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November 23, 2020• Physics 13, 183

Classical computers can efficiently simulate the behavior of quantum computers if the quantum computer is imperfect enough.

With a few quantum bits, an ideal quantum computer can process vast amounts of information in a coordinated way, making it significantly more powerful than a classical counterpart. This predicted power increase will be great for users but is bad for physicists trying to simulate on a classical computer how an ideal quantum computer will behave. Now, a trio of researchers has shown that they can substantially reduce the resources needed to do these simulations if the quantum computer is imperfect [1]. The arXiv version of the trios paper is one of the most Scited papers of 2020 and the result generated quite a stir when it first appeared back in FebruaryI overheard it being enthusiastically discussed at the Quantum Optics Conference in Obergurgl, Austria, at the end of that month, back when we could still attend conferences in person.

In 2019, Google claimed to have achieved the quantum computing milestone known as quantum advantage, publishing results showing that their quantum computer Sycamore had performed a calculation that was essentially impossible for a classical one [2]. More specifically, Google claimed that they had completed a three-minute quantum computationwhich involved generating random numbers with Sycamores 53 qubitsthat would take thousands of years on a state-of-the-art classical supercomputer, such as IBMs Summit. IBM quickly countered the claim, arguing that more efficient memory storage would reduce the task time on a classical computer to a couple of days [3]. The claims and counterclaims sparked an industry clash and an intense debate among supporters in the two camps.

Resolving the disparity between these estimates is one of the goals of the new work by Yiqing Zhou, of the University of Illinois at UrbanaChampaign, and her two colleagues [1]. In their study, they focused on algorithms for classically replicating imperfect quantum computers, which are also known as NISQ (noisy intermediate-scale quantum) devices [4]. Todays state-of-the-art quantum computersincluding Sycamoreare NISQ devices. The algorithms the team used are based on so-called tensor network methods, specifically matrix product states (MPS), which are good for simulating noise and so are naturally suited for studying NISQ devices. MPS methods approximate low-entangled quantum states with simpler structures, so they provide a data-compression-like protocol that can make it less computationally expensive to classically simulate imperfect quantum computers (see Viewpoint: Pushing Tensor Networks to the Limit).

Zhou and colleagues first consider a random 1D quantum circuit made of neighboring, interleaved two-qubit gates and single-qubit random unitary operations. The two-qubit gates are either Controlled-NOT gates or Controlled-Z (CZ) gates, which create entanglement. They ran their algorithm for NISQ circuits containing different numbers of qubits, N, and different depths, Da parameter that relates to the number of gates the circuit executes (Fig. 1). They also varied a parameter in the MPS algorithm. is the so-called bond dimension of the MPS and essentially controls how well the MPS capture entanglement between qubits.

The trio demonstrate that they can exactly simulate any imperfect quantum circuit if D and N are small enough and is set to a value within reach of a classical computer. They can do that because shallow quantum circuits can only create a small amount of entanglement, which is fully captured by a moderate . However, as D increases, the team finds that cannot capture all the entanglement. That means that they cannot exactly simulate the system, and errors start to accumulate. The team describes this mismatch between the quantum circuit and their classical simulations using a parameter that they call the two-qubit gate fidelity fn. They find that the fidelity of their simulations slowly drops, bottoming out at an asymptotic value f as D increases. This qualitative behavior persists for different values of N and . Also, while their algorithm does not explicitly account for all the error and decoherence mechanisms in real quantum computers, they show that it does produce quantum states of the same quality (perfection) as the experimental ones.

In light of Googles quantum advantage claims, Zhou and colleagues also apply their algorithm to 2D quantum systemsSycamore is built on a 2D chip. MPS are specifically designed for use in 1D systems, but the team uses well-known techniques to extend their algorithm to small 2D ones. They use their algorithm to simulate an N=54, D=20 circuit, roughly matching the parameters of Sycamore (Sycamore has 54 qubits but one is unusable because of a defect). They replace Googles more entangling iSWAP gates with less entangling CZ gates, which allow them to classically simulate the system up to the same fidelity as reported in Ref. [2] with a single laptop. The simulation cost should increase quadratically for iSWAP-gate circuits, and although the team proposes a method for performing such simulations, they have not yet carried them out because of the large computational cost it entails.

How do these results relate to the quantum advantage claims by Google? As they stand, they do not weaken or refute claimswith just a few more qubits, and an increase in D or f, the next generation of NISQ devices will certainly be much harder to simulate. The results also indicate that the teams algorithm only works if the quantum computer is sufficiently imperfectif it is almost perfect, their algorithm provides no speed up advantage. Finally, the results provide numerical insight into the values of N, D, f, and for which random quantum circuits are confined to a tiny corner of the exponentially large Hilbert space. These values give insight into how to quantify the capabilities of a quantum computer to generate entanglement as a function of f, for example.

So, whats next? One natural question is, Can the approach here be transferred to efficiently simulate other aspects of quantum computing, such as quantum error correction? The circuits the trio considered are essentially random, whereas quantum error correction circuits are more ordered by design [5]. That means that updates to the new algorithm are needed to study such systems. Despite this limitation, the future looks promising for the efficient simulation of imperfect quantum devices [6, 7].

Jordi Tura is an assistant professor at the Lorentz Institute of the University of Leiden, Netherlands. He also leads the institutes Applied Quantum Algorithms group. Tura obtained his B.Sc. degrees in mathematics and telecommunications and his M.Sc. in applied mathematics from the Polytechnic University of Catalonia, Spain. His Ph.D. was awarded by the Institute of Photonic Sciences, Spain. During his postdoctoral stay at the Max Planck Institute of Quantum Optics in Germany, Tura started working in the field of quantum information processing for near-term quantum devices.

A nanopatterned magnetic structure features an unprecedently strong coupling between lattice vibrations and quantized spin waves, which could lead to novel ways of manipulating quantum information. Read More

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Imperfections Lower the Simulation Cost of Quantum Computers - Physics

BEIJING--(BUSINESS WIRE)--The preliminary round of the 2020-2021 ASC Student Supercomputer Challenge (ASC20-21) officially kicked off on November 16, 2020. More than 300 university teams from five continents registered to participate in this competition. Over the next two months, they will be challenged in several cutting-edge applications of Supercomputing and AI. The 20 teams that eventually make out of the preliminaries will participate in the finals from May 8 to 12, 2021 at Southern University of Science and Technology in Shenzhen, China. During the finals, they will compete for various awards including the Champion, Silver Prize, Highest LINPACK, and e- Prize.

Among the registered participants for ASC20-21 are three prior champion teams: the SC19/SC20 champion team of Tsinghua University, the ISC20 champion team of University of Science and Technology of China, and the ASC19 champion of National Tsing Hua University. Other power competitors include teams from University of Washington (USA), University of Warsaw (Poland), Ural Federal University (Russia), Monash University (Australia), EAFIT University (Columbia) and so much more.

For the tasks of this preliminary round of merged ASC20 and ASC21, the organizing committee has retained the quantum computing simulation and language exam tasks from the ASC20, and added a new fascinating, cutting-edge task in astronomy -- searching for pulsars.

Pulsars are fast-spinning neutron stars, and remnants of collapsed super stars. Pulsars feature a high density and strong magnetic field. By observing and studying the extreme physic of pulsars, the scientists can delve into the mysterious space around black holes and detect the gravitational waves triggered from the intense merge of super massive black holes in distant galaxies. Because of the unique nature of pulsars, the Nobel Prize in physics has been awarded twice for pulsar-related discoveries. Using radio telescopes over the previous decades, astronomers have discovered nearly 3,000 pulsars with 700 being discovered by PRESTO, the open-source pulsar search and analysis software. In ASC20-21, the participants are asked to use PRESTO from its official website, and the observational data from Five-hundred-meter Aperture Spherical radio Telescope (FAST), the worlds largest single-dish radio telescope located in Guizhou, China, operated by National Astronomical Observatories, Chinese Academy of Sciences. Participating teams should achieve the applications maximum parallel acceleration, while searching for a pulsar in the FAST observational data loaded in the computer cluster they build. Practically the teams will need to understand the pulsar search process, complete the search task, analyze the code, and optimize the PRESTO application execution, by minimizing the computing time and resources.

The quantum computing simulation task will require each participating team to use the QuEST (Quantum Exact Simulation Toolkit) running on computer cluster to simulate 30 qubits in two cases: quantum random circuits (random.c), and quantum fast Fourier transform circuits (GHZ_QFT.c). Quantum simulations provides a reliable platform for studying of quantum algorithms, which are particularly important because quantum computers are not practically available yet in the industry.

The Language Exam task will require all participating teams to train AI models on an English Cloze Test dataset, striving to achieve the highest "test scores". The dataset covers multiple levels of English language tests used in China.

This years ASC training camp will be held on November 30 to help the participating teams from all around the world prepare for the competition. HPC and AI experts from Chinese Academy of Sciences, Peng Cheng Laboratory, State Key Laboratory of High-end Server & Storage Technology will introduce in details the competition rules, computer cluster build and optimization, and provide guidance.

About ASC

The ASC Student Supercomputer Challenge is the worlds largest student supercomputer competition, sponsored and organized by Asia Supercomputer Community in China and supported by Asian, European, and American experts and institutions. The main objectives of ASC are to encourage exchange and training of young supercomputing talent from different countries, improve supercomputing applications and R&D capacity, boost the development of supercomputing, and promote technical and industrial innovation. The first ASC Student Supercomputer Challenge was held in 2012 and since has attracted nearly 10,000 undergraduates from all over the world. Learn more ASC at https://www.asc-events.org/.

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ASC20-21 Student Supercomputer Challenge Kickoff: Quantum Computing Simulations, AI Language Exam and Pulsar Searching with FAST - Business Wire

In this weeks podcast, Enrique Blair on quantum computing, Walter Bradley Center director Robert J. Marks talks with fellow computer engineer Enrique Blair about why Quantum mechanics pioneer Niels Bohr said, If quantum mechanics hasnt profoundly shocked you, you havent understood it yet. Lets look at some of the reasons he said that:

The Show Notes and transcript follow.

Enrique Blair: Its really quite different from our daily experience. Quantum mechanics really is a description of the world at the microscopic scale. And its really weird, because there are things that initially we thought maybe were particles but then we learned that they have wave-like behaviors. And there are other things that we thought were waves and then we discovered they have particle-like behaviors.

But thats hardly the strangest part. The strangest part is that a quantum particle does not actually have a position until we measure it, according to the generally accepted Copenhagen interpretation of quantum mechanics.

Robert J. Marks: Whats the Copenhagen interpretation?

Enrique Blair (pictured): Its that the quantum mechanical wave function describes measurement outcomes in probabilities. You cant predict with certainty the outcome of a measurement. Which is really shocking, because in the classical world, if you have a particle and you know its position and its velocity, you can predict where its going to be in the next second or minute or hour. Now in quantum mechanics, the really weird thing is, we say that a particle doesnt even have a position until you measure its position.

Robert J. Marks: It doesnt exist?

Enrique Blair: Not that it doesnt exist, but its position is not defined.

Dr. Marks compared quantum mechanics (QM) to one of the characters in a 1999 film, Mystery Men, featuring inept amateur superheroes, including one who says, Im invisible as long as nobodys looking at me. With QM, thats not a joke. The quantum particle doesnt have a position until we measure it. But how did we discover this? The story goes back to the early 1800s when British physicist Thomas Young (17731829) did a famous experiment with a card held up to a small window

Enrique Blair: Youngs double-slit experiment goes all the way back to 1801, where Young shot light at a couple of slits and then the light passing through the slits would show up on a screen behind them.

So light behaves like a wave, with interference patterns. But what happens when we try doing the same thing with a single particle of lighta photon? Thats something we can do nowadays.

Enrique Blair: We can reduce a beam of light so that its single photon. One photon is emitted at a time, and were shooting it at our double slit again.

What happens when each particle of light goes through these slits? Well, each particle splats up against this screen, and so you can know where the photon hits. But if you do this over a long period of time, the interference pattern shows up again. You have particles hitting the screen, so we see the particle behavior. But we also see the interference pattern which suggests that okay, weve got some wave interference going on here.

So the only way to explain both of these at the same time is that each photon, which is an indivisible packet of light, has to go through both slits at the same time and interfere with itself, and then the buildup of many, many photons gives you that interference pattern.

Robert J. Marks: A particle was hypothesized to go through both slits?

Enrique Blair: Yes, and thats the mind-blowing ramification of this thing.

Robert J. Marks: How do we decide which slit the particles go through? Suppose we went down and we tried to measure? We put out one photon and we put it through the double slit. Weve tried to measure which slit it went through. If its a particle, it can only go through one, right?

Enrique Blair: Right. That introduces this concept of measurement. Like you said, which slit does it go through? Now the interesting thing is, if we know which slit it goes through maybe we set up a detector and we say, Hey, did it go through Slit One or Slit Two? we detect that, we measure it and the interference pattern goes away because now its gone through one slit only, not both.

Robert J. Marks: Just by the act of observation, we are restricting that photon to go through one slit or the other. Observation really kind of screws things up.

Enrique Blair: Thats right. This is one of the things that is hard to understand about quantum mechanics. In the classical world that we deal with every day, we can just observe something and we dont have to interact with it. So we can measure somethings position or its velocity without altering it. But in quantum mechanics, observation or measurement inherently includes interacting with that thing, that particle.

Again, youve got this photon that goes through both slits, but then you measure it and it actually ends up going through oneonce you measure it.

Robert J. Marks: This reminds me again of Invisible Boy in Mystery Men. The photon goes through one of the two slits while youre looking at it. Unless you look away. Then it goes through both slits.

Enrique Blair: Right. Very tricky, those photons.

Next: How scientists have learned to work with the quantum world

Note: The illustration of the double-slit experiment in physics is courtesy NekoJaNekoJa and Johannes Kalliauer (CC BY-SA 4.0).

You may also enjoy: A materialist gives up on determinism. Evolutionary biologist Jerry Coyne undercuts his own argument against free will by admitting that quantum phenomena are real (Michael Egnor)

Quantum randomness gives nature free will. Whether or not quantum randomness explains how our brains work, it may help us create unbreakable encryption codes (Robert J. Marks)

Podcast Transcript Download

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Here's Why the Quantum World Is Just So Strange - Walter Bradley Center for Natural and Artificial Intelligence

By the times that A.I. comes back and tells you that, then we have reached artificial general intelligence, and you should be very scared or very excited, depending on your point of view, Dr. Tegmark said. The reason Im working on this, honestly, is because what I find most menacing is, if we build super-powerful A.I. and have no clue how it works right?

Dr. Thaler, who directs the new institute at M.I.T., said he was once a skeptic about artificial intelligence but now was an evangelist. He realized that as a physicist he could encode some of his knowledge into the machine, which would then give answers that he could interpret more easily.

That becomes a dialogue between human and machine in a way that becomes more exciting, he said, rather than just having a black box you dont understand making decisions for you.

He added, I dont particularly like calling these techniques artificial intelligence, since that language masks the fact that many A.I. techniques have rigorous underpinnings in mathematics, statistics and computer science.

Yes, he noted, the machine can find much better solutions than he can despite all of his training: But ultimately I still get to decide what concrete goals are worth accomplishing, and I can aim at ever more ambitious targets knowing that, if I can rigorously define my goals in a language the computer understands, then A.I. can deliver powerful solutions.

Recently, Dr. Thaler and his colleagues fed their neural network a trove of data from the Large Hadron Collider, which smashes together protons in search of new particles and forces. Protons, the building blocks of atomic matter, are themselves bags of smaller entities called quarks and gluons. When protons collide, these smaller particles squirt out in jets, along with whatever other exotic particles have coalesced out of the energy of the collision. To better understand this process, he and his team asked the system to distinguish between the quarks and the gluons in the collider data.

We said, Im not going to tell you anything about quantum field theory; Im not going to tell you what a quark or gluon is at a fundamental level, he said. Im just going to say, Heres a mess of data, please separate it into basically two categories. And it can do it.

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Can a Computer Devise a Theory of Everything? - The New York Times

Theres a lingering fear among crypto investors that their bitcoin might get swooped by a hacker.

Thats not very likely, but its not impossible either, particularly once quantum computing gets into the wrong hands. Last year Googles quantum computer called Sycamore was given a puzzle that would take even the most powerful supercomputers 10 000 years to solve and completed it in just 200 seconds, according to Nature magazine.

That kind of processing power unleashed on the bitcoin blockchain which is a heavily encrypted ledger of all bitcoin transactions is a cause for concern.

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The encryption technology used by the bitcoin blockchain has proven itself robust enough to withstand any and all attacks. Thats because of its brilliant design, and ongoing improvements by an ever-growing community of open-source cryptographers and developers.

A report by research group Gartner (Hype Cycle for Blockchain Technologies, 2020) suggests blockchain researchers are already anticipating possible attacks by quantum computers that are perhaps five to 10 years away from commercial availability. Its a subject called Postquantum blockchain which is a form of blockchain technology using quantum-resistant cryptographic algorithms that can resist attack by future quantum computers.

The good news is that quantum-resistant algorithms are likely to remain several steps ahead of the hackers, but its an issue that is drawing considerable attention in the financial, security and blockchain communities.

Postquantum cryptography is not a threat just yet, but crypto exchanges are going to have to deploy quantum-resistant technologies in the next few years, before quantum computers become generally available.

Phishing is probably a bigger threat

In truth, youre far more likely to be hit by a phishing scam, where identity thieves use emails, text messages and fake websites to get you to divulge sensitive personal information such as bank account or crypto exchange passwords.

As a user, you should be using LastPass or similar software to generate complex passwords, along with two-factor authentication (requiring the input of a time-sensitive code before you can access your crypto exchange account).Most good exchanges are enabled for this level of security.

There are many sad stories of bitcoin theft, but these are usually as a result of weak security on the part of the bitcoin holder, much like leaving your wallet on the front seat of your car while you pop into the shop for a minute.

Like all tech breakthroughs, quantum computing can be used for good and bad.

On the plus side, it will vastly speed drug discovery, molecular modelling and code breaking. It will also be a gift to hackers and online thieves, which is why financial services companies are going to have to invest in defensive technologies to keep customer information and assets safe.

Most crypto exchanges invest substantial amounts in security. The vast majority of crypto assets (about 97%) are stored in encrypted, geographically-separated, offline storage. These cannot be hacked.

The risk emerges when bitcoin are moved from offline (or cold storage) to online, such as when a client is about to transact.

But even here, the level of security is usually robust. A further level of protection is the insurance of all bitcoin that are stored in online systems. They also have systems in place to prevent any employee from making off with clients assets, requiring multiple keys before a bitcoin transaction is authorised.

There have been hacks on crypto exchanges in the past (though not on the blockchain itself), and millions of dollars in crypto assets stolen. In more recent years, this has become less common as exchanges moved to beef up their security systems.

In 2014 Mt.Gox, at the time responsible for about 70% of all bitcoin transactions in the world, suffered an attack when roughly 800000 bitcoin, valued at $460 million, were stolen. In 2018, Japan-based crypto exchange Coincheck was hit with a $534 million fraud impacting 260000 investors.

As the value of bitcoin and other crypto assets increases, the incentive for hackers rises proportionately, which is why problems such as quantum-enabled thievery are already being addressed.

Read:Moneyweb Crypto glossary

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Is the blockchain vulnerable to hacking by quantum computers? - Moneyweb.co.za