When we talk about quantum computing, there is always the focus on what the ‘quantum’ part of the solution is. Alongside those qubits is often a set of control circuitry, and classical computing power to help make sense of what the quantum bit does – in this instance, classical computing is our typical day-to-day x86 or Arm or others with ones and zeros, rather than the wave functions of quantum computing. Of course, the drive for working quantum computers has been a tough slog, and to be honest, I’m not 100% convinced it’s going to happen, but that doesn’t mean that companies in the industry aren’t working together for a solution. In this instance, we recently spoke with a quantum computing company called Rigetti, who are working with Ampere Computing who make Arm-based cloud processors called Altra, who are planning to introduce a hybrid quantum/classical solution for the cloud in 2023.

It’s All About the Qubits

The striking thing about quantum computing has always been the extravagant hardware required – a ‘golden steampunk chandelier’ of tubes and cables all required to bring the temperature of the hardware down hundredths of a degree above absolute zero. This minimizes thermal effects on the elements of a quantum computer, known as the qubit. Depending on the type of qubit involved, those cables can carry microwave signals, and how the chandelier is constructed often determines how many qubits are involved.

Qubits are the quantum computational power, and the more you have (in theory) the more exponentially more computing power there is on tap. However, because quantum computing doesn’t deal in absolutes, sometimes those qubits are used for resiliency, which is needed at such extreme environments. You’ll find that quantum computers list an ‘effective’ number of qubits equivalent to the computational power, rather than the actual physical number present. Beyond that, there are different types of Qubits.

Transmon qubits rely on superconducting electron pairs being controlled inside a three-dimensional cavity. A spin qubit controls individual electron spins with magnetic fields. Most companies use Transmon qubits (Google, IBM, Rigetti), whereas Intel dropped its Transmon development in favour of spin qubits. Exactly how many qubits a system needs to do ‘useful’ work is a hot topic in the literature, although Google claims it has performed computation impossible on classical computing with only 53 physical Transmon qubits – again, another hot topic for debate.

The ultimate goal of quantum computing is to enable computing resources that can solve classical problems whose compute requirements are impossible within reasonable time frames. The typical example is Shor’s Algorithm, to find prime factors of number (essentially solving the underlying basis for cryptography that should take millions of years) in seconds. Another example is solving a typically quantum-like system, such as chemistry and biochemical interactions. Also optimization, going beyond typical ‘traveling salesman’ into machine learning – the idea is that quantum computing can assist training or inference to check all possible answers, simultaneously.

Quantum computing has always been seen as a future horizon of where high-performance should go. However, it is one of those elements that always seems 10-20 years away. In the early 2000s it was seen as 10-20 years away, and the same is true today. However there are now more startups and funded ventures willing to put in more research to get these systems up and running. One of those is Rigetti, and today is an announcement of a collaboration with Ampere Computing.

Put The Quantum In The Cloud, Ampere Plus Rigetti

For the last few years, there has been a focus in putting high-performance computational resources within reach of everyone. The offering of cloud computing, web services, and 1000s of processors at your fingertips has never been more real, or been more easy. With enough money in your bucket, the cloud providers make it easy to spin up resources for storage, networking, services, or compute. Cloud computing like this is designed to scale as and when you need it. Rigetti wants to do the same with quantum computing.

Rigetti Computing, founded in 2013, is a series C funded quantum computing startup with a public $200m investment to date. Late last year, it announced the start of its new scalable quantum computing infrastructure – with a chip containing 40 transmon-style qubits, multiple chips can be embedded onto a single package for a single quantum computing chandelier. The goal of these designs is to accelerate machine learning, both for quantum compute and classical compute, and as a result, they’re partnering with Ampere Computing which makes the Altra Max Arm-based CPUs.

The goal of the partnership is to provide a cloud-native solution combining both classical and quantum computing. Spinning up an instance would include some qubits and some cores, allowing customers to use standard machine learning APIs that would be naturally split across the two types of hardware. In this heterogeneous combination, the goal is to take advantage of the quantum system to do what it does best, and then leverage the traditional compute resources with the Altra Max CPUs for machine learning scale out.

Rigetti says that its solution will scale to hundreds of qubits, while Ampere resources can scale as naturally as most compute can. Rigetti chose Ampere as a partner in this instance because of what the company can provide – Ampere always states that its processors are cloud-native, or built for the cloud, and that its 128-core chip can provide 1024 cores in a traditional 2U server with Arm Neoverse N1 performance.

At this point of the partnership, Rigetti and Ampere are at work developing a combination system up and running. Right now, the Ampere CPUs are to be part of the coupled performance resource, although Rigetti says that there could be a time where Ampere’s hardware might replace the FPGAs in the control units of the quantum system itself. The partnership aims to start working on a proof of concept, creating a local-to-Rigetti example of a cloud-native hybrid quantum/classical infrastructure, and creating a software stack optimized for machine learning. Rigetti says that it is already working with customers interested in the co-design to give itself targets for software optimizations.

The timeline for the rollout is still early, with a proof-of-concept planned over the next few months, then deployment with tier 1 cloud partners through 2023. The idea is to initially work with key customers to help optimize their workflows to combine with the hardware. Then it’s simply a case of scale out – more qubits for quantum, more CPUs for classical. Ampere is set to launch Siryn this year, its own custom Arm core built on next generation process node technology, and we were told that the scope is to bring in future Ampere generations as they are developed.

Rigetti says that it has made strides in enabling transmon qubits viable at scale. Intel dropped its transmon qubit program because it didn’t think it could scale, but also because they could create spin qubits fairly easily (however, control is a different part of that story). Rigetti plans to scale to the hundreds of qubits, allowing cloud customers to take a chunk of however many qubits they need at the time. One issue I brought up with them is synchronicity, and it sounds like they have a system that, in a traditional sense, can be asynchronous to scale. Rigetti believes there are elements to machine learning, both training and inference, that will scale with qubit count in this way.

Is Quantum Computing still a distant hope? The promise here is a hybrid product, with quantum and classical resources, for cloud customers in 2023. I fully expect that to be a viable use case. However, as is always the question with quantum computing – what problem is it solving, and is it better than classical?

Comments Locked


View All Comments

  • Yojimbo - Friday, February 18, 2022 - link

    No offense to Anandtech, but buyers of servers or cloud compute don't care much about Anandtech's server reviews. Anandtech, for example, has no good idea what AWS is looking for or even much idea of what AWS customers are looking for. AWS does.

    Also, why are we comparing the Altra Max to the Graviton2? The Graviton2 was open in AWS for user access in early 2020. It looks like the Altra Max started showing up in the wild in Q3 2021. Amazon started allowing preview access to its Graviton3 instances in Q4 2021, just a couple months after Altra Max started showing up. Can I yet find Altra Max public cloud instances now in Q1 2022? Oracle and Equinix have 80-core Altras from what I see and nothing with higher core counts. I don't see anything to suggest that Cloudflare is yet using Altra Max in its services.
  • Wilco1 - Friday, February 18, 2022 - link

    Who exactly reads AnandTech is irrelevant. Look back, your claim was that Ampere has absolutely nothing to offer and that you can't differentiate using standard Arm cores anyway. My link proves you are wrong on both counts - Altra (Max) are able to beat all of the x86 competition, and they do far better than Graviton 2. AWS aims for something different of course, which is why I made the comparison - major differentiation despite using the same standard core.

    We're still in the middle of a chip crunch, so things move a bit slower than usual. Either way Altra Max (and likely Altra) will outperform the upcoming Graviton 3 despite being older and not getting the benefit of 5nm and DDR5. They'll likely announce their next generation soon. So claiming Ampere has absolutely zero to offer both now and in the future is just stupid.
  • Yojimbo - Friday, February 18, 2022 - link

    No, Wilco, you missed my point. It's not who reads it, it's the relevance of the tests. Servers are about platforms and roadmaps, not about core performance. And the only core performance that does matter is the performance on the applications customers are running, not on benchmarks.
    Altra Max will not outperform Graviton3.

    Just count the days until Ampere is bought out, who buys it, and how much is paid.
  • Wilco1 - Saturday, February 19, 2022 - link

    First you ask for benchmarks, then you don't want to look at benchmarks because you don't like who is winning them. Years ago when Intel was winning the SPEC benchmarks everybody was claiming how it was the best benchmark for servers. Today Intel is no longer winning on SPEC and suddenly everybody hates it. Go figure.
  • mode_13h - Monday, February 21, 2022 - link

    > Altra Max beats everybody else on the market - not only Intel/AMD's fastest servers,
    > but it looks like it should still outperform Graviton 3 despite being on an older process
    > and using much older cores.

    Graviton 3 is a 100 W CPU. Altra Max isn't. If Amazon wanted to push for more performance, I'm sure they could've spec'd a higher power envelope. And yet, Graviton 3 still features DDR5 and PCIe 5.
  • Wilco1 - Friday, February 25, 2022 - link

    AWS aim for low power, low cost and high density rather than best performance like Ampere. Ampere's next-gen will also use 5nm and DDR5, so performance and efficiency should improve.
  • mode_13h - Monday, February 21, 2022 - link

    > don't see what Ampere really offers anyone since they abandoned their own chips designs

    They didn't, really. It turns out that Altra was something of a stop-gap measure, to buy time until their Siryn CPUs launch, later this year. It was mentioned in this article, but had a separate announcement:


    Also, it turns out that Ampere managed to scale up N1 greater than ARM had intended, through a few tricks they pulled. N1 was only designed to scale to 64 nodes. So, even in Altra, Ampere had a certain value-add.
  • Tilmitt - Wednesday, February 16, 2022 - link

    Is this pseudo-science?
  • GeoffreyA - Thursday, February 17, 2022 - link

    What they're implementing isn't certain, but the physics is quite real, as far as real goes in this universe.
  • mode_13h - Monday, February 21, 2022 - link

    I think they like machine learning, as an application, because it's more tolerant of errors and approximate solutions.

Log in

Don't have an account? Sign up now