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Kotaku reports: A newly unsealed FBI indictment accuses a former analyst at Goldman Sachs of insider trading, including allegedly using an Xbox to pass tips onto his close friends. The friend group earned over $400,000 in ill-gotten gains as a result, federal prosecutors claim. "There's no tracing [Xbox 360 chat]," the analyst allegedly told his friend who was worried they might be discovered.

He appears to have made a grave miscalculation.

The FBI arrested Anthony Viggiano and alleged co-conspirator Christopher Salamone, charging them with securities fraud on September 28. Viggiano is accused of using his previous position at Goldman Sachs to share trading tips with Salamone and others. Salamone has already pleaded guilty. Bloomberg reports that this is the fifth incident in recent years of a person associated with the investment bank allegedly using their position to do crimes...

Probably best to keep the crime talk on Xbox to a minimum either way, especially now that Microsoft is using AI to monitor communications for illicit and toxic activities. In a statement an FBI official said "This indictment is yet another example of individuals believing they can get away with benefiting from trading on material non-public information.

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FBI Indicts Goldman Sachs Analyst Who Tried Using Xbox Chat for ... - Slashdot

One of the most well-established and disruptive uses for a future quantum computer is the ability to crack encryption. A new algorithm could significantly lower the barrier to achieving this.

Despite all the hype around quantum computing, there are still significant question marks around what quantum computers will actually be useful for. There are hopes they could accelerate everything from optimization processes to machine learning, but how much easier and faster theyll be remains unclear in many cases.

One thing is pretty certain though: A sufficiently powerful quantum computer could render our leading cryptographic schemes worthless. While the mathematical puzzles underpinning them are virtually unsolvable by classical computers, they would be entirely tractable for a large enough quantum computer. Thats a problem because these schemes secure most of our information online.

The saving grace has been that todays quantum processors are a long way from the kind of scale required. But according to a report in Science, New York University computer scientist Oded Regev has discovered a new algorithm that could reduce the number of qubits required substantially.

The approach essentially reworks one of the most successful quantum algorithms to date. In 1994, Peter Shor at MIT devised a way to work out which prime numbers need to be multiplied together to give a particular numbera problem known as prime factoring.

For large numbers, this is an incredibly difficult problem that quickly becomes intractable on conventional computers, which is why it was used as the basis for the popular RSA encryption scheme. But by taking advantage of quantum phenomena like superposition and entanglement, Shors algorithm can solve these problems even for incredibly large numbers.

That fact has led to no small amount of panic among security experts, not least because hackers and spies can hoover up encrypted data today and then simply wait for the development of sufficiently powerful quantum computers to crack it. And although post-quantum encryption standards have been developed, implementing them across the web could take many years.

It is likely to be quite a long wait though. Most implementations of RSA rely on at least 2048-bit keys, which is equivalent to a number 617 digits long. Fujitsu researchers recently calculated that it would take a completely fault-tolerant quantum computer with 10,000 qubits 104 days to crack a number that large.

However, Regevs new algorithm, described in a pre-print published on arXiv, could potentially reduce those requirements substantially. Regev has essentially reworked Shors algorithm such that its possible to find a numbers prime factors using far fewer logical steps. Carrying out operations in a quantum computer involves creating small circuits from a few qubits, known as gates, that perform simple logical operations.

In Shors original algorithm, the number of gates required to factor a number is the square of the number of bits used to represent it, which is denoted as n2. Regevs approach would only require n1.5 gates because it searches for prime factors by carrying out smaller multiplications of many numbers rather than very large multiplications of a single number. It also reduces the number of gates required by using a classical algorithm to further process the outputs.

In the paper, Regev estimates that for a 2048-bit number this could reduce the number of gates required by two to three orders of magnitude. If true, that could enable much smaller quantum computers to crack RSA encryption.

However, there are practical limitations. For a start, Regev notes that Shors algorithm benefits from a host of optimizations developed over the years that reduce the number of qubits required to run it. Its unclear yet whether these optimizations would work on the new approach.

Martin Eker, a quantum computing researcher with the Swedish government, also told Science that Regevs algorithm appears to need quantum memory to store intermediate values. Providing that memory will require extra qubits and eat into any computational advantage it has.

Nonetheless, the new research is a timely reminder that, when it comes to quantum computings threat to encryption, the goal posts are constantly moving, and shifting to post-quantum schemes cant happen fast enough.

Image Credit: Google

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Quantum Computers Could Crack Encryption Sooner Than Expected With New Algorithm - Singularity Hub

Science (like us) isn't always sure of where the best possible future is, and computing is no exception. Whether in classic semiconductor systems or in the forward-looking reality of quantum computing, there are sometimes multiple paths forward (and here's our primer on quantum computing if you want a refresher). Transmon superconducting qubits (such as the ones used by IBM, Google, and Alice&Bob) have gained traction as one of the most promising qubit types. But new MIT research could open up a door towards another type of superconducting qubits that are more stable and could offer more complex computation circuits: fluxonium qubits.

Qubits are the quantum computing equivalent to transistors - get increasing numbers of them together, and you get increased computing performance (in theory). But while transistors are deterministic and can only represent a binary system (think of the result being either side of a coin, mapped to either 0 or 1), qubits are probabilistic and can represent the different positions of the coin while it's spinning in the air. This allows you to explore a bigger space of possible solutions than what can easily be represented through binary languages (which is why quantum computing can offer much faster processing of certain problems).

One current limitation to quantum computing is the accuracy of the computed results - if you're looking for, say, new healthcare drug designs, it'd be an understatement to say you need the results to be correct, replicable, and demonstrable. But qubits are sensitive and finicky to external stressors such as temperature, magnetism, vibrations, fundamental particle collisions, and other elements, which can introduce errors into the computation or collapse entangled states entirely. The reality of qubits being much more prone to external interference than transistors is one of the roadblocks on the road to quantum advantage; so a solution lies in being able to improve the accuracy of the computed results.

It's also not just a matter of applying error-correcting code to low-accuracy results and magically turning them into the correct results we want. IBM's recent breakthrough in this area (applying to transmon qubits) showed the effects of an error-correction code that predicted the environmental interference within a qubit system. Being able to predict interference means you can account for its effects within the skewed results and can compensate for them accordingly - arriving at the desired ground truth.

But in order for it to be possible to apply error-correction codes, the system has to already have passed a "fidelity threshold" - a minimum operating-level accuracy that enables those error-correcting codes to be just enough for us to be able to extract predictably useful, accurate results from our quantum computer.

Some qubit architectures - such as fluxonium qubits, the qubit architecture the research is based on - possess higher base stability against external interference. This enables them to stay coherent for longer periods of time - a measure of how long the qubit system can be effectively used between shut-downs and total information loss. Researchers are interested in fluxonium qubits because they've already unlocked coherence times of more than a millisecond - around ten times longer than can be achieved with transmon superconducting qubits.

The novel qubit architecture enables operations to be performed between fluxonium qubits with important accuracy levels. Within it, the research team enabled fluxonium-based two-qubit gates to run at 99.9% accuracy and single-qubit gates to run at a record 99.99% accuracy. The architecture and design were published under the title "High-Fidelity, Frequency-Flexible Two-Qubit Fluxonium Gates with a Transmon Coupler" in PHYSICAL REVIEW X.

You could think about fluxonium qubits as being an alternative qubit architecture with its own strengths and weaknesses; not as an evolution of the quantum computing that has come before. Transmon qubits are made of a single Josephson junction shunted by a large capacitor, while fluxonium qubits are made of a small Josephson junction in series with an array of larger junctions or a high kinetic inductance material. It's partly for this that fluxonium qubits are harder to scale: they require more sophisticated coupling schemes between qubits, sometimes even using transmon qubits for this purpose. The fluxonium architecture design described in the paper does just that in what's called a Fluxonium-Transmon-Fluxonium (FTF) architecture.

Transmon qubits such as the ones used by IBM and Google are relatively easier to manipulate into bigger qubits arrays (IBM's Osprey is already at 433 qubits) and have faster operation times, performing fast and simple gate operations mediated by microwave pulses. Fluxonium qubits do offer the possibility of performing slower yet more accurate gate operations through shaped pulses than a transmon-only approach would enable.

There's no promise of an easy road to quantum advantage through any qubit architecture; that's the reason why so many companies are pursuing their differing approaches. In this scenario, it may be useful to think about this Noisy-Intermediate Scale Quantum (NISQ) era being the age where multiple quantum architectures flourish. From topological superconductors (as per Microsoft) through diamond vacancies, transmon superconduction (IBM, Google, others), ion traps, and a myriad of other approaches, this is the age where we will settle into certain patterns within quantum computing. All architectures may flourish, but it's perhaps most likely that only some will - which also justifies why states and corporations aren't pursuing a single qubit architecture as their main focus.

The numerous, apparently viable approaches to quantum computing we're witnessing put us right in the middle of the branching path before x86 gained dominance as the premier architecture for binary computing. It remains to be seen whether the quantum computing future will readily (and peacefully) agree on a particular technology, and how will a heterogeneous quantum future look like.

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MIT's Superconducting Qubit Breakthrough Boosts Quantum Performance - Tom's Hardware

CAMBRIDGE, Mass. Researchers at MIT have achieved a significant breakthrough in quantum computing, bringing the potential of these incredible thinking machines closer to realization. Quantum computers promise to handle calculations far too complex for current supercomputers, but many hurdles remain. A primary challenge is addressing computational errors faster than they arise.

In a nutshell, quantum computersfind better and quicker ways to solve problems. Scientists believe quantum technology could solve extremely complex problems in seconds, while traditional supercomputers you see today could need months or even years to crack certain codes.

What makes these next generation supercomputers different from your everyday smartphone and laptop is how they process data. Quantum computers harness the properties of quantum physics to store data and perform their functions. While traditional computers use bits (either a 1 or a 0) to encode information on your devices, quantum technology uses qubits.

These qubits can be in a state of 1, 0, or both at once, enabling more complex computations. However, they are highly susceptible to errors.

To reduce these errors, the MIT team developed a new type of superconducting qubit named fluxonium, which has a longer lifespan than the traditional kind. The team crafted a unique architecture involving these fluxonium qubits that can perform operations (known as gates) more accurately. Their design enabled two-qubit gates that exceeded 99.9 percent accuracy and single-qubit gates with 99.99 percent accuracy.

Building a large-scale quantum computer starts with robust qubits and gates. We showed a highly promising two-qubit system and laid out its many advantages for scaling. Our next step is to increase the number of qubits, says study lead author Dr. Leon Ding PhD 23, who was a physics graduate student in the Engineering Quantum Systems (EQuS) group, in a university release.

To give a comparison, in classical computing, a gate would be an operation performed on bits. In quantum computing, a gate would be a logical operation on one or two qubits. Achieving higher accuracy in these operations is essential as errors in quantum systems can multiply quickly, leading to system failures.

For years, the primary focus in quantum research was on a type of qubit known as transmon. The newer fluxonium qubits boast a longer working lifespan, which means they can run algorithms for extended periods without losing data. This longer lifespan has led to the MIT teams development of high-accuracy gates.

Dr. Ding explained that their novel architecture connects two fluxonium qubits using a system that prevents unwanted background noise, which can introduce errors. This system has shown promise in keeping background interactions to a minimum.

The longer a qubit lives, the higher fidelity the operations it tends to promote, says Dr. Ding. These two numbers are tied together. But it has been unclear, even when fluxonium qubits themselves perform quite well, if you can perform good gates on them.

Drawing an analogy, senior researcher William Oliver, likened working with low-quality qubits to trying to perform a task with a room full of kindergartners.

Thats a lot of chaos, and adding more kindergartners wont make it better, notes Oliver. However, several mature graduate students working together leads to performance that exceeds any one of the individuals thats the threshold concept. While there is still much to do to build an extensible quantum computer, it starts with having high-quality quantum operations that are well above threshold.

Following these positive results, a group from MIT has founded Atlantic Quantum, a startup aiming to use fluxonium qubits to construct a practical quantum computer for commercial use.

These results are immediately applicable and could change the state of the entire field, says Dr. Bharath Kannan, CEO of Atlantic Quantum. This shows the community that there is an alternate path forward. We strongly believe that this architecture, or something like this using fluxonium qubits, shows great promise in terms of actually building a useful, fault-tolerant quantum computer.

Experts in the field, such as Chunqing Deng from Alibabas global research institution, have hailed the MIT teams work as a pivotal milestone.

This work pioneers a new architecture for coupling two fluxonium qubits. The achieved gate fidelities are not only the best on record for fluxonium, but also on par with those of transmons, the currently dominating qubit. More importantly, the architecture also offers a high degree of flexibility in parameter selection, a feature essential for scaling up to a multi-qubit fluxonium processor, says Deng.

For those of us who believe that fluxonium is a fundamentally better qubit than transmon, this work is an exciting and affirming milestone. It will galvanize not just the development of fluxonium processors but also more generally that for qubits alternative to transmons.

The study is published in the journal Physical Review X.

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Quantum computing enters the fluxonium era: Breakthrough sends ... - Study Finds

Editors note: Putting AI on the Fast Track to Sure-Fire Success was previously published in September 2023. It has since been updated to include the most relevant information available.

Artificial intelligence is not just a buzzword it is a reality that will transform every aspect of our daily lives in the coming years. It will revitalize industries from healthcare to education, from entertainment to cybersecurity, and offer new possibilities currently unheard of.

One possibility comes from an area hardly anyone is talking about right now a top-secret technology that will fuel the AI Stock Boom.

Before I reveal that technology, you must first understand what makes AI models run. At their core, AI models are like cars. They have an engine the computer on top of which the models are run. And they have fuel the volume of data the model is trained on.

Obviously, the better the engine in a car and the more fuel it has, the better and farther that car will drive.

Its the same with AI.

The better the engine of an AI model (computing power) and the more fuel it has (data), the better that model will perform.

The top-secret tech Im referring to is all about radically upgrading the computing power AI models have.

And Bank of Americas head of global thematic investing Haim Israel has said this technology could create a revolution for humanity bigger than fire, bigger than the wheel.

Thats because this tech will essentially drive everything in the emerging Age of AI.

What on Earth am I talking about?

Two words: quantum computing.

Ill start by saying that the underlying physics of this breakthrough quantum mechanics is highly complex. It would likely require over 500 pages to fully understand.

But, alas, heres my best job at making a Cliffs Notes version in 500 words instead.

For centuries, scientists have developed, tested, and validated the laws of the physical world, known as classical mechanics. These scientifically explain how and why things work, where they come from, so on and so forth.

But in 1897, J.J. Thomson discovered the electron. And he unveiled a new, subatomic world of super-small things that didnt obey the laws of classical mechanics at all. Instead, they obeyed their own set of rules, which have since become known as quantum mechanics.

The rules of quantum mechanics differ from that of classical mechanics in two very weird, almost-magical ways.

First, in classical mechanics, objects are in one place at one time. You are either at the store or at home, not both.

But in quantum mechanics, subatomic particles can theoretically exist in multiple places at once before theyre observed. A single subatomic particle can exist in point A and point B at the same time until we observe it. And at that point, it only exists at either point A or point B.

So, the true location of a subatomic particle is some combination of all its possible positions.

This is called quantum superposition.

Second, in classical mechanics, objects can only work with things that are also real. You cant use an imaginary friend to help move the couch. You need a real friend instead.

But in quantum mechanics, all of those probabilistic states of subatomic particles are not independent. Theyre entangled. That is, if we know something about the probabilistic positioning of one subatomic particle, then we know something about the probabilistic positioning of another subatomic particle meaning that these already super-complex particles can actually work together to create a super-complex ecosystem.

This is called quantum entanglement.

So in short, subatomic particles can theoretically have multiple probabilistic states at once, and all those probabilistic states can work together again, all at once to accomplish their task.

And that, in a nutshell, is the scientific breakthrough that stumped Einstein back in the early 1900s.

It goes against everything classical mechanics had taught us about the world. It goes against common sense. But its true. Its real. And now, for the first time ever, we are learning how to harness this unique phenomenon to change everything about everything

This is why the U.S. government is pushing forward on developing a National Quantum Internet in southwest Chicago. It understands that this tech could be more revolutionary than the discovery of fire or the invention of the wheel.

I couldnt agree more.

Mark my words. Everything will change over the next few years because of quantum mechanics and some investors will make a lot of money.

The study of quantum theory has led to huge advancements over the past century. Thats especially true over the past decade. Scientists at leading tech companies have started to figure out how to harness the power of quantum mechanics to make a new generation of super quantum computers. And theyre infinitely faster and more powerful than even todays fastest supercomputers.

And in fact, Haim Israel, managing director of research at Bank of America, believes that: By the end of this decade, the amount of calculations that we can make [on a quantum computer] will be more than the atoms in the visible universe.

Again, the physics behind quantum computers is highly complex, but heres my shortened version

Todays computers are built on top of the laws of classical mechanics. That is, they store information on what are called bits, which can store data binarily as either 1 or 0.

But what if you could turn those classical bits into quantum bits qubits to leverage superposition to be both 1 and 0 stores at once?

Further, what if you could leverage entanglement and have all multi-state qubits work together to solve computationally taxing problems?

Theoretically, youd create a machine with so much computational power that it would make todays most advanced supercomputers seem ancient.

Thats exactly whats happening today.

Google has built a quantum computer that is about 158 million times faster than the worlds fastest supercomputer.

Thats not hyperbole. Thats a real number.

Imagine the possibilities if we could broadly create a new set of quantum computers that are 158 million times faster than even todays fastest computers

Imagine what AI could do.

Today, AI is already being used to discover and develop new drugs and automate manual labor tasks like cooking, cleaning, and packaging products. It is already being used to write legal briefs, craft ads, create movie scripts, and more.

And thats with AI built on top of classical computers.

But built upon quantum computers computer that are a 158 million times faster than classical computers AI will be able to do nearly everything.

The economic opportunities at the convergence of artificial intelligence and quantum computing are truly endless.

Quantum computing is a game-changer thats flying under the radar.

Its not just another breakthrough its the seismic shift weve been waiting for, rivaling the impact of the internet and the discovery of fire itself.

We think the top stocks at the convergence of AI and QC have a realistic opportunity to soar 1,000% over the next few years alone.

And at the epicenter of this boom is one stock that stands out from the pack.

It is the unrivaled technical and commercial leader in quantum computing. It could be a true millionaire-maker opportunity.

And today, that stock is trading for less than $15.

Learn all about this front-runner and its top-secret technology before the stock soars to $100-plus.

On the date of publication, Luke Lango did not have (either directly or indirectly) any positions in the securities mentioned in this article.

Excerpt from:
Putting AI on the Fast Track to Sure-Fire Success - InvestorPlace