Twist Promises Easier Quantum Programming

February 5, 2022 by Al Williams 17 Comments

We keep trying to learn more about quantum computers. But the truth is, the way we program quantum computers — or their simulators — today will probably not have much in common with how we program them in the future. Think about it. Programming your PC is nothing like programming the ENIAC. So we expect we’ll see more and more abstractions over the “bare metal” quantum computer. The latest of these is Twist, from MIT.

According to the paper (and the video, below), Twist expresses entangled data and processes in a way that traditional programmers can understand. The key concept is known as “purity” of expressions which helps the compiler determine if data is actually entangled with another piece of data or if any potential entanglement is extraneous. A pure expression only depends on qubits it owns, while a mixed expression may use qubits owned by other expressions.

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Arduino Meets Quantum Computer

January 19, 2022 by Al Williams 8 Comments

Quantum computers aren’t quite ready for the home lab, but since there are ways to connect to some over the Internet, you can experiment with them more easily than you might think. [Norbert] decided to interface a giant quantum computer to an ordinary Arduino. Why? Well, that isn’t necessarily clear, but then again, why not? He explains basic quantum computing and shows his setup in the video below.

Using the IBM quantum computer and the open source Qiskit makes it relatively easy, with the Python code he’s using on the PC acting as a link between the Arduino and the IBM computer. Of course, you can also use simulation instead of using the real hardware, and for such a simple project it probably doesn’t matter.

Granted, the demo is pretty trivial, lighting an LED with the state of qubit. But the technique might be useful if you wanted to, say, gather information from the real world into a quantum computer. You have to start somewhere.

We’ve looked at quantum computers before. They tell us it is the next big thing, so we want to be prepared. Qiskit is one of several options available today to make it easier.

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Scientific Honesty And Quantum Computing’s Latest Theoretical Hurdle

November 11, 2021 by Al Williams 25 Comments

Quantum computers are really in their infancy. If you created a few logic gates with tubes back in the 1930s, it would be difficult to predict all the ways we would use computers today. However, you could probably guess where at least some of the problems would lie in the future. One of the things we are pretty sure will limit quantum computer development is error correction.

As far as we know, every quantum qubit we’ve come up with so far is very fragile and prone to random errors. That’s why every practical design today incorporates some sort of QEC — quantum error correction. Of course, error correction isn’t news. We use it all the time on unreliable storage media or communication channels and high-reliability memory. The problem is, you can’t directly clone a qubit (a quantum bit), so it is hard to use traditional error correction techniques with qubits.

After all, the whole point to a qubit is we don’t measure it until the end of the computation which, like Schrödinger’s cat, seals its fate. So if you were to “read” a bunch of qubits to form a checksum or a CRC, you’d destroy their quantum nature in the process making your computer not very useful. You can’t even copy a bit to use something like triple redundancy, either. There seems to be no way to practically duplicate a qubit.

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New Silq Programming Language Aims To Make Quantum Programming Easier

June 22, 2020 by Maya Posch 18 Comments

Fresh from ETH Zurich comes the new Silq programming language. They also have submitted a paper to the PLDI 2020 conference on why they feel that it is the best quantum programming language so far. Although it may be not common knowledge, the lack of usable general purpose quantum computers has not kept multiple teams from developing programming languages for such computer systems.

Microsoft’s Q# is a strong contender in this space, along with the older QCL language. The claims by the Silq team on exactly why their language is better appear to come down to it being ‘more high level’, and by supporting automatic (and safe) uncomputation. While the ‘high level’ aspect is suspect since Q# is most decidedly a high-level programming language, their uncomputation claim does at least have some merit.

Uncomputation is a concept in quantum programming, where one occasionally has to remove a few intermediate objects from the current state because they may cause quantum interference that would affect the resulting output. Normally, one would save the intermediate result to a register for this, then reset the state and continue. Which parts of the state to keep and what to uncompute is however not easily determined, as a quick glance at related answers over at the Quantum Computing StackExchange and Theoretical Computer Science might reveal.

The main question thus appears the validity of this claim about Silq being able to automatically determine what ‘garbage’ can be safely uncomputed, and what should be part of the quantum interference. We have all seen with languages like Java and C# how even with traditional computing something as simple as garbage collecting can go horribly wrong. Maybe we shouldn’t count our quantum chickens yet until this particular waveform has fully collapsed.

(Thanks to Qes)

Learn Quantum Computing With Spaced Repetition

June 19, 2020 by Al Williams 2 Comments

Everyone learns differently, but cognitive research shows that you tend to remember things better if you use spaced repetition. That is, you learn something, then after a period, you are tested. If you still remember, you get tested again later with a longer interval between tests. If you get it wrong, you get tested earlier. That’s the idea behind [Andy Matuschak ‘s]and [Michael Nielsen’s] quantum computing tutorial. You answer questions embedded in the text. You answer to yourself, so there’s no scoring. However, once you click to reveal the answer, you report if you got the answer correct or not, and the system schedules you for retest based on your report.

Does it work? We don’t know, but we have heard that spaced repetition is good for learning languages, among other things. We suspect that like most learning methods, it works better for some people than others.

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Quantum Computing And The End Of Encryption

June 11, 2020 by Maya Posch 58 Comments

Quantum computers stand a good chance of changing the face computing, and that goes double for encryption. For encryption methods that rely on the fact that brute-forcing the key takes too long with classical computers, quantum computing seems like its logical nemesis.

For instance, the mathematical problem that lies at the heart of RSA and other public-key encryption schemes is factoring a product of two prime numbers. Searching for the right pair using classical methods takes approximately forever, but Shor’s algorithm can be used on a suitable quantum computer to do the required factorization of integers in almost no time.

When quantum computers become capable enough, the threat to a lot of our encrypted communication is a real one. If one can no longer rely on simply making the brute-forcing of a decryption computationally heavy, all of today’s public-key encryption algorithms are essentially useless. This is the doomsday scenario, but how close are we to this actually happening, and what can be done?

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Crunching Giant Data From The Large Hadron Collider

May 14, 2020 by Moritz v. Sivers 19 Comments

Modern physics experiments are often complex, ambitious, and costly. The times where scientific progress could be made by conducting a small tabletop experiment in your lab are mostly over. Especially, in fields like astrophysics or particle physics, you need huge telescopes, expensive satellite missions, or giant colliders run by international collaborations with hundreds or thousands of participants. To drive this point home: the largest machine ever built by humankind is the Large Hadron Collider (LHC). You won’t be surprised to hear that even just managing the data it produces is a super-sized task.

Since its start in 2008, the LHC at CERN has received several upgrades to stay at the cutting edge of technology. Currently, the machine is in its second long shutdown and being prepared to restart in May 2021. One of the improvements of Run 3 will be to deliver particle collisions at a higher rate, quantified by the so-called luminosity. This enables experiments to gather more statistics and to better study rare processes. At the end of 2024, the LHC will be upgraded to the High-Luminosity LHC which will deliver an increased luminosity by up to a factor of 10 beyond the LHC’s original design value.

Currently, the major experiments ALICE, ATLAS, CMS, and LHCb are preparing themselves to cope with the expected data rates in the range of Terabytes per second. It is a perfect time to look into more detail at the data acquisition, storage, and analysis of modern high-energy physics experiments. Continue reading “Crunching Giant Data From The Large Hadron Collider” →