Venture capital firms are pouring billions into quantum computing companies, hedging bets that the technology will pay off big time some day.

Rigetti, which makes quantum hardware, announced a $1.5bn merger with Supernova Partners Acquisition Company II, a finance house focusing on strategic acquisitions. Rigetti, which was valued at $1.04bn before the deal, will now be publicly traded.

Before Rigetti's deal, quantum computer hardware and software companies raked in close to $1.02bn from venture capital investments this year, according to numbers provided to The Register by financial research firm PitchBook. That was a significant increase from $684m invested by VC firms in 2020, and $188m in 2019.

Prior to the Rigetti transaction, the biggest deal was a $450mn investment in PsiQuantum, which was valued at $3.15bn, in a round led by venture capital firm BlackRock on July 27.

Quantum computers process information differently way than classical computing. Quantum computers encode information in qubits, and store exponentially more information in the form of 1s, 0s or a superposition of both. These computers can evaluate data simultaneously, while classical computers evaluate data sequentially, simply put.

Theoretically, that makes quantum computers significantly more powerful, and enables applications like drug discovery, which are limited by the constraints of classical computers.

Rigetti and PsiQuantum are startups in a growing field of quantum computer makers that includes heavyweights IBM and Google, which are building superconducting quantum systems based on transmon qubits. D-Wave offers a quantum-annealing system based on flux bits to solve limited-sized problems, but this week said it was building a new superconducting system to solve larger problems.

Quantum computers show promise but still immature, with questions around stability, said Linley Gwennap, president of Linley Group, in a research note last month.

"Solving the error-rate problem will require substantially new approaches. If researchers can meet that challenge, quantum processors will provide an excellent complement to classical processors," Gwennap wrote.

If quantum ever works, there could be a huge market, hence the VC interest, but the technology is years away from significant revenue, Gwennap told The Register.

Deals by SPAC (special purpose acquisition companies) like Supernova Partners tend to be highly speculative, but the venture firm's due diligence on Rigetti was more around the possible rewards if quantum computers live up to their hype.

Rigetti's quantum technology is scalable, practical and manufacturable, said Supernova's chief financial officer Michael Clifton, in a press conference this week related to the deal.

"Quantum is expected to be as important as mobile and cloud have been over the last two decades," Clifton said, adding, "we were focused on large addressable markets, differentiated technologies and excellent management teams."

Rigetti's quantum computer is modular and scalable with qubit systems linked through faster interconnects. The company's introductory system in 2018 had 8 qubits, and will scale it up to 80 qubit multichip system with high-density I/O and 3D signalling. The company's roadmap includes a 1000-qubit system in 2024 that is "error mitigating," and a 4000-qubit system in 2026 with full error correction features.

Rigetti designs and makes the quantum computers chips in its own fabrication plant, which helps accelerate the delivery of chips. Amazon offers access to Rigetti's quantum hardware through AWS.

IT leaders in non-tech companies are taking quantum computing seriously, IDC said in May.

A survey by the analyst house in April revealed companies would allocate more than 19 per cent the annual IT budgets to quantum computing in 2023, growing from 7 per cent in 2021. Investments would in at quantum algorithms and systems available through the cloud to boost AI and cybersecurity.

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Quantum computing startups pull in millions as VCs rush to get ahead of the game - The Register

JP Morgan Identified as Leading Champion in Wealth Management Market

LONDON, October 12, 2021--(BUSINESS WIRE)--Total fees generated from digital assets under management will reach $12.6 billion by 2025, a more than threefold increase on the 2020 figure of $4 billion, according to fintech and payments research specialists Kaleido Intelligence.

According to Kaleidos new report, Digital Wealth Management Strategies & Forecasts 2021, US digital wealth managers currently dominate the space (the five largest players all cater exclusively for US clients) and, with AUM (assets under management) exceeding $700 billion in 2020, accounts for nearly 85% of all digitally managed assets. This dominance is expected to continue for the foreseeable future: even by 2025 the US is expected to account for around 71% of robo-managed funds, with over $2.1 trillion under management

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JP Morgan Heads Digital Wealth Readiness Rankings

The report assessed the digital readiness of the 15 leading wealth management companies by AUM, scoring the players against indicators including the breadth of their digital portfolios, the scale of their fintech acquisitions and investment and the activities in key areas including artificial intelligence, blockchain and quantum computing.

Four companies were identified as Champion companies which have embraced the need for digital transformation, have committed to significant R&D in innovative technologies and have already deployed them at the backend or in customer facing products, and have reached out to third party partners across the ecosystem. JP Morgan headed the rankings, followed by Fidelity Investments, with Blackrock and Vanguard in joint third place.

Changing Audience, Changing Priorities

The report emphasised the need for wealth managers to understand, and respond to, the fact that not only will millennials seek to interact with them increasingly via digital channels, but that their expectations and demands from funds will differ significantly from their predecessors.

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According to report author Dr Windsor Holden, Principal Research Consultant at Kaleido Intelligence, "There is a far greater awareness of environmental issues and a related desire to invest in "green" or socially responsible funds, as well as in technology funds. Similarly, there has been a paradigm shift in goals, needs and lifestyles, which will need to be recognised by the wealth managers so that they can present appropriate investment options."

Investment Soaring into Wealth Management Fintechs

Kaleido estimates that over the past decade, approximately $4.6 billion has been invested by VC firms into robo-advisory fintech platforms, of which $1.8 billion has been invested since June 2020. One company, the Canadian startup Wealthsimple, accounts for 19% of all VC investment in that time, including $610 million earlier this year.

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About Kaleido Intelligence

Kaleido Intelligence is a specialist fintech and payments consulting and market research firm with a proven track record delivering fintech and payments research at the highest level. Research is led by expert analysts, each with significant experience delivering fintech research and insights that matter.

View source version on businesswire.com: https://www.businesswire.com/news/home/20211012005470/en/

Contacts

Contact our Press Team for interviews and research access:Jon King, Chief Commercial Officerfintech@kaleidointelligence.com +44 (0) 2039839843

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Digital Wealth Management Fees to Increase Threefold to $12.6 Billion By 2026 - Yahoo Finance

Intel's Loihi 2 is the company's second generation neuromorphic computing chip, a technology that's ... [+] designed to function like a digital representation of a biological brain complete with neurons and synapses.

If you follow the latest trends in the tech industry, you probably know that theres been a fair amount of debate about what the next big thing is going to be. Odds-on favorite for many has been augmented reality (AR) glasses, while others point to fully autonomous cars, and a few are clinging to the potential of 5G. With the surprise debut of Amazons Astro a few weeks back, personal robotic devices and digital companions have also thrown their hat into the ring.

However, while there has been little agreement on exactly what the next thing is, there seems to be little disagreement that whatever it turns out to be, it will be somehow powered, enabled, or enhanced by artificial intelligence (AI). Indeed, the fact that AI and machine learning (ML) are our future seems to be a foregone conclusion.

Yet, if we do an honest assessment of where some of these technologies actually stand on a functionality basis versus initial expectations, its fair to argue that the results have been disappointing on many levels. In fact, if we extend that thought process out to what AI/ML were supposed to do for us overall, then we start to come to a similarly disappointing conclusion.

To be clear, weve seen some incredible advancements in many areas that AI has powered. Advanced analytics, neural network training, and other related fields (where large chunks of data are used to find patterns, learn rules, and then apply them) have been huge benefactors of existing AI approaches.

At the same time, if we look at an application like autonomous driving, it seems increasingly clear that just pushing more and more data into algorithms that crank out ever refined, yet still flawed, ML models isnt really working. Were still years away from true Level 5 autonomy, and, given the number of accidents and even deaths that efforts like Teslas AutoPilot have led to, its probably time to consider another approach.

Similarly, though we are still at the dawn of the personal robotics age, its easy to imagine how the conceptual similarities between autonomous cars and robots will lead to conceptually similar problems in this new field. The problem, ultimately, is that there is simply no way to feed every potential scenario into an AI training model and create a predetermined answer on how to react for any given situation. Randomness and unexpected surprises are simply too strong an influence.

Whats needed is a type of computing that can really think and learn on its own and then adapt its learning to those unexpected scenarios. As crazy and potentially controversial as that may sound, thats essentially what researchers in the field of neuromorphic computing are attempting to do. The basic idea is to replicate the structure and function of the most adaptable computing/thinking device we know ofthe human brainin digital form. Following the principles of basic biology, neuromorphic chips attempt to re-create a series of connected neurons using digital synapses that send electrical pulses between them, much as biological brains do.

Its an area of academic research thats been around for a few decades now, but only recently has it started to make real progress and gain more attention. In fact, buried in the wave of tech industry announcements that have been made over the last few weeks was news that Intel had released the second generation of its neuromorphic chip, named Loihi 2, along with a new open-source software framework for it that theyve dubbed Lava.

To put realistic expectations around all of this, Loihi 2 is not going to be made commercially availableits termed a research chipand the latest version offers 1 million neurons, a far cry from the approximately 100 billion found in a human brain. Still, its an extremely impressive, ambitious project that offers 10x the performance, 15x the density of its 2018-era predecessor (its built on the companys new Intel 4 chip manufacturing process technology), and improved energy efficiency. In addition, it also provides better (and easier) means of interconnecting its unique architecture with other more traditional chips.

Intel clearly learned a great deal from the first Loihi, and one of the biggest realizations was that software development for this radically new architecture is extremely hard. As a result, another essential part of the companys news was the debut of Lava, an open-source software framework and set of tools that can be used to write applications for Loihi. The company is also offering tools that can simulate its operation on traditional CPUs and GPUs so that developers can create code without having access to the chips.

Whats particularly fascinating about how neuromorphic chips operate is that, despite the fact they function in a dramatically different fashion from both traditional CPU computing and parallel GPU-like computing models, they can be used to achieve some of the same goals. In other words, neuromorphic chips like Loihi 2 can provide the desired outcomes that traditional AI is shooting for, but in a significantly faster, more energy efficient, and less data intensive way. Through a series of event-based spikes that occur asynchronously and trigger digital neurons to respond in various waysmuch as a human brain operates (vs. the synchronous, structured processing in CPUs and GPUs)a neuromorphic chip can essentially learn things on the fly. As a result, its ideally suited for devices that must react to new stimuli in real-time.

These capabilities are why these chips are so appealing to those designing and building robots and robotic-like systems, which autonomous driving cars essentially are. Bottom line is that it could take commercially available neuromorphic chips to power the kind of autonomous cars and personal robots of our science fiction-inspired dreams.

Of course, neuromorphic computing isnt the only new approach to advancing the world of technology. Theres also a great deal of work being done in the more widely discussed world of quantum computing. Like quantum computing, the inner workings of neuromorphic computing are extraordinarily complex and, for now, primarily seen as research projects for corporate R&D labs and academic research. Unlike quantum, however, neuromorphic computing doesnt require the extreme physical challenges (temperatures near absolute zero) and power requirements that quantum currently does. In fact, one of the many appealing aspects of neuromorphic architectures is that theyre designed to be extremely low power, making them suitable for a variety of mobile or other battery-powered applications (like autonomous cars and robots).

Despite recent advancements, its important to remember that commercial application of neuromorphic chips is still several years away. However, its hard not to get excited and intrigued by a technology that has the potential to make AI-powered devices truly intelligent, instead of simply very well-trained. The distinction may seem subtle, but ultimately, its that kind of new smarts that well likely need in order to make some of the next big things really happen in a way that we can all appreciate and imagine.

Disclosure: TECHnalysis Research is a tech industry market research and consulting firm and, like all companies in that field, works with many technology vendors as clients, some of whom may be listed in this article.

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Is Neuromorphic Computing The Answer For Autonomous Driving And Personal Robotics? - Forbes

Scientists performed transmission electron microscopy and x-ray photoelectron spectroscopy (XPS) at Brookhaven Labs Center for Functional Nanomaterials and National Synchrotron Light Source II to characterize the properties of niobium thin films made into superconducting qubit devices at Princeton University. A transmission electron microscope image of one of these films is shown in the background; overlaid on this image are XPS spectra (colored lines representing the relative concentrations of niobium metal and various niobium oxides as a function of film depth) and an illustration of a qubit device. Through these and other microscopy and spectroscopy studies, the team identified atomic-scale structural and surface chemistry defects that may be causing loss of quantum informationa hurdle to enabling practical quantum computers. Credit: Brookhaven National Laboratory

Brookhaven Lab and Princeton scientists team up to identify sources of loss of quantum information at the atomic scale.

Engineers and materials scientists studying superconducting quantum information bits (qubits)a leading quantum computing material platform based on the frictionless flow of paired electronshave collected clues hinting at the microscopic sources of qubit information loss. This loss is one of the major obstacles in realizing quantum computers capable of stringing together millions of qubits to run demanding computations. Such large-scale, fault-tolerant systems could simulate complicated molecules for drug development, accelerate the discovery of new materials for clean energy, and perform other tasks that would be impossible or take an impractical amount of time (millions of years) for todays most powerful supercomputers.

An understanding of the nature of atomic-scale defects that contribute to qubit information loss is still largely lacking. The team helped bridge this gap between material properties and qubit performance by using state-of-the-art characterization capabilities at the Center for Functional Nanomaterials (CFN) and National Synchrotron Light Source II (NSLS-II), both U.S. Department of Energy (DOE) Office of Science User Facilities at Brookhaven National Laboratory. Their results pinpointed structural and surface chemistry defects in superconducting niobium qubits that may be causing loss.

Anjali Premkumar

Superconducting qubits are a promising quantum computing platform because we can engineer their properties and make them using the same tools used to make regular computers, said Anjali Premkumar, a fourth-year graduate student in the Houck Lab at Princeton University and first author on the Communications Materials paper describing the research. However, they have shorter coherence times than other platforms.

In other words, they cant hold onto information very long before they lose it. Though coherence times have recently improved from microseconds to milliseconds for single qubits, these times significantly decrease when multiple qubits are strung together.

Qubit coherence is limited by the quality of the superconductors and the oxides that will inevitably grow on them as the metal comes into contact with oxygen in the air, continued Premkumar. But, as qubit engineers, we havent characterized our materials in great depth. Here, for the first time, we collaborated with materials experts who can carefully look at the structure and chemistry of our materials with sophisticated tools.

This collaboration was a prequel to the Co-design Center for Quantum Advantage (C2QA), one of five National Quantum Information Science Centers established in 2020 in support of the National Quantum Initiative. Led by Brookhaven Lab, C2QA brings together hardware and software engineers, physicists, materials scientists, theorists, and other experts across national labs, universities, and industry to resolve performance issues with quantum hardware and software. Through materials, devices, and software co-design efforts, the C2QA team seeks to understand and ultimately control material properties to extend coherence times, design devices to generate more robust qubits, optimize algorithms to target specific scientific applications, and develop error-correction solutions.

Andrew Houck

In this study, the team fabricated thin films of niobium metal through three different sputtering techniques. In sputtering, energetic particles are fired at a target containing the desired material; atoms are ejected from the target material and land on a nearby substrate. Members of the Houck Lab performed standard (direct current) sputtering, while Angstrom Engineering applied a new form of sputtering they specialize in (high-power impulse magnetron sputtering, or HiPIMS), where the target is struck with short bursts of high-voltage energy. Angstrom carried out two variations of HiPIMS: normal and with an optimized power and target-substrate geometry.

Back at Princeton, Premkumar made transmon qubit devices from the three sputtered films and placed them in a dilution refrigerator. Inside this refrigerator, temperatures can plunge to near absolute zero (minus 459.67 degrees Fahrenheit), turning qubits superconducting. In these devices, superconducting pairs of electrons tunnel across an insulating barrier of aluminum oxide (Josephson junction) sandwiched between superconducting aluminum layers, which are coupled to capacitor pads of niobium on sapphire. The qubit state changes as the electron pairs go from one side of the barrier to the other. Transmon qubits, co-invented by Houck Lab principal investigator and C2QA Director Andrew Houck, are a leading kind of superconducting qubit because they are highly insensitive to fluctuations in electric and magnetic fields in the surrounding environment; such fluctuations can cause qubit information loss.

For each of the three device types, Premkumar measured the energy relaxation time, a quantity related to the robustness of the qubit state.

The energy relaxation time corresponds to how long the qubit stays in the first excited state and encodes information before it decays to the ground state and loses its information, explained Ignace Jarrige, formerly a physicist at NSLS-II and now a quantum research scientist at Amazon, who led the Brookhaven team for this study.

Ignace Jarrige

Each device had different relaxation times. To understand these differences, the team performed microscopy and spectroscopy at the CFN and NSLS-II.

NSLS-II beamline scientists determined the oxidation states of niobium through x-ray photoemission spectroscopy with soft x-rays at the In situ and Operando Soft X-ray Spectroscopy (IOS) beamline and hard x-rays at the Spectroscopy Soft and Tender (SST-2) beamline. Through these spectroscopy studies, they identified various suboxides located between the metal and the surface oxide layer and containing a smaller amount of oxygen relative to niobium.

We needed the high energy resolution at NSLS-II to distinguish the five different oxidation states of niobium and both hard and soft x-rays, which have different energy levels, to profile these states as a function of depth, explained Jarrige. Photoelectrons generated by soft x-rays only escape from the first few nanometers of the surface, while those generated by hard x-rays can escape from deeper in the films.

At the NSLS-II Soft Inelastic X-ray Scattering (SIX) beamline, the team identified spots with missing oxygen atoms through resonant inelastic x-ray scattering (RIXS). Such oxygen vacancies are defects, which can absorb energy from qubits.

At the CFN, the team visualized film morphology using transmission electron microscopy and atomic force microscopy, and characterized the local chemical makeup near the film surface through electron energy-loss spectroscopy.

Sooyeon Hwang

The microscope images showed grainspieces of individual crystals with atoms arranged in the same orientationsized larger or smaller depending on the sputtering technique, explained coauthor Sooyeon Hwang, a staff scientist in the CFN Electron Microscopy Group. The smaller the grains, the more grain boundaries, or interfaces where different crystal orientations meet. According to the electron energy-loss spectra, one film had not just oxides on the surface but also in the film itself, with oxygen diffused into the grain boundaries.

Their experimental findings at the CFN and NSLS-II revealed correlations between qubit relaxation times and the number and width of grain boundaries and concentration of suboxides near the surface.

Grain boundaries are defects that can dissipate energy, so having too many of them can affect electron transport and thus the ability of qubits to perform computations, said Premkumar. Oxide quality is another potentially important parameter. Suboxides are bad because electrons are not happily paired together.

Going forward, the team will continue their partnership to understand qubit coherence through C2QA. One research direction is to explore whether relaxation times can be improved by optimizing fabrication processes to generate films with larger grain sizes (i.e., minimal grain boundaries) and a single oxidation state. They will also explore other superconductors, including tantalum, whose surface oxides are known to be more chemically uniform.

From this study, we now have a blueprint for how scientists who make qubits and scientists who characterize them can collaborate to understand the microscopic mechanisms limiting qubit performance, said Premkumar. We hope other groups will leverage our collaborative approach to drive the field of superconducting qubits forward.

Reference: Microscopic relaxation channels in materials for superconducting qubits by Anjali Premkumar, Conan Weiland, Sooyeon Hwang, Berthold Jck, Alexander P. M. Place, Iradwikanari Waluyo, Adrian Hunt, Valentina Bisogni, Jonathan Pelliciari, Andi Barbour, Mike S. Miller, Paola Russo, Fernando Camino, Kim Kisslinger, Xiao Tong, Mark S. Hybertsen, Andrew A. Houck and Ignace Jarrige, 1 July 2021, Communications Materials.DOI: 10.1038/s43246-021-00174-7

This work was supported by the DOE Office of Science, National Science Foundation Graduate Research Fellowship, Humboldt Foundation, National Defense Science and Engineering Graduate Fellowship, Materials Research Science and Engineering Center, and Army Research Office. This research used resources of the Electron Microscopy, Proximal Probes, and Theory and Computation Facilities at the CFN, a DOE Nanoscale Science Research Center. The SST-2 beamline at NSLS-II is operated by the National Institute of Standards and Technology.

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Connecting the Dots Between Material Properties and Superconducting Qubit Performance - SciTechDaily

Quantum computing is an area of study focused on the development of computer based technologies centered around the principles ofquantum theory. Quantum theory explains the nature and behavior of energy and matter on thequantum(atomic and subatomic) level. Quantum computing uses a combination ofbitsto perform specific computational tasks. All at a much higher efficiency than their classical counterparts. Development ofquantum computersmark a leap forward in computing capability, with massive performance gains for specific use cases. For example quantum computing excels at like simulations.

The quantum computer gains much of its processing power through the ability for bits to be in multiple states at one time. They can perform tasks using a combination of 1s, 0s and both a 1 and 0 simultaneously. Current research centers in quantum computing include MIT, IBM, Oxford University, and the Los Alamos National Laboratory. In addition, developers have begun gaining access toquantum computers through cloud services.

Quantum computing began with finding its essential elements. In 1981, Paul Benioff at Argonne National Labs came up with the idea of a computer that operated with quantum mechanical principles. It is generally accepted that David Deutsch of Oxford University provided the critical idea behind quantum computing research. In 1984, he began to wonder about the possibility of designing a computer that was based exclusively on quantum rules, publishing a breakthrough paper a few months later.

Quantum Theory

Quantum theory's development began in 1900 with a presentation by Max Planck. The presentation was to the German Physical Society, in which Planck introduced the idea that energy and matter exists in individual units. Further developments by a number of scientists over the following thirty years led to the modern understanding of quantum theory.

Quantum Theory

Quantum theory's development began in 1900 with a presentation by Max Planck. The presentation was to the German Physical Society, in which Planck introduced the idea that energy and matter exists in individual units. Further developments by a number of scientists over the following thirty years led to the modern understanding of quantum theory.

The Essential Elements of Quantum Theory:

Further Developments of Quantum Theory

Niels Bohr proposed the Copenhagen interpretation of quantum theory. This theory asserts that a particle is whatever it is measured to be, but that it cannot be assumed to have specific properties, or even to exist, until it is measured. This relates to a principle called superposition. Superposition claims when we do not know what the state of a given object is, it is actually in all possible states simultaneously -- as long as we don't look to check.

To illustrate this theory, we can use the famous analogy of Schrodinger's Cat. First, we have a living cat and place it in a lead box. At this stage, there is no question that the cat is alive. Then throw in a vial of cyanide and seal the box. We do not know if the cat is alive or if it has broken the cyanide capsule and died. Since we do not know, the cat is both alive and dead, according to quantum law -- in a superposition of states. It is only when we break open the box and see what condition the cat is in that the superposition is lost, and the cat must be either alive or dead.

The principle that, in some way, one particle can exist in numerous states opens up profound implications for computing.

A Comparison of Classical and Quantum Computing

Classical computing relies on principles expressed by Boolean algebra; usually Operating with a 3 or 7-modelogic gateprinciple. Data must be processed in an exclusive binary state at any point in time; either 0 (off / false) or 1 (on / true). These values are binary digits, or bits. The millions of transistors and capacitors at the heart of computers can only be in one state at any point. In addition, there is still a limit as to how quickly these devices can be made to switch states. As we progress to smaller and faster circuits, we begin to reach the physical limits of materials and the threshold for classical laws of physics to apply.

The quantum computer operates with a two-mode logic gate:XORand a mode called QO1 (the ability to change 0 into a superposition of 0 and 1). In a quantum computer, a number of elemental particles such as electrons or photons can be used. Each particle is given a charge, or polarization, acting as a representation of 0 and/or 1. Each particle is called a quantum bit, or qubit. The nature and behavior of these particles form the basis of quantum computing and quantum supremacy. The two most relevant aspects of quantum physics are the principles of superposition andentanglement.

Superposition

Think of a qubit as an electron in a magnetic field. The electron's spin may be either in alignment with the field, which is known as aspin-upstate, or opposite to the field, which is known as aspin-downstate. Changing the electron's spin from one state to another is achieved by using a pulse of energy, such as from alaser. If only half a unit of laser energy is used, and the particle is isolated the particle from all external influences, the particle then enters a superposition of states. Behaving as if it were in both states simultaneously.

Each qubit utilized could take a superposition of both 0 and 1. Meaning, the number of computations a quantum computer could take is 2^n, where n is the number of qubits used. A quantum computer comprised of 500 qubits would have a potential to do 2^500 calculations in a single step. For reference, 2^500 is infinitely more atoms than there are in the known universe. These particles all interact with each other via quantum entanglement.

In comparison to classical, quantum computing counts as trueparallel processing. Classical computers today still only truly do one thing at a time. In classical computing, there are just two or more processors to constitute parallel processing.EntanglementParticles (like qubits) that have interacted at some point retain a type can be entangled with each other in pairs, in a process known ascorrelation. Knowing the spin state of one entangled particle - up or down -- gives away the spin of the other in the opposite direction. In addition, due to the superposition, the measured particle has no single spin direction before being measured. The spin state of the particle being measured is determined at the time of measurement and communicated to the correlated particle, which simultaneously assumes the opposite spin direction. The reason behind why is not yet explained.

Quantum entanglement allows qubits that are separated by large distances to interact with each other instantaneously (not limited to the speed of light). No matter how great the distance between the correlated particles, they will remain entangled as long as they are isolated.

Taken together, quantum superposition and entanglement create an enormously enhanced computing power. Where a 2-bit register in an ordinary computer can store only one of four binary configurations (00, 01, 10, or 11) at any given time, a 2-qubit register in a quantum computer can store all four numbers simultaneously. This is because each qubit represents two values. If more qubits are added, the increased capacity is expanded exponentially.

Quantum Programming

Quantum computing offers an ability to write programs in a completely new way. For example, a quantum computer could incorporate a programming sequence that would be along the lines of "take all the superpositions of all the prior computations." This would permit extremely fast ways of solving certain mathematical problems, such as factorization of large numbers.

The first quantum computing program appeared in 1994 by Peter Shor, who developed a quantum algorithm that could efficiently factorize large numbers.

The Problems - And Some Solutions

The benefits of quantum computing are promising, but there are huge obstacles to overcome still. Some problems with quantum computing are:

There are many problems to overcome, such as how to handle security and quantum cryptography. Long time quantum information storage has been a problem in the past too. However, breakthroughs in the last 15 years and in the recent past have made some form of quantum computing practical. There is still much debate as to whether this is less than a decade away or a hundred years into the future. However, the potential that this technology offers is attracting tremendous interest from both the government and the private sector. Military applications include the ability to break encryptions keys via brute force searches, while civilian applications range from DNA modeling to complex material science analysis.

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What is quantum computing?