Daily Archives: July 23, 2022

A recent analysis by Boston Consulting Group (BCG) has forecast that by 2030, there could be as many as one billion Bitcoin users worldwide. The crypto business is still in the early stages of the adoption curve though, the joint report by BCG, Bitget, and Foresight Ventures, said.

According to the report, a mere 0.3 per cent of personal wealth is invested in cryptocurrencies, as opposed to 25 per cent in stocks. It did, however, mention that the comparatively low penetration simply shows that there is an opportunity for expansion. This is in line with the findings of a study released by the US-based bank, Wells Fargo.

The banks Global Investment Strategy Team had stated in its research titled Understanding Cryptocurrency that the sector was in its hyper-adoption period, and compared the current stage of cryptocurrency to that of the Internet in the mid-to-late 1990s.

The BCG analysis extended the Wells Fargo papers comparison of cryptocurrency to the earlier Internet stages as well as the impending digital revolution, known as Web 3.0. There is still a lot of room for development, the report said.

If we use the number of cryptocurrency holders as a proxy for Web 3 users, and compare it against the adoption rate of Internet users in the 1990s, the overall number of crypto users is projected to exceed 1 billion by 2030, the report said, however, adding that it is challenging to estimate whether the trend of cryptocurrency acceptance will continue.

Crypto Native Funds are growing rapidly.

The BCG analysis said that individual investors are still the main holders of Bitcoin, while hedge funds and venture capitalists are among the institutional crypto investors, adding that these participants nearly doubled their exposure to $70 billion from Q4 2020 to the end of 2021.

The research further indicated that allocations will continue to climb. In addition, the paper mentioned an emerging class of crypto native funds that are gathering financial pace, such as Paradigm and Hashed.

These VCs, however, were the ones that took the brunt of the Terra-Luna debacle, it added.

The BCG report further said that South Korean early-stage VC Hashed has earned a position among the worlds most financially-troubled VCs. According to CoinMarketCap statistics from April, the black swan incident cost the Hashed wallet more than $3.5 billion.

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There Will Be 1 Billion Cryptocurrency Users Worldwide By 2030, Says BCG Report - Outlook India

The two of us, along with many other researchers involved in quantum computing, are trying to move definitively beyond these preliminary demos of QEC so that it can be employed to build useful, large-scale quantum computers. But before describing how we think such error correction can be made practical, we need to first review what makes a quantum computer tick.

Information is physical. This was the mantra of the distinguished IBM researcher Rolf Landauer. Abstract though it may seem, information always involves a physical representation, and the physics matters.

Conventional digital information consists of bits, zeros and ones, which can be represented by classical states of matter, that is, states well described by classical physics. Quantum information, by contrast, involves qubitsquantum bitswhose properties follow the peculiar rules of quantum mechanics.

A classical bit has only two possible values: 0 or 1. A qubit, however, can occupy a superposition of these two information states, taking on characteristics of both. Polarized light provides intuitive examples of superpositions. You could use horizontally polarized light to represent 0 and vertically polarized light to represent 1, but light can also be polarized on an angle and then has both horizontal and vertical components at once. Indeed, one way to represent a qubit is by the polarization of a single photon of light.

These ideas generalize to groups of n bits or qubits: n bits can represent any one of 2n possible values at any moment, while n qubits can include components corresponding to all 2n classical states simultaneously in superposition. These superpositions provide a vast range of possible states for a quantum computer to work with, albeit with limitations on how they can be manipulated and accessed. Superposition of information is a central resource used in quantum processing and, along with other quantum rules, enables powerful new ways to compute.

Researchers are experimenting with many different physical systems to hold and process quantum information, including light, trapped atoms and ions, and solid-state devices based on semiconductors or superconductors. For the purpose of realizing qubits, all these systems follow the same underlying mathematical rules of quantum physics, and all of them are highly sensitive to environmental fluctuations that introduce errors. By contrast, the transistors that handle classical information in modern digital electronics can reliably perform a billion operations per second for decades with a vanishingly small chance of a hardware fault.

Of particular concern is the fact that qubit states can roam over a continuous range of superpositions. Polarized light again provides a good analogy: The angle of linear polarization can take any value from 0 to 180 degrees.

Pictorially, a qubits state can be thought of as an arrow pointing to a location on the surface of a sphere. Known as a Bloch sphere, its north and south poles represent the binary states 0 and 1, respectively, and all other locations on its surface represent possible quantum superpositions of those two states. Noise causes the Bloch arrow to drift around the sphere over time. A conventional computer represents 0 and 1 with physical quantities, such as capacitor voltages, that can be locked near the correct values to suppress this kind of continuous wandering and unwanted bit flips. There is no comparable way to lock the qubits arrow to its correct location on the Bloch sphere.

Early in the 1990s, Landauer and others argued that this difficulty presented a fundamental obstacle to building useful quantum computers. The issue is known as scalability: Although a simple quantum processor performing a few operations on a handful of qubits might be possible, could you scale up the technology to systems that could run lengthy computations on large arrays of qubits? A type of classical computation called analog computing also uses continuous quantities and is suitable for some tasks, but the problem of continuous errors prevents the complexity of such systems from being scaled up. Continuous errors with qubits seemed to doom quantum computers to the same fate.

We now know better. Theoreticians have successfully adapted the theory of error correction for classical digital data to quantum settings. QEC makes scalable quantum processing possible in a way that is impossible for analog computers. To get a sense of how it works, its worthwhile to review how error correction is performed in classical settings.

Simple schemes can deal with errors in classical information. For instance, in the 19th century, ships routinely carried clocks for determining the ships longitude during voyages. A good clock that could keep track of the time in Greenwich, in combination with the suns position in the sky, provided the necessary data. A mistimed clock could lead to dangerous navigational errors, though, so ships often carried at least three of them. Two clocks reading different times could detect when one was at fault, but three were needed to identify which timepiece was faulty and correct it through a majority vote.

The use of multiple clocks is an example of a repetition code: Information is redundantly encoded in multiple physical devices such that a disturbance in one can be identified and corrected.

As you might expect, quantum mechanics adds some major complications when dealing with errors. Two problems in particular might seem to dash any hopes of using a quantum repetition code. The first problem is that measurements fundamentally disturb quantum systems. So if you encoded information on three qubits, for instance, observing them directly to check for errors would ruin them. Like Schrdingers cat when its box is opened, their quantum states would be irrevocably changed, spoiling the very quantum features your computer was intended to exploit.

The second issue is a fundamental result in quantum mechanics called the no-cloning theorem, which tells us it is impossible to make a perfect copy of an unknown quantum state. If you know the exact superposition state of your qubit, there is no problem producing any number of other qubits in the same state. But once a computation is running and you no longer know what state a qubit has evolved to, you cannot manufacture faithful copies of that qubit except by duplicating the entire process up to that point.

Fortunately, you can sidestep both of these obstacles. Well first describe how to evade the measurement problem using the example of a classical three-bit repetition code. You dont actually need to know the state of every individual code bit to identify which one, if any, has flipped. Instead, you ask two questions: Are bits 1 and 2 the same? and Are bits 2 and 3 the same? These are called parity-check questions because two identical bits are said to have even parity, and two unequal bits have odd parity.

The two answers to those questions identify which single bit has flipped, and you can then counterflip that bit to correct the error. You can do all this without ever determining what value each code bit holds. A similar strategy works to correct errors in a quantum system.

Learning the values of the parity checks still requires quantum measurement, but importantly, it does not reveal the underlying quantum information. Additional qubits can be used as disposable resources to obtain the parity values without revealing (and thus without disturbing) the encoded information itself.

Like Schrdingers cat when its box is opened, the quantum states of the qubits you measured would be irrevocably changed, spoiling the very quantum features your computer was intended to exploit.

What about no-cloning? It turns out it is possible to take a qubit whose state is unknown and encode that hidden state in a superposition across multiple qubits in a way that does not clone the original information. This process allows you to record what amounts to a single logical qubit of information across three physical qubits, and you can perform parity checks and corrective steps to protect the logical qubit against noise.

Quantum errors consist of more than just bit-flip errors, though, making this simple three-qubit repetition code unsuitable for protecting against all possible quantum errors. True QEC requires something more. That came in the mid-1990s when Peter Shor (then at AT&T Bell Laboratories, in Murray Hill, N.J.) described an elegant scheme to encode one logical qubit into nine physical qubits by embedding a repetition code inside another code. Shors scheme protects against an arbitrary quantum error on any one of the physical qubits.

Since then, the QEC community has developed many improved encoding schemes, which use fewer physical qubits per logical qubitthe most compact use fiveor enjoy other performance enhancements. Today, the workhorse of large-scale proposals for error correction in quantum computers is called the surface code, developed in the late 1990s by borrowing exotic mathematics from topology and high-energy physics.

It is convenient to think of a quantum computer as being made up of logical qubits and logical gates that sit atop an underlying foundation of physical devices. These physical devices are subject to noise, which creates physical errors that accumulate over time. Periodically, generalized parity measurements (called syndrome measurements) identify the physical errors, and corrections remove them before they cause damage at the logical level.

A quantum computation with QEC then consists of cycles of gates acting on qubits, syndrome measurements, error inference, and corrections. In terms more familiar to engineers, QEC is a form of feedback stabilization that uses indirect measurements to gain just the information needed to correct errors.

QEC is not foolproof, of course. The three-bit repetition code, for example, fails if more than one bit has been flipped. Whats more, the resources and mechanisms that create the encoded quantum states and perform the syndrome measurements are themselves prone to errors. How, then, can a quantum computer perform QEC when all these processes are themselves faulty?

Remarkably, the error-correction cycle can be designed to tolerate errors and faults that occur at every stage, whether in the physical qubits, the physical gates, or even in the very measurements used to infer the existence of errors! Called a fault-tolerant architecture, such a design permits, in principle, error-robust quantum processing even when all the component parts are unreliable.

A long quantum computation will require many cycles of quantum error correction (QEC). Each cycle would consist of gates acting on encoded qubits (performing the computation), followed by syndrome measurements from which errors can be inferred, and corrections. The effectiveness of this QEC feedback loop can be greatly enhanced by including quantum-control techniques (represented by the thick blue outline) to stabilize and optimize each of these processes.

Even in a fault-tolerant architecture, the additional complexity introduces new avenues for failure. The effect of errors is therefore reduced at the logical level only if the underlying physical error rate is not too high. The maximum physical error rate that a specific fault-tolerant architecture can reliably handle is known as its break-even error threshold. If error rates are lower than this threshold, the QEC process tends to suppress errors over the entire cycle. But if error rates exceed the threshold, the added machinery just makes things worse overall.

The theory of fault-tolerant QEC is foundational to every effort to build useful quantum computers because it paves the way to building systems of any size. If QEC is implemented effectively on hardware exceeding certain performance requirements, the effect of errors can be reduced to arbitrarily low levels, enabling the execution of arbitrarily long computations.

At this point, you may be wondering how QEC has evaded the problem of continuous errors, which is fatal for scaling up analog computers. The answer lies in the nature of quantum measurements.

In a typical quantum measurement of a superposition, only a few discrete outcomes are possible, and the physical state changes to match the result that the measurement finds. With the parity-check measurements, this change helps.

Imagine you have a code block of three physical qubits, and one of these qubit states has wandered a little from its ideal state. If you perform a parity measurement, just two results are possible: Most often, the measurement will report the parity state that corresponds to no error, and after the measurement, all three qubits will be in the correct state, whatever it is. Occasionally the measurement will instead indicate the odd parity state, which means an errant qubit is now fully flipped. If so, you can flip that qubit back to restore the desired encoded logical state.

In other words, performing QEC transforms small, continuous errors into infrequent but discrete errors, similar to the errors that arise in digital computers.

Researchers have now demonstrated many of the principles of QEC in the laboratoryfrom the basics of the repetition code through to complex encodings, logical operations on code words, and repeated cycles of measurement and correction. Current estimates of the break-even threshold for quantum hardware place it at about 1 error in 1,000 operations. This level of performance hasnt yet been achieved across all the constituent parts of a QEC scheme, but researchers are getting ever closer, achieving multiqubit logic with rates of fewer than about 5 errors per 1,000 operations. Even so, passing that critical milestone will be the beginning of the story, not the end.

On a system with a physical error rate just below the threshold, QEC would require enormous redundancy to push the logical rate down very far. It becomes much less challenging with a physical rate further below the threshold. So just crossing the error threshold is not sufficientwe need to beat it by a wide margin. How can that be done?

If we take a step back, we can see that the challenge of dealing with errors in quantum computers is one of stabilizing a dynamic system against external disturbances. Although the mathematical rules differ for the quantum system, this is a familiar problem in the discipline of control engineering. And just as control theory can help engineers build robots capable of righting themselves when they stumble, quantum-control engineering can suggest the best ways to implement abstract QEC codes on real physical hardware. Quantum control can minimize the effects of noise and make QEC practical.

In essence, quantum control involves optimizing how you implement all the physical processes used in QECfrom individual logic operations to the way measurements are performed. For example, in a system based on superconducting qubits, a qubit is flipped by irradiating it with a microwave pulse. One approach uses a simple type of pulse to move the qubits state from one pole of the Bloch sphere, along the Greenwich meridian, to precisely the other pole. Errors arise if the pulse is distorted by noise. It turns out that a more complicated pulse, one that takes the qubit on a well-chosen meandering route from pole to pole, can result in less error in the qubits final state under the same noise conditions, even when the new pulse is imperfectly implemented.

One facet of quantum-control engineering involves careful analysis and design of the best pulses for such tasks in a particular imperfect instance of a given system. It is a form of open-loop (measurement-free) control, which complements the closed-loop feedback control used in QEC.

This kind of open-loop control can also change the statistics of the physical-layer errors to better comport with the assumptions of QEC. For example, QEC performance is limited by the worst-case error within a logical block, and individual devices can vary a lot. Reducing that variability is very beneficial. In an experiment our team performed using IBMs publicly accessible machines, we showed that careful pulse optimization reduced the difference between the best-case and worst-case error in a small group of qubits by more than a factor of 10.

Some error processes arise only while carrying out complex algorithms. For instance, crosstalk errors occur on qubits only when their neighbors are being manipulated. Our team has shown that embedding quantum-control techniques into an algorithm can improve its overall success by orders of magnitude. This technique makes QEC protocols much more likely to correctly identify an error in a physical qubit.

For 25 years, QEC researchers have largely focused on mathematical strategies for encoding qubits and efficiently detecting errors in the encoded sets. Only recently have investigators begun to address the thorny question of how best to implement the full QEC feedback loop in real hardware. And while many areas of QEC technology are ripe for improvement, there is also growing awareness in the community that radical new approaches might be possible by marrying QEC and control theory. One way or another, this approach will turn quantum computing into a realityand you can carve that in stone.

This article appears in the July 2022 print issue as Quantum Error Correction at the Threshold.

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Central Banks Join the Cryptocurrency Gold Rush - IEEE Spectrum

Cryptocurrency Price Today: The global cryptocurrency market, despite its overall rally through the week, fell slightly on Saturday, July 18, even as it held its $1 trillion mark. This was due to an overall loss incurred by major cryptocurrencies including Bitcoin, Ethereum, Solana and Dogecoin. The crypto market cap was standing at $1.05 trillion on the day, which is a 0.83 per cent decrease over the past 24 hours.

Even though the week started on a bit bearish note, the momentum picked up heavily mid-week, taking most cryptocurrencies up, said Edul Patel, CEO and co founder of Mudrex.

Bitcoin price today fell1.06 per cent over the last 24 hours to stand at $22,896.23 as per data from CoinMarketCap while writing this article, as traders bore the brunt of Tesla selling 75 per cent of its holdings of the crypto coin. However, the sentiment over Bitcoin improved further and reached its highest since April this year, analysts said earlier this week.

Bitcoin has rallied from US$20,000 on Monday to above US$23,000 on Friday. Despite the crypto sliding a bit after Tesla announced it had sold 75 per cent of its BTC holdings, the crypto has tried to keep up with the selling pressure from bears. If bulls can make a move, one can expect BTC to trade at the US$24,000 mark soon, said Patel.

Ethereum dropped on the day too, not being able to hold the $1600 mark it had reached earlier in the week. Ether price today at the time of writing this article was $1,580.16, down by0.63 per cent in the last 24 hours, data from CoinMarketCap showed. However, Ether has gained over 30 per cent in the last seven days, according to the data.

While the second largest cryptocurrency, Ethereum, has outperformed the market by continuously rallying for the past seven days. ETH has gained nearly 34 per cent in the past seven days following the announcement date of the Merge. If the consistency of gains is maintained, we might see ETH regain the US$2,000 level in the coming week, noted Patel.

Here are the top 10 cryptocurrencies and their prices onJuly 23, 2022, Saturday, (According to data from coinmarketcap.com)

Bitcoin $22,896.23 or1.06 per centloss in the last 24 hours

Ethereum $1,580.16 or0.63 per centloss in the last 24 hours

Tether $1.00 or0.00 per centgain in the last 24 hours

USD Coin$1.00 or 0.01 per centgain inthe last 24 hours

BNB $266.64 or0.25 per centloss in the last 24 hours

Binance USD$1.00 or 0.13 per cent gainin the last 24 hours

XRP $0.361 or1.42 per centloss in the last 24 hours

Cardano$0.4933 or1.17 per centloss in the last 24 hours

Solana$41.51 or3.50 per centloss in the last 24 hours

Dogecoin$0.06891 or1.69 per centloss in thelast 24 hours

Read all the Latest News and Breaking News here

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Cryptocurrency Price Today: Bitcoin Near $23,000, Ether Flat at $1,580; Full List - News18

SAVANNAH, Ga. (WSAV) There could be an uprising of bitcoin maximalists as Bitget has partnered with MarketPeak, a crypto education platform.

The partnership is aimed at boosting cryptocurrency adoption and increasing financial independence for different groups of investors by offering them educational resources about blockchain and cryptocurrency.

MarketPeaks new education materials and services might be beneficial to those new and old to the cryptocurrency world who may want the convivence of a one-stop platform regarding crypto and blockchain education.

Bitget in return will offer professional exchange services and a trading platform to the MarketPeak community.It will also offer One-Click Copy Trade, a function that allows new traders to follow the trading strategies of veteran players.

Established in 2018, Bitget is one of the worlds leading cryptocurrency exchanges with a core focus on social trading. Currently serving over two million users in more than 50 countries around the world, Bitget accelerated its mission to promote decentralized finance with a 600-strong workforce representing over 38 nationalities.

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Bitget partners with MarketPeak to boost cryptocurrency adoption - WSAV-TV

Cryptocurrency investment funds lost almost $6 billion worth of Bitcoin following the massive liquidation series on the cryptocurrency market back in the May-June period. The biggest loser on the market is, as expected, the Luna Foundation Guard.

The largest portionof the coins that were lost is tied to the series of large liquidations that appeared on the market after the price of the first cryptocurrency tumbled from $30,000 to $17,000.

Luna Foundation Guard lost more than 80,000 BTC, which is worth more than $1.8 billion at press time. Second place on the chart goes to Teslasince the company sold $900 million worth of BTC back in May.

The notorious Three Arrows Capital was not the biggest loser on the market, despite being the most popular object of ridicule in the space since May.

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The chart is being closed by massive selling events conducted by public miners whogot rid of around 19,000 Bitcoins, causing enormous selling pressure on the market in June and fueling BTC's catastrophicrun to $17,000.

Following Tesla's earnings report, it become clear that the company's massive sell-off made a huge contribution to Bitcoin's rally to $17,000 as it caused another cascade of liquidations that hit Three Arrows Capital and pretty much liquidated a large portionof its positions, including Ethereum, whichfollowed the first cryptocurrency's path.

The beginning of the summer of 2022 could end up being one of the worst months for the entiremarket, whichalmost crashed to critical levels that would have affected the entireindustry's evolution in the long term.

Read this article:
Almost $6 Billion Lost by Cryptocurrency Investment Funds Like 3AC, Celsius and Others - U.Today

What happened

CryptocurrencyEthereum Classic (ETC 4.16%) is having a week to write home about. According to CoinMarketCap, Ethereum Classic is up 72% over the past seven days, and it's likely because of upcoming changes to Ethereum (more on that in a moment).

Interestingly,Bitcoin Gold (BTG 0.05%) is also up big over the past seven days: 47% as of this writing.But this Bitcoin alternative doesn't have a clear catalyst like Ethereum Classic.

This week's outsize moves from Ethereum Classic and Bitcoin Gold are interesting considering they're both hard forks -- of Ethereum and Bitcoin, respectively.

Cryptocurrencies are decentralized. Private, independent parties volunteer their computers for the blockchain network. However, if developers tweak the code, miners or validators could choose to keep using the old code and no one could stop them -- a fork. Moreover, developers could tweak things in different directions, leaving parties to decide which blockchain to use.

Ethereum is getting closer to completely changing from a proof-of-work blockchain to a proof-of-stake blockchain, which greatly alters the network's incentive structure. Miners won't be able to operate as they have in the past. And for this reason, there's speculation that miners will jump over to Ethereum Classic instead, which could explain why it has been gaining so much recently.

As for Bitcoin Gold, I'm not sure why it's suddenly gaining momentum. According to CoinMarketCap, trading volume is up over 1,200% in just the past 24 hours. It could be that traders are looking for forks of popular cryptocurrencies, considering Ethereum Classic is a fork and performing well. But that's just speculation on my part.

Whether investing in stocks or cryptocurrencies, I recommend always looking for fundamental reasons to be bullish. Therefore, I'd be leery of getting too excited about Bitcoin Gold today, considering there doesn't appear to be a fundamental driver to its performance.

But Ethereum Classic is a different story. If Ethereum miners truly do jump ship and switch blockchains, that would theoretically make Ethereum Classic more stable and secure, which could help boost its long-term adoption. But this driver will remain fairly speculative until Ethereum's actual merge, which likely won't occur before September.

Jon Quast has positions in Bitcoin and Ethereum. The Motley Fool has positions in and recommends Bitcoin and Ethereum. The Motley Fool has a disclosure policy.

Excerpt from:
Ethereum Classic Soars Over 70% This Week, and This Other Cryptocurrency Is Close Behind - The Motley Fool