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Quantum Mechanics created their chapter in the history of the early 20th Century. With its regular binary computing twin going out of style, quantum mechanics led quantum computing to be the new belle of the ball! While the memory used in a classical computer encodes binary bits one and zero, quantum computers use qubits (quantum bits). And Qubit is not confined to a two-state solution, but can also exist in superposition i.e., qubits can be employed at 0, 1 and both 1 and 0 at the same time.

Hence it can perform many calculations in parallel owing to the ability to pursue simultaneous probabilities through superposition along with manipulating them with magnetic fields. Its coefficients allow predicting how much zero-ness and one-ness it has, are complex numbers, which indicates the real and imaginary part. This provides a huge technical edge over other conventional computing. The beauty of this is if you have n qubits, you can have a superposition of 2n states or bits of information simultaneously.

Another magic up its sleeve is that Qubits are capable of pairing which is referred to as entanglement. Here, the state of one qubit cannot be described independently of the state of the others which allows instantaneous communication.

To quote American theoretical physicist, John Wheeler, If you are not completely confused by quantum mechanics, you do not understand it. So, without a doubt it is safe to say that even quantum computing has few pitfalls. First, the qubits tend to loss the information they contain, and also lose their entanglement in other words, decoherence. Second, imperfections of quantum rotations. These led to a loss of information within a few microsecond.

Ultimately, quantum computing is the Trump Card as promises to be a disruptive technology with such dramatic speed improvements. This will enable systems to solve complex higher-order mathematical problems that earlier took months to be computed, investigate material properties, design new ones, study superconductivity, aid in drug discovery via simulation and understanding new chemical reactions.

This quantum shift in the history of computer sciences can also pave way for encrypted communication (as keys cannot be copied nor hacked), much better than Blockchain technology, provide improved designs for solar panels, predict financial markets, big data mining, develop Artificial Intelligence to new heights, enhanced meteorological updates and a much-anticipated age of quantum internet. According to scientists, Future advancements can also lead to help find a cure for Alzheimers.

The ownership and effective employment of a quantum computer could change the political and technological dynamics of the world. Computing power, in the end, is power whether it is personal, national or globally strategic. In short, a quantum computer could be an existential threat to a nation that hasnt got one. At the moment Google, IBM, Intel, and D-Wave are pursuing this technology. While there are scientific minds who dont believe in the potential of quantum computing yet unless you are a time-traveler like Marty McFly in Back to the Future series or any one of the Doctor Who, one cannot say what future beholds.

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Back in February 2020, scientists from the U.S. Department of Energy's Argonne National Laboratory and the University of Chicago revealed that they had achieved a quantum entanglement in which the behavior of a pair two tiny particles becomes linked, so that their states are identical over a 52-mile (83.7 kilometer) quantum-loop network in the Chicago suburbs.

You may be wondering what all the fuss is about, if you're not a scientist familiar with quantum mechanics that is, the behavior of matter and energy at the smallest scale of reality, which is peculiarly different from the world we can see around us.

But the researchers' feat could be an important step in the development of a new, vastly more powerful version of the internet in the next few decades. Instead of the bits that today's network uses, which can only express a value of either 0 or 1, the future quantum internet would utilize qubits of quantum information, which can take on an infinite number of values. (A quibit is the unit of information for a quantum computer; it's like a bit in an ordinary computer).

That would give the quantum internet way more bandwidth, which would make it possible to connect super-powerful quantum computers and other devices and run massive applications that simply aren't possible with the internet we have now.

"A quantum internet will be the platform of a quantum ecosystem, where computers, networks, and sensors exchange information in a fundamentally new manner where sensing, communication, and computing literally work together as one entity, " explains David Awschalom via email. He's a spintronics and quantum information professor in the Pritzker School of Molecular Engineering at the University of Chicago and a senior scientist at Argonne, who led the quantum-loop project.

So why do we need this and what does it do? For starters, the quantum internet is not a replacement of the regular internet we now have. Rather it would be a complement to it or a branch of it. It would be able to take care of some of the problems that plague the current internet. For instance, a quantum internet would offer much greater protection from hackers and cybercriminals. Right now, if Alice in New York sends a message to Bob in California over the internet, that message travels in more or less a straight line from one coast to the other. Along the way, the signals that transmit the message degrade; repeaters read the signals, amplify and correct the errors. But this process allows hackers to "break in" and intercept the message.

However, a quantum message wouldn't have that problem. Quantum networks use particles of light photons to send messages which are not vulnerable to cyberattacks. Instead of encrypting a message using mathematical complexity, says Ray Newell, a researcher at Los Alamos National Laboratory, we would rely upon the peculiar rules of quantum physics. With quantum information, "you can't copy it or cut it in half, and you can't even look at it without changing it." In fact, just trying to intercept a message destroys the message, as Wired magazine noted. That would enable encryption that would be vastly more secure than anything available today.

"The easiest way to understand the concept of the quantum internet is through the concept of quantum teleportation," Sumeet Khatri, a researcher at Louisiana State University in Baton Rouge, says in an email. He and colleagues have written a paper about the feasibility of a space-based quantum internet, in which satellites would continually broadcast entangled photons down to Earth's surface, as this Technology Review article describes.

"Quantum teleportation is unlike what a non-scientist's mind might conjure up in terms of what they see in sci-fi movies, " Khatri says. "In quantum teleportation, two people who want to communicate share a pair of quantum particles that are entangled. Then, through a sequence of operations, the sender can send any quantum information to the receiver (although it can't be done faster than light speed, a common misconception). This collection of shared entanglement between pairs of people all over the world essentially constitutes the quantum internet. The central research question is how best to distribute these entangled pairs to people distributed all over the world. "

Once it's possible to do that on a large scale, the quantum internet would be so astonishingly fast that far-flung clocks could be synchronized about a thousand times more precisely than the best atomic clocks available today, as Cosmos magazine details. That would make GPS navigation vastly more precise than it is today, and map Earth's gravitational field in such detail that scientists could spot the ripple of gravitational waves. It also could make it possible to teleport photons from distant visible-light telescopes all over Earth and link them into a giant virtual observatory.

"You could potentially see planets around other stars, " says Nicholas Peters, group leader of the Quantum Information Science Group at Oak Ridge National Laboratory.

It also would be possible for networks of super-powerful quantum computers across the globe to work together and create incredibly complex simulations. That might enable researchers to better understand the behavior of molecules and proteins, for example, and to develop and test new medications.

It also might help physicists to solve some of the longstanding mysteries of reality. "We don't have a complete picture of how the universe works," says Newell. "We have a very good understanding of how quantum mechanics works, but not a very clear picture of the implications. The picture is blurry where quantum mechanics intersects with our lived experience."

But before any of that can happen, researchers have to figure out how to build a quantum internet, and given the weirdness of quantum mechanics, that's not going to be easy. "In the classical world you can encode information and save it and it doesn't decay, " Peters says. "In the quantum world, you encode information and it starts to decay almost immediately. "

Another problem is that because the amount of energy that corresponds to quantum information is really low, it's difficult to keep it from interacting with the outside world. Today, "in many cases, quantum systems only work at very low temperatures," Newell says. "Another alternative is to work in a vacuum and pump all the air out. "

In order to make a quantum internet function, Newell says, we'll need all sorts of hardware that hasn't been developed yet. So it's hard to say at this point exactly when a quantum internet would be up and running, though one Chinese scientist has envisioned that it could happen as soon as 2030.

Read more from the original source:
We're Getting Closer to the Quantum Internet, But What Is It? - HowStuffWorks

The collaboration will focus on developing a superconducting quantum processor, hardware -aware software and custom algorithms that take direct advantage of the hardware advances to solve scheduling and asset allocation problems. In addition, the team will design methods for benchmarking the hardware against classical computers to determine quantum advantage.

USRA Senior Vice President Bernie Seery noted, This is a very exciting public-private partnership for the development of forefront quantum computing technology and the algorithms that will be used to address pressing, strategically significant challenges. We are delighted to receive this award and look forward to working with our partner institutions to deliver value to DARPA.

In particular, the work will target scheduling problems whose complexity goes beyond what has been done so far with the quantum approximate optimization algorithm (QAOA). USRAs Research Institute for Advanced Computer Science (RIACS) has been working on quantum algorithms for planning and scheduling for NASA QuAIL since 2012. The innovations on quantum gates performed by Rigetti coupled perfectly with the recent research ideas at QuAIL, enabling an unprecedented hardware-theory co-design opportunity explains Dr. Venturelli, USRA Associate Director for Quantum Computing and project PI for USRA. Understanding how to use quantum computers for scheduling applications could have important implications for national security such as real time strategic asset deployment, as well as commercial applications including global supply chain management, network optimizations or vehicle routing.

The grant is a part of the DARPA Optimization with Noisy Intermediate-Scale Quantum program (ONISQ). The goal of this program is to establish that quantum information processing using NISQ devices has a quantitative advantage for solving real-world-combinatorial optimization problems using the QAOA method.

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Universities Space Research Association to Lead a DARPA Project on Quantum Computing - Quantaneo, the Quantum Computing Source

Since 1965, the computer industry has relied on Moores Law to accelerate innovation, pushing more transistors into integrated circuits to improve computation performance. Making transistors smaller helped lift all boats for the entire industry and enable new applications. At some point, we will reach a physical limit that is, a limit stemming from physics itself. Even with this setback, improvements kept on pace thanks to increased parallelism of computation and consolidation of specialized functions into single chip packages, such as systems on chip).

In recent years, we are nearing another peak. This article proposes to improve computation performance not only by building better hardware, but by changing how we use existing hardware. More specifically, the focusing on how we use existing processor types. I call this approach Compute Orchestration: automatic optimization of machine code to best use the modern datacenter hardware (again, with special emphasis on different processor types).

So what is compute orchestration? It is the embracing of hardware diversity to support software.

There are many types of processors: Microprocessors in small devices, general purpose CPUs in computers and servers, GPUs for graphics and compute, and programmable hardware like FPGAs. In recent years, specialized processors like TPUs and neuromorphic processors for machine learning are rapidly entering the datacenter.

There is potential in this variety: Instead of statically utilizing each processor for pre-defined functions, we can use existing processors as a swarm, each processor working on the most suitable workloads. Doing that, we can potentially deliver more computation bandwidth with less power, lower latency and lower total cost of ownership).

Non-standard utilization of existing processors is already happening: GPUs, for example, were already adapted from processors dedicated to graphics into a core enterprise component. Today, GPUs are used for machine learning and cryptocurrency mining, for example.

I call the technology to utilize the processors as a swarm Compute Orchestration. Its tenets can be described in four simple bullets:

Compute orchestration is, in short, automatic adaptation of binary code and automatic allocation to the most suitable processor types available. I split the evolution of compute orchestration into four generations:

Compute Orchestration Gen 1: Static Allocation To Specialized Co-Processors

This type of compute orchestration is everywhere. Most devices today include co-processors to offload some specialized work from the CPU. Usually, the toolchain or runtime environment takes care of assigning workloads to the co-processor. This is seamless to the developer, but also limited in functionality.

Best known example is the use of cryptographic co-processors for relevant functions. Being liberal in our definitions of co-processor, Memory Management Units (MMUs) to manage virtual memory address translation can also be considered an example.

Compute Orchestration Gen 2: Static Allocation, Heterogeneous Hardware

This is where we are at now. In the second generation, the software relies on libraries, dedicated run time environments and VMs to best use the available hardware. Lets call the collection of components that help better use the hardware frameworks. Current frameworks implement specific code to better use specific processors. Most prevalent are frameworks that know how to utilize GPUs in the cloud. Usually, better allocation to bare metal hosts remains the responsibility of the developer. For example, the developer/DevOps engineer needs to make sure a machine with GPU is available for the relevant microservice. This phenomenon is what brought me to think of Compute Orchestration in the first place, as it proves there is more slack in our current hardware.

Common frameworks like OpenCL allow programming compute kernels to run on different processors. TensorFlow allows assigning nodes in a computation graph to different processors (devices).

This better use of hardware by using existing frameworks is great. However, I believe there is a bigger edge. Existing frameworks still require effort from the developer to be optimal they rely on the developer. Also, no legacy code from 2016 (for example) is ever going to utilize a modern datacenter GPU cluster. My view is that by developing automated and dynamic frameworks, that adapt to the hardware and workload, we can achieve another leap.

Compute Orchestration Gen 3: Dynamic Allocation To Heterogeneous Hardware

Computation can take an example from the storage industry: Products for better utilization and reliability of storage hardware have innovated for years. Storage startups develop abstraction layers and special filesystems that improve efficiency and reliability of existing storage hardware. Computation, on the other hand, remains a stupid allocation of hardware resources. Smart allocation of computation workloads to hardware could result in better performance and efficiency for big data centers (for example hyperscalers like cloud providers). The infrastructure for such allocation is here, with current data center designs pushing to more resource disaggregation, introduction of diverse accelerators, and increased work on automatic acceleration (for example: Workload-aware Automatic Parallelization for Multi-GPU DNN Training).

For high level resource management, we already have automatic allocation. For example, project Mesos (paper) focusing on fine-grained resource sharing, Slurm for cluster management, and several extensions using Kubernetes operators.

To further advance from here would require two steps: automatic mapping of available processors (which we call the compute environment) and workload adaptation. Imagine a situation where the developer doesnt have to optimize her code to the hardware. Rather, the runtime environment identifies the available processing hardware and automatically optimizes the code. Cloud environments are heterogeneous and changing, and the code should change accordingly (in fact its not the code, but the execution model in the run time environment of the machine code).

Compute Orchestration Gen 4: Automatic Allocation To Dynamic Hardware

A thought, even a possibility, can shatter and transform us. Friedrich Wilhelm Nietzsche

The quote above is to say that there we are far from practical implementation of the concept described here (as far as I know). We can, however, imagine a technology that dynamically re-designs a data center to serve needs of running applications. This change in the way whole data centers meet computation needs as already started. FGPAs are used more often and appear in new places (FPGAs in hosts, FPGA machines in AWS, SmartNICs), providing the framework for constant reconfiguration of hardware.

To illustrate the idea, I will use an example: Microsoft initiated project Catapult, augmenting CPUs with an interconnected and configurable compute layer composed of programmable silicon. The timeline in the projects website is fascinating. The project started off in 2010, aiming to improve search queries by using FPGAs. Quickly, it proposed the use of FPGAs as bumps in the wire, adding computation in new areas of the data path. Project Catapult also designed an architecture for using FPGAs as a distributed resource pool serving all the data center. Then, the project spun off Project BrainWave, utilizing FPGAs for accelerating AI/ML workloads.

This was just an example of innovation in how we compute. Quick online search will bring up several academic works on the topic. All we need to reach the 4th generation is some idea synthesis, combining a few concepts together:

Low effort HDL generation (for example Merlin compiler, BORPH)

In essence, what I am proposing is to optimize computation by adding an abstraction layer that:

Automatic allocation on agile hardware is the recipe for best utilizing existing resources: faster, greener, cheaper.

The trends and ideas mentioned in this article can lead to many places. It is very likely, that we are already working with existing hardware in the optimal way. It is my belief that we are in the midst of the improvement curve. In recent years, we had increased innovation in basic hardware building blocks, new processors for example, but we still have room to improve in overall allocation and utilization. The more we deploy new processors in the field, the more slack we have in our hardware stack. New concepts, like edge computing and resource disaggregation, bring new opportunities for optimizing legacy code by smarter execution. To achieve that, legacy code cant be expected to be refactored. Developers and DevOps engineers cant be expected to optimize for the cloud configuration. We just need to execute code in a smarter way and that is the essence of compute orchestration.

The conceptual framework described in this article should be further explored. We first need to find the killer app (what type of software we optimize to which type of hardware). From there, we can generalize. I was recently asked in a round table what is the next generation of computation? Quantum computing? Tensor Processor Units? I responded that all of the above, but what we really need is better usage of the existing generation.

Guy Harpak is the head of technology at Mercedes-Benz Research & Devcelopment in its Tel Aviv, Israel facility. Please feel free to contact him on any thoughts on the topics above at harpakguy@gmail.com. Harpak notes that this contributed article reflects his personal opinion and is in no way related to people or companies that he works with or for.

Related Reading: If you find this article interesting, I would recommend researching the following topics:

Some interesting articles on similar topics:

Return Of The Runtimes: Rethinking The Language Runtime System For The Cloud 3.0 Era

The Deep Learning Revolution And Its Implications For Computer Architecture And Chip Design (by Jeffrey Dean from Google Research)

Beyond SmartNICs: Towards A Fully Programmable Cloud

Hyperscale Cloud: Reimagining Datacenters From Hardware To Applications

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