Dell Technologies today released a hybrid classical-quantum computing package that offers a quantum emulation platform and access to IonQ’s quantum computers can enable both on-premise and cloud-based quantum acceleration. The package was released as part of this week's SC22 supercomputing event.

The tech giant had been offering the platform to “very few numbers” of customers for collaboration and experimentation, Ken Durazzo, VP of the Dell Research Office, told SDxCentral. The platform will now be broadly available.

The hybrid package includes two components: a quantum emulation platform built on Dell EMC PowerEdge R750xa servers, its own software, and IBM’s open-source Qiskit Runtime containerized service for quantum computers; and a hybrid classical-quantum platform using Dell EMC PowerEdge R750xa servers paired with IonQ’s simulation engine, quantum processing unit (QPU), Aria software, and its cloud-based quantum computing capabilities.

IonQ is a quantum computing vendor developing a general-purpose trapped ion quantum computer and software. It supports almost all the major public cloud providers, libraries, and tools including Amazon Web Services (AWS)’ Amazon Braket, Microsoft Azure Quantum, and Google Cloud quantum services.

The Dell and IonQ partnership “supports a customer's journey through learning experimentation, proof of concept, individual privatization on quantum systems,” Durazzo said.

Dell started its quantum computing journey in 2016, working with partners including IonQ and IBM to integrate quantum computing into existing classical computational infrastructure. This approach differed from other tech giants such as IBM, Google, and Microsoft that chose to develop their own quantum hardware and software.

Quantum computing is still an emerging market and the industry is not yet at the end state, Durazzo noted. “We asked ourselves a very simple question, which is — is quantum the end state of computing, or is quantum an accelerator for certain types of computing?”

“It's become very clear to us that applications will run on the classical side and the algorithms will be optimized inside of the QPUs,” he added. “And so if that continues as we move toward fault-tolerant quantum, there is a definite role for both classical and quantum infrastructure to work together to solve the most demanding challenges that are coming out of some industry.”

“There's likely to be a lot of different moving parts, specifically on the quantum side of the house that is likely to change,” Durazzo said, referring to the reasons behind Dell’s quantum choices.

The still maturing industry is also why Dell offers a hybrid approach of on-premise and cloud-based quantum computing. “Public clouds are a great way to outreach quite a few customers in a shared access type of model until we reach that level of scale where we can build a lot of quantum machines to have on-premises,” he said.

IonQ CTO Jungsang Kim echoed that the development environment is inherently hybrid, and the company can offer cloud-based quantum computing for scalability and on-premises-based for customized solutions.

Dell claims its classical-quantum computing offerings can accelerate chemistry and materials simulation, machine learning, and natural language processing.

Dell’s solution is to “provide scalable and cost-effective learning and early experimentation for customers in the world of quantum and then as they're ready to move toward proof of concept for a very specific type of algorithm or application and then later to productize, we have a way to support that no matter which way that their business needs are either on-premise or cloud-based," Durazzo said. “[The] industry is still in a very nascent type of state and so a lot of learning and education and experimentation is necessary in order for customers to take advantage of quantum leadership as the industry continues to mature."

Durazzo and Kim also highlighted quantum machine learning and optimization use cases.

“We're ahead of the curve in [customers’] learning process and their internal development problem solution process” in machine learning, chemistry, and materials simulation, and also for optimization issues, Kim said.