The future of quantum computing in the cloud
May 18, 2021
Developers use quantum computing to encode problems as qubits, which compute multiple combinations of variables at once rather than exploring each possibility discreetly. In theory, this could allow researchers to quickly solve problems involving different combinations of variables, such as breaking encryption keys, testing the properties of different chemical compounds or simulating different business models. Researchers have begun to demonstrate real-world examples of how these early quantum computers could be put to use.
However, this technology is still being developed, so experts caution that it could take more than a decade for quantum computing to deliver practical value. In the meantime, there are a few cloud services, such as Amazon Bracket and Microsoft Quantum, that aim to get developers up to speed on writing quantum applications.
Quantum computing in the cloud has the potential to disrupt industries in a similar way as other emerging technologies, such as AI and machine learning. But quantum computing is still being established in university classrooms and career paths. Similarly, major cloud providers are focusing primarily on education at this early stage.
“The cloud services today are aimed at preparing the industry for the soon-to-arrive day when quantum computers will begin being useful,” said David Moche, Founder and CEO of Qfinity labs.
There’s still much to iron out regarding quantum computing and the cloud, but the two technologies appear to be a logical fit, for now.
How quantum computing fits into the cloud model
Cloud-based quantum computing is more difficult to pull off than AI, so the ramp up will be slower and the learning curve steeper. For starters, quantum computers require highly specialized room conditions that are dramatically different from how cloud providers build and operate their existing data centers.
We believe practical quantum computers are at least a decade away. The biggest drawback lies in aligning the quantum state of qubits in the computer with a given problem, especially since quantum computers still haven’t been proven to solve problems better than traditional computers.
Coders also must learn new math and logic skills to utilize quantum computing. This makes it hard for them since they can’t apply traditional digital programming techniques. IT teams need to develop specialized skills to understand how to apply quantum computing in the cloud so they can fine tune the algorithms, as well as the hardware, to make this technology work.
Current limitations aside, the cloud is an ideal way to consume quantum computing, because quantum computing has low I/O but deep computation. Because cloud vendors have the technological resources and a large pool of users, they will inevitably be some of the first quantum-as-a-service providers and will look for ways to provide the best software development and deployment stacks.
Quantum computing could even supplement general compute and AI services cloud providers currently offer. In that scenario, the cloud would integrate with classical computing cloud resources in a co-processing environment.
Simulate and access quantum with cloud computing
The cloud plays two key roles in quantum computing today. Some companies provide an application development and test environment for developers to simulate the use of quantum computers through standard computing resources.
The second is to offer access to the few quantum computers that are currently available, in the way mainframe leasing was common a generation ago. This improves the financial viability of quantum computing, since multiple users can increase machine utilization.
It takes significant computing power to simulate quantum algorithm behavior from a development and testing perspective. For the most part, cloud vendors want to provide an environment to develop quantum algorithms before loading these quantum applications onto dedicated hardware from other providers, which can be quite expensive.
However, classical simulations of quantum algorithms that use large numbers of qubits are not practical. The issue is that the size of the classical computer needed will grow exponentially with the number of qubits in the machine. So, a classical simulation of a 50-qubit quantum computer would require a classical computer with roughly 1 petabyte of memory. This requirement will double with every additional qubit.
But classical simulations for problems using a smaller number of qubits are useful both as a tool to teach quantum algorithms to students and also for quantum software engineers to test and debug algorithms with “toy models” for their problem. Once they debug their software, they should be able to scale it up to solve larger problems on a real quantum computer.
In terms of putting quantum computing to use, organizations can currently use it to support last-mile optimization, encryption and other computationally challenging issues. This technology could also aid teams across logistics, cybersecurity, predictive equipment maintenance, weather predictions and more. Researchers can explore multiple combinations of variables in these kinds of problems simultaneously, whereas a traditional computer needs to compute each combination separately.
However, there are some drawbacks to quantum computing in the cloud. Developers should proceed cautiously when experimenting with applications that involve sensitive data. To address this, many organizations prefer to install quantum hardware in their own facilities despite the operational hassles.
Also, a machine may not be immediately available when a quantum developer wants to submit a job through quantum services on the public cloud. The machines will have job queues and sometimes there may be several jobs ahead of you when you want to run your own job. Some of the vendors have implemented a reservation capability so a user can book a quantum computer for a set time period to eliminate this problem.
Still testing the quantum filaments
Researchers are pursuing a variety of approaches to quantum computing — using electrons, ions or photons — and it’s not yet clear which approaches will pan out for practical applications first.
Nobody knows which approach is best, or which materials are best. We’re at the Edison light bulb filament stage, where Edison reportedly tested thousands of ways to make a carbon filament until he got to one that lasted 1,500 hours”. In the meantime, recent cloud offerings promise to enable developers to start experimenting with these different approaches to get a taste of what’s to come.