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What is quantum cloud computing?

Quantum cloud computing makes quantum computing resources available to organizations, academics and other users through cloud technology.

Cloud-based quantum computers function at greater speeds, with higher computing power than conventional computers, because they employ the principles of quantum physics when solving complex computational problems.

Different types of quantum computers exist, such as quantum annealers, analog quantum simulators and universal quantum computers. Quantum annealers are considered the least powerful among quantum computers but work well to solve optimization problems. Analog quantum simulators, on the other hand, are powerful systems that can solve physics and biochemistry problems.

Universal quantum computers are the most powerful and widely used type of quantum computer. They are also the most difficult to build. Universal computing can potentially access up to 1 million qubits (basic units of quantum information). However, the current technology can only access around 100 to 400 qubits.

How is all of this relevant to blockchain technology? Because quantum computing is incredibly powerful, it has understandably raised concern in the blockchain community, as it could potentially be used to the detriment of blockchain technology as we know it today.

First, quantum computing can hypothetically be used to gain an unfair advantage over other proof-of-work (PoW) miners and possibly dominate blockchain mining. This places decentralized PoW networks such as Bitcoin (BTC) and Litecoin (LTC) under the threat of centralization.

Second, quantum computing can also theoretically decrypt encryption codes used by blockchains. This means that quantum computing could enable an attack on a blockchain network using cryptography. However, its not all doom and gloom for cryptographic systems, as quantum cloud computing may also offer an effective solution for protecting and strengthening blockchains from quantum attacks.

Quantum cloud computing employs quantum principles to distributed computing, while cloud computing uses remote servers to provide distributed computing services.

Cloud computing simply refers to providing services such as data storage, servers, databases and networking via the internet. Instead of storing data on physical servers onsite, for example, an organization can opt for cloud storage services to cut hardware maintenance and other costs.

Quantum cloud computing, on the other hand, derives from quantum computing a form of computing that uses quantum mechanics principles to solve complex problems. It provides quantum computers for users to access quantum-enabled services and solutions through the cloud.

Companies using cloud computing, such as Google, Amazon, IBM and Microsoft, are also at the forefront of developing quantum computers to refine computing technology and enable more users to access quantum computers through the cloud. IBMs Osprey quantum computer, for example, features 433 qubits. The company reportedly plans to scale up to 4,000 qubits by 2025.

Related: Cryptocurrency vs. quantum computing: A deep dive into the future of cryptocurrencies

Akin to platform-as-a-service solutions, quantum cloud computing services work by connecting users directly to quantum processors, emulators and simulators.

Physical quantum computers are very complex, making cloud-based access an ideal setup for those needing to harness the power of quantum computing without purchasing their own machine.

According to IBM, its quantum hardware systems are roughly the size of an average car mainly comprised of cooling systems to ensure that the superconducting processor remains at the ultra-cold ideal operating temperature.

Quantum hardware systems consist of superfluids that work to super-cool the system; superconductors, which form a Josephson junction to carry charges through quantum tunneling; and qubits facilitating behavior control and information relay.

Qubits can perform an important function called superposition, which allows them to place the quantum information they hold in a state of superposition or a combination of all the possible configurations of the qubits. This phenomenon allows for the creation of multidimensional computational spaces, facilitating the solution of complex problems.

Another thing that bears understanding when talking about quantum computing is the concept of entanglement a quantum mechanical effect. Entanglement refers to correlations between the behavior of two separate things. In the context of quantum entanglement, as qubits become entangled, they cause changes to other qubits, allowing the system to find solutions faster than conventional computers.

Contrary to the widespread but mistaken belief that quantum computing can solve complex problems by trying every possible configuration to a problem in parallel, quantum computers leverage qubit entanglement to explore probabilities. Then, they carry out an algorithm to increase the chances of coming up with the best possible answer.

Quantum computing can potentially solve previously intractable problems in various fields, such as economics, drug design and development, finance, logistics, and more.

For example, large-scale quantum cloud computing platforms can be used to solve problems related to optimization in logistics and scheduling of resources in a business context. In healthcare, quantum cloud computing can potentially analyze large volumes of patient data to find the most effective treatments for specific illnesses.

Moreover, in the cybersecurity field, quantum computers can use their enhanced computing power to help combat cybercrime and data breaches. The benefits of quantum cloud computing are plenty. One significant benefit is that it allows organizations to access the power of quantum computing without purchasing their own machine and cooling systems.

It also allows quantum researchers, such as quantum physics students and scholars, to understand quantum principles better, and perform experiments without physically needing to access a quantum computer.

Among the current applications of quantum cloud computing are those related to quantum algorithm testing.

Specifically, quantum algorithms are created on conventional computers and tested on quantum computers to ensure viability. Because of the high technical cost and barriers to entry involved with quantum computing, cloud quantum computing allows businesses and researchers to leverage the technology to explore various quantum computing applications.

Quantum computing is still in its early days in terms of development and implementation, so adoption is still low. However, making the technology available through distributed cloud computing is a game-changer that opens the doors to many potential applications in the future.

Experts project that implementing cloud-based quantum computing might be more challenging than artificial intelligence, which has boomed considerably over the past decade.

This challenge is partly due to the complex technical requirements of quantum computers. Because quantum hardware systems require extremely cold operating conditions, cloud providers will need to construct dedicated spaces for quantum computers. The data centers in existence today are ill-equipped for this purpose.

Furthermore, quantum computing and its related software are still in their early stages of development and implementation, so the overall industry is still considered nascent. Programmers will also need to acquire new arithmetic and logic skills, as typical digital programming approaches differ vastly from the ones required for quantum computing.

That said, experts remain optimistic about the potential of cloud quantum computing, believing that it can provide significant benefits for various industries such as finance, logistics, healthcare and technology.

As the technology evolves, it is still highly likely that cloud-based quantum computing will be widely available in the near future, making it easier and more cost-efficient for businesses to access this powerful technology.

Cloud companies will likely be the first quantum-as-a-service providers, as the service will simply expand current offerings. If deployed and marketed effectively, quantum cloud computing may be as pervasive as artificial intelligence and machine learning implementations.

Related: 10 emerging technologies in computer science that will shape the future

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What is quantum cloud computing, and how does it work? - Cointelegraph

Up and down orientations of qubits at the nodes of a quasicrystal yield multiple magnetic configurations. Different textures can be created by applying different magnetic fields. A D-Wave quantum annealer demonstrated potential for material prototyping, experimenting with actual spins in purposely designed geometries. Credit: Los Alamos National Laboratory

Research using a quantum computer as the physical platform for quantum experiments has found a way to design and characterize tailor-made magnetic objects using quantum bits, or qubits. That opens up a new approach to develop new materials and robust quantum computing.

"With the help of a quantum annealer, we demonstrated a new way to pattern magnetic states," said Alejandro Lopez-Bezanilla, a virtual experimentalist in the Theoretical Division at Los Alamos National Laboratory. Lopez-Bezanilla is the corresponding author of a paper about the research in Science Advances.

"We showed that a magnetic quasicrystal lattice can host states that go beyond the zero and one bit states of classical information technology," Lopez-Bezanilla said. "By applying a magnetic field to a finite set of spins, we can morph the magnetic landscape of a quasicrystal object."

"A quasicrystal is a structure composed by the repetition of some basic shapes following rules different to those of regular crystals," he said.

For this work with Cristiano Nisoli, a theoretical physicist also at Los Alamos, a D-Wave quantum annealing computer served as the platform to conduct actual physical experiments on quasicrystals, rather than modeling them. This approach "lets matter talk to you," Lopez-Bezanilla said, "because instead of running computer codes, we go straight to the quantum platform and set all the physical interactions at will."

Lopez-Bezanilla selected 201 qubits on the D-Wave computer and coupled them to each other to reproduce the shape of a Penrose quasicrystal.

Since Roger Penrose in the 1970s conceived the aperiodic structures named after him, no one had put a spin on each of their nodes to observe their behavior under the action of a magnetic field.

"I connected the qubits so all together they reproduced the geometry of one of his quasicrystals, the so-called P3," Lopez-Bezanilla said. "To my surprise, I observed that applying specific external magnetic fields on the structure made some qubits exhibit both up and down orientations with the same probability, which leads the P3 quasicrystal to adopt a rich variety of magnetic shapes."

Manipulating the interaction strength between qubits and the qubits with the external field causes the quasicrystals to settle into different magnetic arrangements, offering the prospect of encoding more than one bit of information in a single object.

Some of these configurations exhibit no precise ordering of the qubits' orientation.

"This can play in our favor," Lopez-Bezanilla said, "because they could potentially host a quantum quasiparticle of interest for information science." A spin quasiparticle is able to carry information immune to external noise.

A quasiparticle is a convenient way to describe the collective behavior of a group of basic elements. Properties such as mass and charge can be ascribed to several spins moving as if they were one.

More information: Alejandro Lopez-Bezanilla, Field-induced magnetic phases in a qubit Penrose quasicrystal, Science Advances (2023). DOI: 10.1126/sciadv.adf6631. http://www.science.org/doi/10.1126/sciadv.adf6631

Journal information: Science Advances

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Qubits put new spin on magnetism: Boosting applications of quantum computers - Phys.org

by Ingrid Fadelli , Phys.org

Quantum computing systems have the potential to outperform classical computers on some tasks, helping to solve complex real-world problems in shorter times. Research teams worldwide have thus been trying to realize this quantum advantage over traditional computers, by creating and testing different quantum systems.

Researchers at Tsinghua University recently developed a new programmable quantum phononic processor with trapped ions. This processor, introduced in a paper in Nature Physics, could be easier to scale up in size than other previously proposed photonic quantum processors, which could ultimately enable better performances on complex problems.

"Originally, we were interested in the proposal of Scott Aaronson and others about Boson sampling, which might show the quantum advantages of simple linear optics and photons," Kihwan Kim, one of the researchers who carried out the study, told Phys.org. "We were wondering if it is possible to realize it with the phonons in a trapped ion system."

The use of phonons (i.e., sound waves or elementary vibrations) to create quantum computing systems was theoretically explored for some time. In recent years, however, physicists created trapped-ion systems created the technology necessary to use phonons as a quantum information processing resource, rather than mere mediators for entangling qubits.

"It has been shown that phonons at a harmonic potential can coherently transfer to the other harmonic potential and these phonons can interfere with each other," Kihwan Kim explained. "When we learned that a modified boson sampling (Gaussian boson sampling) can also be applied to a chemical problem (i.e., vibrational sampling) we demonstrated the sampling of SO2 molecules and developed a method to create a highly entangled phononic state; yet this was limited to a single ion. In this work, we finally implemented the phononic network in a scalable way, overcoming the limits of single ions."

The system created by Kihwan Kim and his colleagues is a programmable bosonic network, a network consisting of a set of bosonic modes, connected to each other via controllable beam splitters. They realized this network using phonons, excitations of collective vibrational modes that are also bosons.

"Our system is scalable because the number of collective vibrational modes proportionally increases with the number of ions and we demonstrated how to use the additional vibrational modes and ions in a programmable way," Kihwan Kim said. "Basically, we control the vibrational mode by a properly assigned qubit. We can program the phase and ratio of each beam splitter by controlling the phase and the duration of the individually addressed laser beams."

The phononic quantum processor created by Kihwan Kim and his colleagues has several advantages over previously proposed bosonic networks. Firstly, the input and output of the phonons in the processor are deterministically prepared and detected. Furthermore, the loss of phonons over time is minimal, while in other bosonic networks based on photons losses is an issue to overcome.

"Boson sampling can be a powerful tool for certain tasks in quantum algorithms and simulations," Myungshik Kim, another researcher at Imperial College involved in the study, told Phys.org. "While boson sampling has been mostly realized by photons, there are technical difficulties in realizing scalable boson sampling because single photon generation is probabilistic and photon loss on a chip is high. In our work we use phonons of the ions in a harmonic potential instead of photons. The clear advantages of this are that we can generate quantum states of phonons deterministically and do not lose phonons during the process."

Boson sampling is a model of quantum computation that can be very advantageous for tackling some tasks using quantum algorithms or simulations. Boson sampling is typically realized using several distinct techniques.

Kihwan Kim, Myungshik Kim and their colleagues were able to implement all these techniques in a single platform, which could have notable advantages for the development of larger systems. This was achieved by reconstructing the states of phonons in their network.

In the future, the phononic network they created could be scaled up to achieve large-scale and programmable boson sampling. In addition, their work could inspire the development of other programmable quantum networks based on phonons and trapped ions.

"Now, it is important for us to scale up our system and hopefully use it to demonstrate quantum advantage over classical computing," Kim added. "At the same time, we may also try to achieve continuous-variable universal quantum computation with the qubit-controlled beam splitter."

More information: Wentao Chen et al, Scalable and programmable phononic network with trapped ions, Nature Physics (2023). DOI: 10.1038/s41567-023-01952-5

Journal information: Nature Physics

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A scalable and programmable quantum phononic processor based on trapped ions - Phys.org

Image Credits: Kirsten Korosec

Its a very modern conjurers trick: Create a SXSW talk out of thin air, with the help of generative AI. Thats what whurley did this year in Austin.

It took nine weeks for whurley a staple of the Austin tech scene to create and prepare for a keynote at SXSW 2018, where he would debut Strangeworks, a quantum computing startup he co-founded and runs. Five years later, generative AI would complete the task in just a few hours.

And it was actually pretty good. The 45-minute speech was comprehensive, interesting and struck a whurley-like tone. There was one swear word (fuck) and a few jokes (including two lawyer ones) that the audience laughed at. It seemed that the trickiest part, at least while he was on stage, was reading the script the AI had provided off of his tablet. (Whurley is known for his free-wheeling style on the SXSW stage, where he is a regular.)

The kicker? He waited until the end to deliver the punchline.

Everything today from the slides to the speech Im reading now is created by generative AI, he said onstage before launching into the how and why of it all. A buzz of whispers, wows and chuckles spread through the room filled with hundreds of SXSW attendees.

Strangeworks might just be the first startup to tap generative AI for all of its on and off stage content at SXSW. And while its a novel and fun demonstration, the experiment also illustrates the flexibility of AI tools and its growing popularity.

Why bother? Exposure and education, whurley told TechCrunch this week after the event.

We are on the verge of the greatest period of technological advances in the history of mankind. I feel people are not only not ready for this, theyre not even aware its happening. I wanted to put a spotlight on it, he said. Were going to see more changes in the next decade than we have in the past 100 years. People can naysay it all they want, but the technological change about to occur can not be stopped. The convergence of quantum computing and AI will be a step function, if not several step functions, for scientific discovery and advancement.

The experiment started as many do with a limited scope. Whurley used generative AI back in October 2022 to write a description for his SXSW talk entitled Quantum AI: Why Your Future Depends on Quantum Computing & Artificial Intelligence. And no he didnt tell SXSW organizers.

This all started with a prompt, he said while onstage. I said write a South by Southwest abstract of 800 words. Heres the concept, heres a title I gave it and a few points and everything you saw on South by Southwest website was created by ChatGPT. And I submitted it.

ChatGPT is the image- and text-understanding AI model powered by GPT 3.5 and developed by OpenAI. A new version of the underlying engine, GPT-4, was released March 14.

His prompt was:

Write an 800 word abstract for a SXSW keynote for a session called QuantumAI: Why your future depends on the convergence of Quantum Computing & Artificial Intelligence in which the speaker discusses the advances in quantum computing and artificial intelligence, the challenges facing our species, and the inevitable convergence that may lead to a quantum super intelligence that will forever change our world.

Just days before the SXSW featured session, whurley decided to take it further. He asked the AI to use the abstract to create an outline of what the presentation might look like. After a few tweaks (or reprompting as he calls it), the outline met his approval.

His prompt:

This is great, I need to come up with enough slides to discuss this topic for an hour. Can you suggest what a potential out line for a 1 hour talk on this would be?

Whurley shared it with his team at Strangeworks and collectively they decided to go all in. At that point, I told them the plan was to start on everything needed for the keynote tomorrow at 11:30 a.m., he told TechCrunch.

Strangeworks creative team Casey Barthels, Nicole Majeske and Ada Onyiukeused Midjourney, an AI generative art tool, to make the slides and graphics for the presentation. And then they upped the ante again by having Midjourney create the story and graphics in a seven-page printed publication featuring the Strangeworks mascot Schrody Cat. The publication was handed out to attendees.

And then the night before last, I thought if did an outline, the abstract and all the slides, why cant we just put words in my mouth too? he said. Whurley took all of his previous prompts and fed it into GPT-4, which had been released Tuesday.

In other words, what would become the final script, graphics and slides were created the morning of the keynote. And they cut it close. As we pulled into the hotel at almost 11 am on the dot, I took the final version of the script and cut and pasted it into the teleprompter software I had downloaded to my iPad, he wrote to TechCrunch in a text following the event.

Its certainly the biggest risk Ive ever taken at SXSW, he said.

The generative AI was also used to create whurleys personal website, which debuted Wednesday, featuring hundreds of blogs in whurleys voice. He worked with collaborator David Hudson of Big Human on the blog project.

Those blogs were deleted to make way for another project that launched Thursday. The Strangeworks CEO ran the prompt through ChatGPT again, this time asking it to publish the website and blogs in 10 languages, including Spanish, Chinese, Italian and Arabic.

Whurley said the response has been overwhelmingly positive. He noted that a few people who are anti AI/technology have made snide comments or veiled threats via social media, but again the detractors are few and far between.

Read more:
Strangeworks might be the first startup to rely on AI to create everything it brought to SXSW - TechCrunch

Google and Alphabet CEO Sundar Pichai

Hype surrounding the rise of ChatGPT and the supposed ground Google is losing to Microsoft and OpenAI in the search wars has overshadowed more important developments in computing, progress which will have far greater implications than which website serves up better tax advice.

Quantum computing is the holy grail of scientists and researchers, but its still decades away from reality. Googles parent company, Alphabet, however moved the ball down the field last month with news that it found ways to ameliorate one of the biggest problems facing the nascent field: accuracy.

To date, all computing is done on a binary scale. A piece of information is stored as either one or zero, and these binary units (bits) are clumped together for further calculation. We need four bits to store the number eight (1000 in binary), for example. Its slow and clunky, but at least its simple and accurate. Silicon chips have been holding and processing bits for almost seven decades.

Quantum bits qubits can store data in more than two forms (it can be both 1 and 0 at the same time). That means larger chunks of information can be processed in a given amount of time. Among the many downsides is that the physical manifestation of a qubit requires super-cold temperatures just above zero degrees Kelvin and are susceptible to even the minutest amount of interference such as light. Theyre also error prone, which is a big problem in computing.

In a paper published in Nature last month, Google claims to have made a huge breakthrough in an important sub-field called quantum error correction. Their approach is quite simple. Instead of relying on individual physical qubits, scientists store information across many physical qubits but then view this collection as a single one (called a logical qubit).

Google had theorised that clumping a larger number of physical qubits to form a single logical qubit would reduce error rate. In its research paper, outlined in a blog post by CEO Sundar Pichai, the team found that a logical qubit formed from 49 physical qubits did indeed outperform one comprised of 17.

In reality, dedicating 49 qubits to the handling of just a single logical one sounds inefficient and even overkill. Imagine storing your photos on 49 hard drives just to ensure that, collectively, a single hard drive is error free. But given the vast potential of quantum computing, even such baby steps amount to significant progress.

More importantly, it gives the broader scientific community a basis from which to build on this knowledge to further advance related fields including materials science, mathematics and electrical engineering which will all be needed to make an actual quantum computer reality. The hope of building a system that can solve a problem which no current machine could feasibly manage is called quantum supremacy.

Four years ago, Google said it completed a test in 200 seconds for a task that would take a conventional supercomputer thousands of years, proof that were on the path to quantum supremacy.

But like artificial intelligence tools such as ChatGPT, proving they work is only one part of the puzzle. High accuracy and low error rates something recent chatbots are prone to remain elusive. Improvement on this front is a major goal for developers of both technologies, with OpenAI this week saying its new GPT-4 is 40% more likely to produce factual results than its predecessor.

Unfortunately, a supercooled computer crunching data isnt as fun as a digital assistant that can write limericks or draft a school essay. But in future these breakthroughs will be as comparable as the entertainment value of television versus the world-changing feat of landing a human on the moon. (c) 2023 Bloomberg LP

Feasible is a nebulous term, but generally means completion in a reasonable amount of time such as minutes or days, instead of years.

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Google is making breakthroughs much bigger than AI - TechCentral - TechCentral