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mihirjaiswal · 4 months

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A System Architect’s Perspective on Quantum Computing: An Interview with Dr. Gokul Subramanian Ravi

Dr. Gokul Subramanian Ravi has been a career computer scientist. He is an assistant professor and active researcher in quantum computing at the University of Michigan. In this interview, we talked about the philosophy and technology of quantum computers. Dr. Ravi talked about quantum computers, its need, current state, architecture, and quantum advantage at length. While this interview is more technical than previous ones in quantum chats, it is very enjoyable and informative.

Mihir: Why do we need quantum computers? Classical computers are getting better every day. Can’t we just use classical computers for everything?

Dr. Ravi: That’s a good question. The classical computers’ capability is not increasing as fast as it used to be. We all have heard about Moore’s law failing. Thus, there is a fundamental need for new technology. We want more computing capabilities in any form, not specifically quantum computing. Today, computing and computers are the most fundamental driver of innovation. We want to keep pushing for new innovations. That reason motivates us towards emerging technologies, quantum computing being one of them. Some problems are exponentially hard to solve. That means the computational resources required to solve such problems increase exponentially with the increase in problem size. Classical computers quickly reach their limitations in addressing this kind of problem. For example, to discover new medicines, you want to understand chemistry between two chemical molecules. You can’t mix thousands to chemicals together in the lab and study them, so computer simulations are done. Computational models for solving such problems represent each molecule as some numerical interaction and perform calculations to predict the molecular interactions. As the size of molecule increases, the numerical equations become exponentially complicated and soon reach the limits of classical computers. The reason for that is at the molecular level, when we are considering electrons; we cannot ignore some of the non-trivial forces, which we generally ignore in day to day calculations like gravity. With number of electrons, these forces become very large in numbers, hence the exponential growth of the problem. These are called quantum mechanical properties.

When Richard Feynman proposed quantum computing, the idea was that we needed a device that was able to simulate quantum mechanical properties and such a device would be quantum computing machine. Thus, quantum computer is specifically of interest for solving large-scale scientific problems in physics, chemistry etc. Other problems, like factoring, also have important application of quantum computing. If you are able to factor a number quickly, that has implication in security and cryptography. Factoring is a classical problem, not quantum, but there is a method that can solve factoring faster than a classical computer can. Quantum computer has a long way to do. However, in theory, there are quantum, classical as well as scientific problems that can be solved more efficiently using quantum computer than any classical computer.

Mihir: As you said, classical computers are reaching its capacity and no longer growing as fast as they were. As a result, we need new technologies to fill that gap and continue expanding our computational power. Is quantum computing one such new technology or are we calling a group of technologies quantum computing? Are we able to define quantum computer today?

Dr. Ravi: Again a very good question. In general, we would define quantum computer as a technology that is able to exploit quantum mechanical properties towards computing. Within that definition, all different technologies like supercomputing qubit, trapped ion qubit, neutral atoms, and photonic qubits are quantum technology. In their own way they all are exploiting quantum mechanics. If we are being very specific than you are absolutely right that quantum computing is an umbrella term. However, broadly they all fall within the same scope of exploitation of quantum mechanics.

Mihir: In my understanding, a problem has to be converted to a mathematical formula to make an algorithm that can be computed by a classical computer. Is that true for quantum computing also?

Dr. Ravi: I would say yes and no. I would approach this question in two different ways. Think of a problem which can be solved 90% on a classical computer and only last 10% needs a quantum computer because that last part is really exponentially hard. In classical computer, we would use an approximation and perhaps accept a 90% solution. We still need mathematical formula to reach that 90% solution and then improve beyond 90% using a quantum computer. We want to continue to use classical computer to go as far as we can, because quantum computer is always going to be an expensive resource. Now the other question: is the quantum computing also based on a mathematical formula? I would argue, yes to some extent. Let’s take an example of a classical computer. In designing a complex machine learning algorithm, the algorithm would have complex metrics, its addition, multiplication and many complex mathematical operations. When coded onto a classical computer, a compiler would take that and through multiple steps ultimately pass down to transistors. Transistors would always work in a series of 0 and 1, no mathematical formula there. Thus, classical computer is formulas up to transistors and then it is just transistors’ natural property of 0 and 1. Quantum computer is not much different. Let’s take example of chemistry. Let’s assume that we are trying to find energy of some chemical molecule, a common problem in chemistry. There are techniques like Jordan Wigner method, which converts fermionic (chemistry) form to the qubit form. There would be cleaning and optimization steps to remove non-important components from the molecular formula and properties. Finally, the qubit form is run on a quantum computer. If we assume there are twenty steps in calculating molecular energy, than nineteen of them are mathematical like cleaning, optimization, Jordan Wigner transformation and so on. Only the twentieth step is quantum computing, similar to going to the transistors in classical computing. Mathematics and software gets less focus in quantum computing, because everybody is focused on qubits. Whereas in classical computing, we don’t think about transistors anymore.

Mihir: Let’s pivot now to system architecture. What is the simplest way to define system architecture irrespective of technology?

Dr. Ravi: Entire system is made up of multiple layers known as abstraction layers. One layer is an application like zoom or software doing chemistry calculations. Second layer is algorithm that application runs on. Then you have instruction layer like instruction set architecture which runs your device. To convert algorithm to instructions, you need a compiler. You may also have an operating system that is doing resource management. Another layer is micro architecture of the computer, which is how the computer is designed. This micro architecture has components like circuits and circuits are made up of transistors for classical bits or qubits. System architecture is interactions between these different layers. Hardware architects focus on interactions between circuits, transistors and qubits like hardware components. Architects working at micro-architecture levels organize components within a processor. Other types of system architects deal with interaction between compiler and hardware, or compiler and algorithm, or stacking servers to build complex super-computing architecture. System architect is a broadly defined term for a group of experts working anywhere among different layers of hardware and software and they understand the pathway from application to technology. It is a complex pathway and system architects usually work on only a subset of different layers.

Mihir: How has the role of system architect evolved over the year?

Dr. Ravi: Yes, the role has definitely changed over the years. That change has come based on the needs. During the seventies, there were so many opportunities and needs in a single layer of the stack that a person can focus on being expert of just one layer like on micro-architecture or compiler. Early 2000s, computers started to reach limits of computational power within a single core and multi-core systems became a norm. That prompted change in the role of some system architects. They asked questions about parallelization of processes, dependencies between applications and different cores and other questions that system architects did not think about before. Because the capacity of processors was not increasing rapidly, the focus shifted to building accelerators. Again that had an impact on role of system architects. The architects needed to look at multiple layers from application to processors, but they were focusing on just one application. Earlier system architect’s role was broad within a layer or two. Modern system architect’s role has become deeper than broader.

Mihir: While systems architecture was evolving for classical computing we had opportunities to try and fail. Now that we have all these knowledge about computing, we have to use our knowledge in quantum computing. We do not have enough opportunity to try new things and fail, isn’t it?

Dr. Ravi: Again, a very good question. On one hand it has been a huge positive that we have learned to build a full stack in classical computing and we can apply that knowledge to quantum computing. For example, IBM has been at the forefront of building system architecture for classical computing; it is applying that knowledge to the quantum computing and doing very well. On the other hand some of the strategies and habits that work in classical computing may not work in quantum computing. In emerging technology you can’t start with being broadly expert in one layer like how classical computing started. We have to be flexible. As other layers are evolving, system architect in quantum computing needs more depth and flexibility in their knowledge and approach.

#qubit #quantum computing #quantum #quantum mechanics #qc #system architecture

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sneek-m · 5 months

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"Mr. Sonic" by QUBIT [9BIT, 2023]

#QUBIT #Song of the day #Youtube

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atlatszo · 5 months

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#444 #direkt36 #lakmusz #magyar hang #magyar narancs #partizán #qubit #szuverenitásvédelmi hivatal #szuverenitásvédelmi törvény #telex #válasz online

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jpopstreaming · 5 months

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🆕🎶 「 9BIT 」 new album by QUBIT is now available worldwide! 🌐 Listen now and discover new sounds from Japan on our weekly updated playlist 🎧 https://spoti.fi/3lgjH73

#QUBIT #Japanese music #Jmusic #Jpop

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ferios-favorite-styles · 2 months

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コンタクト / QUBIT

#spotify #pops #music #nowplaying #newreleases #newsong #newmusic #jpop #jmusic #音楽 #音楽好き #新譜 #音楽好きな人と繋がりたい #おすすめの音楽 #音楽のある生活 #プレイリスト #FeriosList185 #qubit

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sonofkumiko · 5 months

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QUBIT’s 9BIT Review - Better than expected

The first album to come from DAOKO’s new project, QUBIT. The album being appropriately named 9BIT (a play on words which sounds similar to the band’s name in Japanese), features 9 songs - 6 being brand new and one being an album mix.

As the first album from the band, I was pretty skeptical of the songs I’d be hearing, since the first couple of singles from them didn’t really resonate with me and felt a bit too out nowhere for me, however I’m glad to say that this album features a wide array of songs, from the energetic Mr. Sonic, to the dark distorted rumbles of Distant Dance.

This album has a lot of experimental tracks that can feature anywhere from little plucks in the bass to vocal effects being thrown everywhere throughout the song, hell, sometimes even the melodies don’t make sense - giving this album a very chaotic, erratic, and indie feel, but still working well as a commercial release.

For those who have kept up with DAOKO’s other works, this album feels a lot like a successor to the 2020 album Anima, although this album is much more cohesive and consistent with the quality and style it’s trying to portray (thankfully).

In the end I feel like this album definitely won’t be for everyone, or even people who like DAOKO’s 2014-2019 works, it’s its own thing derived from the same idea that DAOKO’s 2020 works were based on… ‘slap anything up there and see what sticks!’

Now, onto the actual song ratings…

Big Mouth 3.5/5 - it’s a nice intro song, but doesn’t do much on its own.

G.A.D - 3.5/5 - the debut song from QUBIT, it’s a bit too ‘weird’ and erratic for me. Still enjoyable but definitely gave me a very iffy first impression.

Mr. Sonic 5/5 - released as a promo single for the album and is probably the strongest song they’ve made so far. All of the chaos and sampling really work in their favor for this one, my only complaint is the outro is very abrupt.

Wonder World 3.5-4/5 - the harmonies and chord progression are very nice in this particular song, there’s not much more to say on it but it’s definitely worth a listen!

Distant Dance 4.5/5 - a very dark and grungy song with melodic moments that DAOKO really lends a hand to. Definitely one of the more standout songs from here.

Fast Life (album mix) 4/5 - this song feels less like a qubit song and more like a solo release from DAOKO. With a happy tone, heavy rapping and it being surprisingly safe with how it uses vocal sampling and unconventional methods. Not to say any of this is bad, I really enjoyed this song, and I’m glad that some of the songs here aren’t all over the place. The album mix doesn’t really add anything, more or less just rebalances some stuff - definitely more of a remaster if anything.

Room Tour Complex 4/5 - this track is either standout or bland depending on who you are. This song features glitchy vocals with the lyrics being a bit more standout than others and a very nice melody to follow. Along with some nice “oohs” during the chorus which makes this a very charming song.

Neon Diver 4/5 - a very melodic and mellow track, being a bit more minimalistic than the others. The synths have a very “poppy” feel to it with a pretty prominent and grooved bass. Her vocals feel really smooth in this song with a slightly longing melody paired with it. I feel like we could’ve gotten a bit more of a bridge for this song rather than the very short break in it but that’s my complaint.

Beautiful Days 4/5 - a very atmospheric, anthemic, and slightly psychedelic track that does a very good job leading out the album and summarizing what QUBIT actually is. A mix of different genres that lend themselves whenever and wherever. I love how upbeat this track is.

Overall album rating: 4/5 - absolute banger of an album and I love the new ideas that they’re experimenting with. Here’s hoping they keep trying new things!

Stream: https://qubit.lnk.to/9bit

Mr. Sonic official MV: https://www.youtube.com/watch?v=KQfRyJ2v5RY

Official website: https://columbia.jp/qubit/

#jpop #qubit #DAOKO #album review #mine #mr. sonic

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eannpatterson · 5 months

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Chirping while calculating probabilities

A couple of weeks ago, I visited the London headquarters of IBM in the UK and Ireland for discussions about possible areas of collaboration in research and education. At the end of our meeting, we were taken to see some of their latest developments, one of which was their Quantum System One computer. We had seen its casing, a shiny silver cylinder about half metre in diameter and a metre and…

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#cryogenic #IBM #probability #quantum computing #Qubit

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prose2passion · 7 months

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#science #nanoworld #qubit #quantum computer #atoms

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scienza-magia · 7 months

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La velocissima anomaly detection quantistica

Il computer quantistico batte ancora quello tradizionale. In Italia, per la prima volta su un tema complesso. Per la prima volta un computer quantistico ha superato un computer tradizionale nell'analisi di un problema reale come la ricerca di attacchi informatici nel traffico internet. Gli annunci fatti finora sulla supremazia quantistica, arrivati dalla Cina, riguardavano infatti dimostrazioni su piccola scala. Il risultato, che segna un nuovo passo in avanti verso la supremazia quantistica, è pubblicato sulla rivista Nature Communication Physics, si deve alla ricerca coordinata dall'Università Statale di Milano, in collaborazione con il Politecnico di Milano.

I ricercatori hanno dimostrato che utilizzando l'intelligenza artificiale su un computer quantistico si rilevano gli attacchi informatici più velocemente che con i computer tradizionali e nell'esperimento hanno utilizzato il computer canadese D-Wave Advantage da 5.000 bit quantistici (qubit) su una banca dati da 3 milioni di pacchetti di traffico internet. "Due anni fa avevamo già dimostrato un nuovo modo di programmare l'algoritmo di intelligenza artificiale quantistica sviluppato in origine dalla Nasa, chiamato macchina di Boltzmann e ispirato a quello adottato, tra gli altri, da Netflix per raccomandare i film", osserva il coordinatore della ricerca, il fisico della materia condensata Enrico Prati, dell'Università Statale di Milano. "In quel caso per - aggiunge - i risultati erano limitati a piccoli database dimostrativi, anche per le limitazioni dei computer quantistici di allora. Lo sviluppo dell'hardware è così rapido che oggi siamo in grado di trattare database realistici come quelli tipici della cybersecurity. Quello che osserviamo - dice ancora Prati - è che vi sono condizioni per le quali impiegare il computer quantistico è più rapido, anche fino a 64 volte, che non a usare un computer tradizionale". L'originalità della ricerca italiana, aggiunge il ricercatore, è nel fatto che "il metodo di apprendimento è stato applicato a database reali di traffico internet, quelli solitamente usati per addestrare i sistemi di protezione dei nostri dati da parte delle grandi aziende, come Amazon o Microsoft, e dalle istituzioni governative". I ricercatori, aggiunge, hanno addestrato una rete di neuroni quantistici e in seguito hanno esaminato ogni pacchetto di dati. "Questo tipo di analisi informatica si chiama anomaly detection perché è in grado di individuare, tra milioni di pacchetti, quelli anomali e quindi da verificare perché potenzialmente rivelatori di un attacco", osserva Lorenzo Moro, il ricercatore del Politecnico di Milano che ha programmato l'algoritmo e oggi fondatore della prima startup italiana di software quantistico QBrain. Una delle mosse vincenti a favore del computer quantistico è stata la possibilità di ottenere più copie alla volta del programma sullo stesso processore quantistico, riducendo così il tempo di esecuzione. "Abbiamo trovato un modo - dice ancora Moro - per sfruttare con maggiore efficienza l'hardware quantistico che era già disponibile". Read the full article

#anomalydetection #attacchiinformatici #computerquantistico #computertradizionale #D-WaveAdvantage #intelligenzaartificiale #macchinadiBoltzmann #QBrain #qubit #trafficoInternet

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