What they fail to mention is that the bulk of a modern quantum computer is the cooling system required to keep the quantum chip at below 1 degree Kelvin.
Quantum computers can only come out of lab environments once they can run at sensible temperatures achievable by a small cooling system.
And there's not much reason to make one "desktop size" until one of the laboratory size quantum computers actually does a useful calculation faster than a traditional computer could have.
NISQ-era QC machines are just too noisy to really be that useful without a lot of legwork, regardless of speed.
AWS has machines available right now. Translating from qiskit to braket isn't rocket science either, but no one is clamoring to use them. It is hard to convince someone to take QC seriously for at-home use we can't even guarantee 1 logical qubit.
Still, every step counts and I'm really excited to see this one.
You cannot yet get a guarantee of 1 logical gate-model qubit, no, but Amazon Braket will let you submit problems to D-Wave machines right now, with >5000 annealing qubits available.
D-Wave is seeing really promising results with optimization problems and materials simulation. The real barrier to entry right now is not access to the machines, or the middleware needed to use them - it's the ability to actually harness Binary Quadratic Model or Discrete Quadratic Model problems to apply to something that delivers real ROI.
There is a lot of training material available, but there's still a difficult road between systems that mathematicians and computational physicists can work with, and systems that are turn-key enough for a business analyst with a stats background to integrate into a reporting or planning workflow. Of course, anything in software that is a 'difficult road' is also potentially a goldmine for anyone that pulls it off.
The problem there is that heat is noise, and the balmy 300 Kelvin environment next to my workstation is a very noisy place indeed. So far, qubits seem to demonstrate the ability to actually be in a superposition of states only when we get them extremely cold so as to remove as much noise as possible from the system. This doesn't seem to be changing anytime soon, at least in the realm of superconducting qubits!
It's not actually a problem at this point, because cryogenics technology is really advanced, to some extent commoditized - if you want a fridge, you can buy one, though at D-Wave we've pushed the state of the art on these systems far past what you'd get off the shelf, to the extent where we've had production systems spend years cold with only routine maintenance.
For the foreseeable future, it makes way more sense to interact with a quantum computer remotely, the same way you interact with your Hadoop cluster or your GPU farm; you don't want to work right next to it, it's noisy, and unfortunately unlike a Cray it doesn't provide built-in seating. I should know, there are a large number of these machines a stone's throw from my desk...
Neither this article nor the article it is paraphrasing ( https://www.cambridgeindependent.co.uk/business/world-first-... ) appear to say anything about what "quantum computing" their system is doing, or why anyone would prefer to use it over a classical computer.
There's plenty of hype ("better batteries"), but no details on what it can actually do now or in the next 5 years.
Does that mean we can bring a cluster of quantum laptop to ISS and run them at the space station and attack the bitcoin blockchain? Sounds like a valuable mission.
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[ 5.7 ms ] story [ 35.1 ms ] threadQuantum computers can only come out of lab environments once they can run at sensible temperatures achievable by a small cooling system.
AWS has machines available right now. Translating from qiskit to braket isn't rocket science either, but no one is clamoring to use them. It is hard to convince someone to take QC seriously for at-home use we can't even guarantee 1 logical qubit.
Still, every step counts and I'm really excited to see this one.
D-Wave is seeing really promising results with optimization problems and materials simulation. The real barrier to entry right now is not access to the machines, or the middleware needed to use them - it's the ability to actually harness Binary Quadratic Model or Discrete Quadratic Model problems to apply to something that delivers real ROI.
There is a lot of training material available, but there's still a difficult road between systems that mathematicians and computational physicists can work with, and systems that are turn-key enough for a business analyst with a stats background to integrate into a reporting or planning workflow. Of course, anything in software that is a 'difficult road' is also potentially a goldmine for anyone that pulls it off.
It's not actually a problem at this point, because cryogenics technology is really advanced, to some extent commoditized - if you want a fridge, you can buy one, though at D-Wave we've pushed the state of the art on these systems far past what you'd get off the shelf, to the extent where we've had production systems spend years cold with only routine maintenance.
For the foreseeable future, it makes way more sense to interact with a quantum computer remotely, the same way you interact with your Hadoop cluster or your GPU farm; you don't want to work right next to it, it's noisy, and unfortunately unlike a Cray it doesn't provide built-in seating. I should know, there are a large number of these machines a stone's throw from my desk...
There's plenty of hype ("better batteries"), but no details on what it can actually do now or in the next 5 years.