qubits as its first go-to-market product within about five years. This would be a major breakthrough if and when it becomes available. The challenges for photons lie in developing single photon sources and detectors as well as controlling multiphoton interactions, which are critical for two-qubit gates.

In the longer run, it could prove easier, and thus faster, to scale these atomic-size qubits and draw from global silicon manufacturing experience to realize a many-qubit architecture. The core ambition of the final—still very early-stage—topological approach is an unprecedented low error rate of 1 part per million and not excluding even 1 part per billion.

This would constitute a game changer. The underlying physical mechanism the exotic Majorana quasiparticle is now largely accepted, and the first topological qubit is still expected by Microsoft to become reality in Two-qubit gates, however, are an entirely different ballgame, and even a truly ambitious roadmap would not produce a workable quantum computer for at least five years.

One could think that the number of calculation cycles simply dividing qubit lifetime by gate operation time is a good measure to compare different technologies. However, this could provide a somewhat skewed view: in the short term, the actual calculation cycles are capped by the infidelities of the gate operations, so their number ranges between 10 and for now and the near future. Improving the fidelity of qubit operations is therefore key for being able to increase the number of gates and the usefulness of algorithms, as well as for implementing error correction schemes with reasonable qubit overhead.

Once error correction has been implemented, calculation cycles will be a dominant measure of performance. However, there is a price on clock speed that all gate-based technologies will have to pay for fault-tolerant quantum computing. Measurement times, required in known error-correction schemes, are in the range of microseconds. Thus, an upper limit on clock speed of about one megahertz emerges for future fault-tolerant quantum computers. This in turn will be a hurdle for the execution speed-up potential of quantum algorithms.

There is an additional important player in the industry: D-Wave, the first company to ever build any kind of still special-purpose quantum computer. D-Wave has sparked near-endless debates on whether its annealing truly performs quantum computing it is now largely accepted that D-Wave uses quantum-mechanical properties to run algorithms and how universal its specific type of problem solver can become. Enabling more general operations is the biggest hurdle for quantum annealers going forward.

The company also plans a new quantum chip by early with more than 4, still short-lived qubits and improved connectivity. Both could put D-Wave back into the game for real-time applications or inspire new classical algorithms during the NISQ period, when medium-sized gate-based quantum computers will still lack error correction.

## Quantum computation center opens

In summary, the near-term focus in quantum computing will be on what can be achieved over the next five years by the applications based on superconducting and ion trap circuit systems with a few hundred qubits each, as well as annealing. In parallel, the technology players will keep fighting it out for the next generation of scalable quantum computers. Quantum algorithms are the tools that tell quantum computers what to do. Two of their attributes are especially important in the near term:. There are two classes of algorithm today.

See Exhibit 8. We call the first purebreds—they are built for speed in noiseless or error-corrected environments.

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The ones shown in the exhibit have theoretically proven exponential speed-up over conventional computers for specific problems, but require a long sequence of flawless execution, which in turn necessitate very low noise operations and error correction. Unfortunately, their susceptibility to noise puts them out of the realm of practical application for the next ten years and perhaps longer. The other class, which we call workhorses, are very sturdy algorithms, but they have a somewhat uncertain speed-up over classical algorithms.

The members of this group, which include many more-recent algorithms, are designed to be robust in the face of noise and errors. They might have built-in error mitigation, but the most important feature is their shallow depth—that is, the number of gate operations is kept low. Most of them are then integrated with classical algorithms to enable longer, more productive loops although these still have to be wary of accumulating errors.

The workhorses should be able to run on anticipated machines in the qubit range the annealing approaches, although somewhat different, also fall into this category. The dilemma is that very little can be proven about their speed-up performance with respect to classical algorithms until they are put to experimental testing. Challenging any quantum advantage has become a favorite pastime of theorists and classical algorithm developers alike, with the most productive approaches actually improving upon the performance of the classical algorithms with which they are compared by creating new, quantum-inspired algorithms.

The lack of proof for speed-up might frustrate the purist, but not the practical computer scientist. Remember that deep learning, which today dominates the fast-growing field of AI, was also once a purely experimental success. Indeed, almost nothing had been proven theoretically about the performance of deep neural networks by when they started to win every AI and ML competition.

The real experiments in quantum computing of the coming years will be truly interesting. Ohzeki , H. Katzgraber , M. Sarvepalli , R. Mariantoni , H. Paik , D. Wang , A. Fowler , L. Quantum Inf. Fowler , D. Wang , L.

## Is quantum computing the end of security as we know it?

Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy , Terms of Service , and Dataset License. A group of scientists led by Australian of the Year Professor Michelle Simmons has achieved the first two-qubit gate between atom qubits in silicon -- a major milestone on the team's quest to build an atom-scale quantum computer. The pivotal piece of research was published today in the journal Nature.

A two-qubit gate is the central building block of any quantum computer -- and the UNSW team's version of it is the fastest that's ever been demonstrated in silicon, completing an operation in 0. In the Simmons' group approach, a two-qubit gate is an operation between two electron spins -- comparable to the role that classical logic gates play in conventional electronics. For the first time, the team was able to build a two-qubit gate by placing two atom qubits closer together than ever before, and then -- in real-time -- controllably observing and measuring their spin states.

## [] Surface codes: Towards practical large-scale quantum computation

The team's unique approach to quantum computing requires not only the placement of individual atom qubits in silicon but all the associated circuitry to initialise, control and read-out the qubits at the nanoscale -- a concept that requires such exquisite precision it was long thought to be impossible. But with this major milestone, the team is now positioned to translate their technology into scalable processors. We've also demonstrated that our atomic-scale circuitry has the lowest electrical noise of any system yet devised to connect to a semiconductor qubit.

Their next major goal is building a qubit quantum integrated circuit -- and we hope they reach that within years. Getting up and close with qubits -- engineering with a precision of just thousand-millionths of a metre. Using a scanning tunnelling microscope to precision-place and encapsulate phosphorus atoms in silicon, the team first had to work out the optimal distance between two qubits to enable the crucial operation. This allows us to engineer our two-qubit gate to be as fast as possible," says study lead co-author Sam Gorman from CQC2T. The team was then able to measure how the qubits states evolved in real-time.

And, most excitingly, the researchers showed how to control the interaction strength between two electrons on the nano-second timescale.