20 December 2020

Microsoft Topological Quantum Computing

Microsoft Topological Quantum Computing Approach

For faster quantum computing, Microsoft builds a better qubit

Microsoft's new approach to quantum computing is "very close," an executive says.

Mostly borrowed & updated from Steve Lamb in Microsoft Land….

Google just announced quantum supremacy, a milestone in which the radically different nature of a quantum computer lets it vastly outpace a traditional machine. But Microsoft expects progress of its own by redesigning the core element of quantum computing, the qubit.

Microsoft has been working on a qubit technology called a topological qubit that it expects will deliver benefits from quantum computing technology that today are mostly just a promise. After spending five years figuring out the complicated hardware of topological qubits, the company is almost ready to put them to use, said Krysta Svore, general manager of Microsoft's quantum computing software work.

"We've really spent the recent few years developing that technology," Svore said Thursday after a talk at the IEEE International Conference on Rebooting Computing. "We believe we're very close to having that."

Quantum computers are hard to understand, hard to build, hard to operate and hard to program. Since they only work when chilled to a tiny fraction of a degree above absolute zero -- colder than outer space -- you're not likely to have a quantum laptop anytime soon.

But running them in data centers where customers can tap into them could deliver profound benefits by tackling computing challenges that classical computers can't handle. Among examples Svore offered are solving chemistry problems like making fertilizer more efficiently, or routing trucks to speed deliveries and cut traffic.
Better qubits

Together, the phenomena should enable quantum computers to explore an enormous number of possible solutions to a problem at the same time. ... The key advantage of Microsoft's topological qubit is that fewer physical qubits are needed to make one logical qubit, Svore said.

Better qubits

Classical computers store data as a bit that represents either a 0 or a 1. Qubits, though, can store a combination of 0 and 1 simultaneously through a peculiar quantum physics principle called superposition. And qubits can be ganged together through another phenomenon called entanglement. Together, the phenomena should enable quantum computers to explore an enormous number of possible solutions to a problem at the same time.

Quantum Computing 101

One of the basic quantum computing problems is that qubits are easily perturbed. That's why the heart of a quantum computer is housed in a refrigerated container the size of a 55-gallon drum.

Even with that isolation, though, individual qubits today only can perform useful work for a fraction of a second. To compensate, quantum computer designers plan technology called error correction that yokes many qubits together into a single effective qubit, called a logical qubit. The idea is that logical qubits can perform useful processing work when many of their underlying physical qubits have gone astray.

The key advantage of Microsoft's topological qubit is that fewer physical qubits are needed to make one logical qubit, Svore said.

Specifically, she thinks one logical qubit will require 10 to 100 physical qubits with Microsoft's topological qubits. That compares to something like 1,000 to 20,000 physical qubits for other approaches.

"We believe that overhead will be far less," she said. That'll mean quantum computers will become practical with far fewer qubits.

By comparison, Google's Sycamore quantum computing chip used 53 physical qubits. For serious quantum computing work, researchers are hoping to reach qubit levels of at least a million.

Topological quantum computing with Majorana Fermions

One drawback of Microsoft's topological qubit, though, is that they're not available yet. Alternative designs might not work as well, but they're in real-world testing today.

Better quantum computing algorithms

Microsoft is also trying to improve other aspects of quantum computing. One is the control system, which in today's quantum computers is a snarl of hundreds of wires, each an expensive coaxial cable used to communicate with qubits.

On Monday at Microsoft's Ignite conference, the company also showed off a new quantum computer control system developed with the University of Sydney that uses many fewer wires -- down from 216 to just three, Svore said. "We think this will scale to tens of thousands of qubits and beyond."

And Svore pushed for progress on quantum computing software, too, urging professors to introduce their students to learning and improving quantum computing algorithms.

In one example of those benefits, Microsoft tackled an aspect of that nitrogen-fixing fertilizer problem that simply couldn't be solved on a classical machine -- but found that a quantum computer would still take 30,000 years.

That's faster than a classical computer that would require "the lifetime of the universe," but still not practical, she said. But with algorithm improvements, Microsoft found a way to shorten that to just a day and a half.

Non-Abelian Anyons & Topological Quantum Computation ESE 523

"New algorithms can be a breakthrough in how to solve something," Svore said. "We need to make them better, we need to optimize them, we need to be pushing."

Developing a Topological Qubit

As quantum technologies advance, we get closer to finding solutions to some of the world’s most challenging problems. While this new paradigm holds incredible possibility, quantum computing is very much in its infancy. To fully embrace the power and potential of quantum computing, the system must be engineered to meet the demands of the solutions the world needs most.

The fragile nature of qubits is well-known as one of the most significant hurdles in quantum computing. Even the slightest interference can cause qubits to collapse, making the solutions we’re pursuing impossible to identify because the computations cannot be completed.

Microsoft is addressing this challenge by developing a topological qubit. Topological qubits are protected from noise due to their values existing at two distinct points, making our quantum computer more robust against outside interference. This increased stability will help the quantum computer scale to complete longer, more complex computations, bringing the solutions we need within reach. 

Quantum computing explained to my (Schrödinger) cat - Alessandro Vozza - Codemotion Amsterdam 2018

Topology and quantum computing

Topology is a branch of mathematics describing structures that experience physical changes such as being bent, twisted, compacted, or stretched, yet still maintain the properties of the original form. When applied to quantum computing, topological properties create a level of protection that helps a qubit retain information despite what’s happening in the environment. The topological qubit achieves this extra protection in two different ways: 

Electron fractionalization.  By splitting the electron, quantum information is stored in both halves, behaving similarly to data redundancy. If one half of the electron runs into interference, there is still enough information stored in the other half to allow the computation to continue.  
Ground state degeneracy. Topological qubits are engineered to have two ground states—known as ground state degeneracy—making them much more resistant to environmental noise. Normally, achieving this protection isn’t feasible because there’s no way to discriminate between the two ground states. However, topological systems can use braiding or measurement to distinguish the difference, allowing them to achieve this additional protection.

From Bohr’s Atom to the Topological Quantum Computer | Charles Marcus

The path to the topological qubit

Currently years into the development of the topological qubit, the journey began with a single question, “Could a topological qubit be achieved”? Working with theory as a starting point, Microsoft brought together mathematicians, computer scientists, physicists, and engineers to explore possible approaches. These experts collaborated, discussed methods, and completed countless equations to take the first steps on the path toward realizing a topological qubit.

Modeling and experimentation work hand-in-hand as an ongoing, iterative cycle, guiding the design of the topological qubit. Throughout this process, the Microsoft team explored possible materials, ways to apply control structure, and methods to stabilize the topological qubit.

A team member proposed the use of a superconductor in conjunction with a strong magnetic field to create a topological phase of matter—an approach that has been adopted toward realizing the topological qubit. While bridging these properties has been long-taught, it had never been done in such a controlled way prior to this work.

To create the exact surface layer needed for the qubit, chemical compounds are currently being grown in Microsoft labs using a technique called “selective area growth.” Chosen for its atomic-level precision, this unique method can be described as spraying atoms in the exact arrangement needed to achieve the properties required.

Topological Quantum Computing

The team continues testing functional accuracy through device simulation, to ensure that every qubit will be properly tuned, characterized, and validated.

Bridging fields to advance technology

Many fields of knowledge have come together to realize the topological qubit, including mathematics, theoretical physics, solid state physics, materials science, instrumentation and measurement technology, computer science, quantum algorithms, quantum error correction, and software applications development.

Bridging these fields has led to breakthrough techniques across all aspects of realizing a topological qubit, including:

  • Theory and simulation – Turning a vision into reality by creating a rapid design, simulation, and prototyping process
  • Fabrication – Pioneering unique fabrication approaches and finding new ways to bridge properties
  • Materials growth – Developing inventive methods to create materials using special growth techniques to create the exact properties required at nanoscale
  • Measurement and quantum control – Tuning devices for accuracy in function and measurement
At Microsoft, the development of the topological qubit continues, bringing us closer to scalable quantum computing and finding solutions to some of the world’s most challenging problems.

Introduction to Topological Quantum Computing

A complete quantum system from hardware to software

The process of building a quantum computer includes creating the raw materials needed to make topological quantum devices, fabricating the cold electronics and refrigeration systems, and developing the overall infrastructure needed to bring the solution to life. In addition, our system includes everything you need to program the quantum computer, including a control system, software, development tools, and Azure services—a combination we refer to as our full quantum stack.

Because quantum and classical work together, Microsoft Azure is a perfect environment for quantum processing and deployment. With data stored in Azure, developers will be able to access quantum processing alongside classical processing, creating a streamlined experience.

Using the complete Microsoft quantum system, what would the start-to-finish experience look like?

Beginning with a problem you may be able to solve with a quantum algorithm…

You would start by building your solution in Visual Studio, using the tools found in the Microsoft Quantum Development Kit.

Using Q#, a language created specifically for quantum development, you would write the code for your solution with the help of the extensive Microsoft quantum libraries.

When your code is complete, you would run a quantum simulation to check for bugs and validate that your solution is ready for deployment.

Once validated, you would be ready to run your solution on the quantum computer.
Your quantum solution would be deployed from within Microsoft Azure, using the quantum computer as a co-processor. As many scenarios will use both quantum and classical processing, Azure will streamline workflows as real-time or batch applications, later connecting results directly into your business processes.

Together, this full quantum stack pairs with familiar tools to create an integrated, streamlined environment for quantum processing.

Scalability, from top to bottom

Quantum computers can help address some of the world’s toughest problems, provided the quantum computer has enough high-quality qubits to find the solution. While the quantum systems of today may be able to add a high number of qubits, the quality of the qubits is the key factor in creating useful scale.  From the cooling system to qubits to algorithms, scalability is a fundamental part of the Microsoft vision for quantum computing.

The topological qubit is a key ingredient in our scalable quantum system. Different from traditional qubits, a topological qubit is built in a way that automatically protects the information it holds and processes. Due to the fragile nature of conventional qubits, this protection offers a landmark improvement in performance, providing added stability and requiring fewer qubits overall. This critical benefit makes the ability to scale possible.

Microsoft has been working on scalable quantum computing for nearly two decades, creating its first quantum computing group—known as Station Q—in 2006. Investing in scalable quantum computing for over a decade, we have connected some of the brightest minds in the industry and academia to make this dream a reality. Blending physics, mathematics, engineering, and computer science, teams around the globe work daily to advance the development of the topological qubit and the Microsoft vision for quantum computing.

Empowering the quantum revolution

At Microsoft, we envision a future where quantum computing is available to a broad audience, scaling as needed to solve some of the world’s toughest challenges. Our quantum approach begins within familiar tools you know and use such as Visual Studio. It provides development resources to build and simulate your quantum solutions. And it continues with deployment through Azure for a streamlined combination of both quantum and classical processing.

As the path to build a quantum computer continues, challenges from across industries await solutions from this new computational power. One of the many examples of high-impact problems that can be solved on a quantum computer is developing a new alternative to fertilizer production. Making fertilizer requires a notable percentage of the world’s annual production of natural gas. This implies high cost, high energy waste, and substantial greenhouse emissions. Quantum computers can help identify a new alternative by analyzing nitrogenase, an enzyme in plants that converts nitrogen to ammonia naturally. To address this problem, a quantum computer would require at least 200 fault-free qubits—far beyond the small quantum systems of today. In order to find a solution, quantum computers must scale up. The challenge, however, is that scaling a quantum computer isn’t merely as simple as adding more qubits.

Building a quantum computer differs greatly from building a classical computer. The underlying physics, the operating environment, and the engineering each pose their own obstacles. With so many unique challenges, how can a quantum computer scale in a way that makes it possible to solve some of the world’s most challenging problems?

Introduction to Quantum Computer

Navigating obstacles

Most quantum computers require temperatures colder than those found in deep space. To reach these temperatures, all the components and hardware are contained within a dilution refrigerator—highly specialized equipment that cools the qubits to just above absolute zero. Because standard electronics don’t work at these temperatures, a majority of quantum computers today use room-temperature control. With this method, controls on the outside of the refrigerator send signals through cables, communicating with the qubits inside. The challenge is that this method ultimately reaches a roadblock: the heat created by the sheer number of cables limits the output of signals, restraining the number of qubits that can be added.

As more control electronics are added, more effort is needed to maintain the very low temperature the system requires. Increasing both the size of the refrigerator and the cooling capacity is a potential option, however, this would require additional logistics to interface with the room temperature electronics, which may not be a feasible approach.

Another alternative would be to break the system into separate refrigerators. Unfortunately, this isn’t ideal either because the transfer of quantum data between the refrigerators is likely to be slow and inefficient.

At this stage in the development of quantum computers, size is therefore limited by the cooling capacity of the specialized refrigerator. Given these parameters, the electronics controlling the qubits must be as efficient as possible.

Physical qubits, logical qubits, and the role of error correction

By nature, qubits are fragile. They require a precise environment and state to operate correctly, and they’re highly prone to outside interference. This interference is referred to as ‘noise’, which is a consistent challenge and a well-known reality of quantum computing. As a result, error correction plays a significant role.

As a computation begins, the initial set of qubits in the quantum computer are referred to as ‘physical qubits’. Error correction works by grouping many of these fragile physical qubits, which creates a smaller number of usable qubits that can remain immune to noise long enough to complete the computation. These stronger, more stable qubits used in the computation are referred to as ‘logical qubits’.

In classical computing, noisy bits are fixed through duplication (parity and Hamming codes), which is a way to correct errors as they occur. A similar process occurs in quantum computing, but is more difficult to achieve. This results in significantly more physical qubits than the number of logical qubits required for the computation. The ratio of physical to logical qubits is influenced by two factors: 1) the type of qubits used in the quantum computer, and 2) the overall size of the quantum computation performed. And due to the known difficulty of scaling the system size, reducing the ratio of physical to logical qubits is critical. This means that instead of just aiming for more qubits, it is crucial to aim for better qubits.

Stability and Scale with a Topological Qubit

The topological qubit is a type of qubit that offers more immunity to noise than many traditional types of qubits. Topological qubits are more robust against outside interference, meaning fewer total physical qubits are needed when compared to other quantum systems. With this improved performance, the ratio of physical to logical qubits is reduced, which in turn, creates the ability to scale.

As we know from Schrödinger’s cat, outside interactions can destroy quantum information. Any interaction from a stray particle, such as an electron, a photon, a cosmic ray, etc., can cause the quantum computer to decohere.

There is a way to prevent this: parts of the electron can be separated, creating an increased level of protection for the information stored. This is a form of topological protection known as a Majorana quasi-particle. The Majorana quasi-particle was predicted in 1937 and was detected for the first time in the Microsoft Quantum lab in the Netherlands in 2012. This separation of the quantum information creates a stable, robust building block for a qubit. The topological qubit provides a better foundation with lower error rates, reducing the ratio of physical to logical qubits. With this reduced ratio, more logical qubits are able to fit inside the refrigerator, creating the ability to scale.

If topological qubits were used in the example of nitrogenase simulation, the required 200 logical qubits would be built out of thousands of physical qubits. However, if more traditional types of qubits were used, tens or even hundreds of thousands of physical qubits would be needed to achieve 200 logical qubits. The topological qubit’s improved performance causes this dramatic difference; fewer physical qubits are needed to achieve the logical qubits required.

Developing a topological qubit is extremely challenging and is still underway, but these benefits make the pursuit well worth the effort.

A solid foundation to tackle problems unsolved by today’s computers
A significant number of logical qubits are required to address some of the important problems currently unsolvable by today’s computers. Yet common approaches to quantum computing require massive numbers of physical qubits in order to reach these quantities of logical qubits—creating a huge roadblock to scalability. Instead, a topological approach to quantum computing requires far fewer physical qubits than other quantum systems, making scalability much more achievable.

Providing a more solid foundation, the topological approach offers robust, stable qubits, and helps to bring the solutions to some of our most challenging problems within reach.

Microsoft's Approach: Topological Systems

At Microsoft Quantum, our ambition is to help solve some of the world’s most complex problems by developing scalable quantum technology. Our global team of researchers, scientists, and engineers are addressing this challenging task by developing a topological qubit.

To realize this vision, our teams have been making advances in materials and device fabrication, designing the precise physical environment required to support the topological state of matter. The latest discovery by the team expands the landscape for creating and controlling the exotic particles critical for enabling topological superconductivity in nanoscale devices.

Discovery: a new route to topology

Our qubit architecture is based on nanowires, which under certain conditions (low-temperature, magnetic field, material choice) can enter a topological state. Topological quantum hardware is intrinsically robust against local sources of noise, making it particularly appealing as we scale up the number of qubits.

An intriguing feature of topological nanowires is that they support Majorana zero modes (MZMs) that are neither fermions nor bosons. Instead, they obey different, more exotic quantum exchange rules. If kept apart and braided around each other, similar to strands of hair, MZMs remember when they encircle each other. Such braiding operations act as quantum gates on a state, allowing for a new kind of computation that relies on the topology of the braiding pattern.

A topological qubit is constructed by arranging several nanowires hosting MZMs in a comb-like structure and coupling them in a specific way that lets them share multiple MZMs. The first step in building a topological qubit is to reliably establish the topological phase in these nanowires.

While exploring the conditions for the creation of topological superconductivity, the team discovered a topological quantum vortex state in the core of a semiconductor nanowire surrounded on all sides by a superconducting shell. They were very surprised to find Majorana modes in the structure, akin to a topological vortex residing inside of a nanoscale coaxial cable.

With hindsight, the findings can now be understood as a novel topological extension of a 50-year old piece of physics known as the Little-Parks effect. In the Little-Parks effect, a superconductor in the shape of a cylindrical shell – analogous to a soda straw – adjusts to an external magnetic field, threading the cylinder by jumping to a “vortex state” where the quantum wavefunction around the cylinder carries a twist. The quantum wavefunction must close on itself.

Thus, the wavefunction phase accumulated by going around the cylinder must take the values zero, one, two, and so on, in units of 2π. This has been known for decades. What had not been explored in depth was what those twists do to the semiconductor core inside the superconducting shell. The surprising discovery made by the Microsoft team—experiment and theory—was a twist in the shell, under appropriate conditions, can make a topological state in the core, with MZMs localized at the opposite ends.

While signatures of Majorana modes have been reported in related systems without the fully surrounding cylindrical shell, these previous realizations placed rather stringent requirements on materials and required large magnetic fields. This discovery places few requirements on materials and needs a smaller magnetic field, expanding the landscape for creating and controlling Majoranas.

Worldwide collaboration

What started as two separate papers – one experimental, the other theoretical – was combined into a single publication that tells the complete story, with mutual support of experiment, theory, and numerics.

Of course, looking back, deep connections to previous ideas and experiments can now be recognized, and results that were first mysterious now seem inevitable. That is the nature of scientific progress: from seemingly impossible to seemingly obvious after a few months of making, measuring, and thinking.

Saulius Vaitiekėnas, then a PhD student and postdoc at the Niels Bohr Institute, University of Copenhagen, and now a newly minted Microsoft researcher, was the main experimentalist. As he comments, “The paper represents a series of surprises. And it was really exciting to see so many different disciplines come together, all in a united activity.”

Roman Lutchyn, Principal Research Manager and lead of the theoretical effort, reflected on the collaboration process. “Microsoft Quantum started with just a small group in Santa Barbara. Now we’ve grown into a much broader organization with labs all around the world – Copenhagen, Delft, Purdue, Sydney, Redmond, among others. I think this paper is a landmark in our partnership between teams and is a model of how we can work effectively together as one team – around the world – on related ideas in physics, ultimately generating new and potentially important results.”

Charles Marcus, Scientific Director of Microsoft Quantum Lab – Copenhagen and lead for the experimental effort, concurs, “[This paper is an example] where two results – from theory and experiment – help each other to make more conclusive statements about physics. Otherwise, we would have been left with more abstract theory; and experimentally, we would have measurements but may have hedged on interpretation. By merging theory and experiment, the overall story is stronger and also more interesting, seeing the connection to related phenomena in different systems.”

Inside Microsoft’s Quest to Make Quantum Computing Scalable

The company’s researchers are building a system that’s unlike any other quantum computer being developed today.

Introduction to topological superconductivity and Majorana fermions

There’s no shortage of big tech companies building quantum computers, but Microsoft claims its approach to manufacturing qubits will make its quantum computing systems more powerful than others’. The company’s researchers are pursuing “topological” qubits, which store data in the path of moving exotic Majorana particles. This is different from storing it in the state of electrons, which is fragile.

That’s according to Krysta Svore, research manager in Microsoft’s Quantum Architectures and Computation group. The Majorana particle paths -- with a fractionalized electron appearing in many places along them -- weave together like a braid, which makes for a much more robust and efficient system, she said in an interview with Data Center Knowledge. These qubits are called “topological cubits,” and the systems are called “topological quantum computers.”

With other approaches, it may take 10,000 physical qubits to create a logical qubit that’s stable enough for useful computation, because the state of the qubits storing the answer to your problem “decoheres” very easily, she said. It’s harder to disrupt an electron that’s been split up along a topological qubit, because the information is stored in more places.

In quantum mechanics, particles are represented by wavelengths. Coherence is achieved when waves that interfere with each other have the same frequency and constant phase relation. In other words, they don’t have to be in phase with each other, but the difference between the phases has to remain constant. If it does not, the particle states are said to decohere.
“We’re working on a universally programmable circuit model, so any other circuit-based quantum machine will be able to run the same class of algorithms, but we have a big differentiator,” Svore said. “Because the fidelity of the qubit promises to be several orders of magnitude better, I can run an algorithm that’s several orders of magnitude bigger. If I can run many more operations without decohering, I could run a class of algorithm that in theory would run on other quantum machines but that physically won’t give a good result. Let’s say we’re three orders of magnitude better; then I can run three orders of magnitude more operations in my quantum circuit.”
Theoretically, that could mean a clear advantage of a quantum computer over a classical one. “We can have a much larger circuit which could theoretically be the difference between something that shows quantum advantage or not. And for larger algorithms, where error corrections are required, we need several orders of magnitude less overhead to run that algorithm,” she explained.

A Hardware and Software System that Scales

Microsoft has chosen to focus on topological qubits because the researchers believe it will scale, and the company is also building a complete hardware stack to support the scaling. “We’re building a cryogenic computer to control the topological quantum chip; then we're building a software system where you can compile millions of operations and beyond.”

The algorithms running on the system could be doing things like quantum chemistry – looking for more efficient fertilizer or a room temperature semiconductor – or improving machine learning. Microsoft Research has already shown that deep learning trains faster with a quantum computer. With the same deep learning models in use today, Svore says, the research shows “quadratic speedups” even before you start adding quantum terms to the data model, which seems to improve performance even further.

Redesigning a Programming Language

To get developers used to the rather different style of quantum programming, Microsoft will offer a new set of tools in preview later this year (which doesn’t have a name yet) that’s a superset built on what it learned from the academics, researchers, students, and developers who used Liquid, an embedded domain specific language in F# that Microsoft created some years ago.

The language itself has familiar concepts like functions, if statements, variables, and branches, but it also has quantum-specific elements and a growing set of libraries developers can call to help them build quantum apps.

“We’ve almost completely redesigned the language; we will offer all the things Liquid had, but also much more, and it’s not an embedded language. It’s really a domain-specific language designed upfront for scalable quantum computing, and what we’ve tried to do is raise the level of abstraction in this high-level language with the ability to call vast numbers of libraries and subroutines.”

Some of those are low-level subroutines like an adder, a multiplier, and trigonometry functions, but there are also higher-level functions that are commonly used in quantum computing. “Tools like phase estimation, amplitude amplification, amplitude estimation -- these are very common frameworks for your quantum algorithms. They’re the core framework for setting up your algorithm to measure and get the answer out at the end [of the computation], and they’re available in a very pluggable way.”

Quantum computing

A key part of making the language accessible is the way it’s integrated into Visual Studio, Microsoft’s IDE. “I think this is a huge step forward,” Svore told us. “It makes it so much easier to read the code because you get the syntax coloring and the debugging; you can set a breakpoint, you can visualise the quantum state.”

Being able to step through your code to understand how it works is critical to learning a new language or a new style of programming, and quantum computing is a very different style of computing.

“As we’ve learned about quantum algorithms and applications, we’ve put what we’ve learned into libraries to make it easier for a future generation of quantum developers,” Svore said. “Our hope is that as a developer you’re not having to think at the lower level of circuits and probabilities. The ability to use these higher-level constructs is key.”

Hybrid Applications

The new language will also make it easier to develop hybrid applications that use both quantum and classical computing, which Svore predicts will be a common pattern. “With the quantum computer, many of the quantum apps and algorithms are hybrid. You're doing pre and post-processing or in some algorithms you’ll even be doing a very tight loop with a classical supercomputer.”

How Many Qubits Can You Handle?

Microsoft, she says, is making progress with its topological qubits, but, as it’s impossible to put any kind of date on when a working system might emerge from all this work, the company will come out with a quantum simulator to actually run the programs you write, along with the other development tools.

Depending on how powerful your system is, you’ll be able to simulate between 30 and 33 qubits on your own hardware. For 40 qubits and more, you can do the simulation on Azure.

“At 30 qubits, it takes roughly 16GB of classical memory to store that quantum state, and each operation takes a few seconds,” Svore explains. But as you simulate more qubits, you need a lot more resources. Ten qubits means adding two to the power of 10, or 16TB of memory and double that to go from 40 to 41 qubits. Pretty soon, you’re hitting petabytes of memory. “At 230 qubits, the amount of memory you need is 10^80 bytes, which is more bytes than there are particles in the physical universe, and one operation takes the lifetime of the universe,” Svore said. “But in a quantum computer, that one operation takes 100 nanoseconds.”

Microsoft’s broad-based quantum effort

LIQUi|> is one of a number of quantum computing projects Microsoft researchers have been spearheading for more than a decade, in the quest to create the next generation of computing that will have a profound effect on society.
In addition to the QuArC research group, Microsoft’s Station Q research lab, led by renowned mathematician Michael Freedman, is pursuing an approach called topological quantum computing that they believe will be more stable than other quantum computing methods.
The idea is to design software, hardware and other elements of quantum computing all at the same time.
“This isn’t just, ‘Make the qubits.’ This is, ‘Make the system,’” Wecker said.
A qubit is a unit of quantum information, and it’s the key building block to a quantum computer. Using qubits, researchers believe that quantum computers could very quickly evaluate multiple solutions to a problem at the same time, rather than sequentially. That would give scientists the ability to do high-speed, complex calculations, allowing biologists, physicists and chemists to get information they never thought possible before.

LIQUiD - Station Q overview

Fertilizer, batteries and climate change

Take fertilizer, for example. Fertilizers are crucial to feeding the world’s growing population because they allow plants to develop better and faster. But synthetic fertilizer relies on natural gas, and lots of it: That’s expensive, depletes an important natural resource and adds to pollution.
Using a quantum computer, Wecker said scientists think they could map the chemical used by bacteria that naturally creates fertilizers, making it easier to create an alternative to the current, natural-gas based synthetic fertilizer.
The incredible power of quantum computers also could be used to figure out how to create organic batteries that don’t rely on lithium, and Wecker said they could help to create systems for capturing carbon emissions effectively, potentially reducing the effects of climate change.
Researchers believe that quantum computers will be ideal for challenges like this, which involve mapping complex physical systems, but they also know that they won’t be the best choice for all computing problems. That’s because quantum computers operate very differently from classical digital computers.
Although quantum computers can process data much faster, it’s much more difficult to get the results of their calculations because of how qubits are structured. A person using a quantum system needs to know the right question to ask in order to efficiently get the answer they want.
For now at least, quantum computer scientists also are struggling to create systems that can run lots of qubits. Because qubits are essentially a scarce resource, Svore said another big research focus is on how to minimize the number of qubits needed to do any algorithm or calculation. That’s also one of the main focuses of Station Q, which is using an area of math called topology to find ways to use fewer qubits.
Wecker said that’s another major advantage to a system like LIQUi|>: It will help researchers figure out how best to use these unique computers.

As quantum computing technology becomes increasingly sophisticated, the techniques required to calibrate and certify device performance are becoming commensurately sophisticated. In this talk, I will discuss the need for QCVV (quantum characterization, verification, and validation) protocols to facilitate advances towards fault-tolerant universal quantum computation. In particular, I'll examine what kind of errors we expect nascent quantum information processors to suffer from, and how the QCVV tools may be used for detecting, diagnosing, and ultimately correcting such errors. To illustrate this point, I will examine the role gate set tomography (GST) played in characterizing quantum operations on a trapped-Yb-ion qubit, and how GST was iteratively used to a) make the qubit gate behavior Markovian and b) verify that the errors on the qubit operations were below the threshold for fault-tolerance. Lastly, several "GST-adjacent" QCVV protocols, such as drift- and cross-talk detection will be examined, and the future of QCVV research will be discussed.
This work was supported by the Intelligence Advanced Research Projects Activity (IARPA), and Sandia's Laboratory Directed Research and Development (LDRD) Program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA- 0003525.

Better Quantum Living Through QCVV

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