Company offers quantum computing systems for datacenters

IonQ, a company operating in the field of quantum computing, recently announced two new rack-mounted systems aimed at existing infrastructures, such as datacenters, that want to integrate quantum resources into their facilities.

According to the company, with the IonQ Forte Enterprise and IonQ Tempo solutions, datacenters will be able to exploit quantum potential more easily and affordably, integrating its functionalities into core workflows and business processes.

IonQ Forte Enterprise is suitable for complex computational problems such as process optimisation, quantum machine learning, correlation analysis and pattern recognition, and its performance level is #AQ 35. With today’s announcement, IonQ is offering this computing power in a compact format that can be easily deployed in existing datacenter infrastructures.

IonQ Tempo, on the other hand, is aimed at even more demanding use cases, offering an #AQ 64 system. According to the company, it can far exceed what is simulated with classic computers and GPUs and provide a computational space 536 million times larger even than IonQ Forte Enterprise.

Systems #AQ 35 are capable of considering more than 34 billion different possibilities simultaneously, and systems #AQ 64 can consider more than 18 quintillion different possibilities at the same time.

“Current quantum resources are generally limited by system accessibility and inaccuracy at scale. With the Forte Enterprise and Tempo solutions, IonQ is signalling to our partners that quantum technology can work hand in hand with existing hardware in datacenters and guarantee commercial advantages within a two-year timeframe. We are rapidly approaching the point where quantum computers will be standard as a tool for solving the world’s most challenging problems,” says Peter Chapman, CEO and president of IonQ.

Recently, the company IonQ announced contracts to sell future systems to QuantumBasel, based in Switzerland, with the aim of establishing a European quantum datacenter with two systems, one with #AQ 35 capacity and the other with #AQ 64 capacity. With this environment, IonQ and QuantumBasel intend to create new applications in areas such as logistics, finance, pharmaceuticals, chemistry and Artificial Intelligence (AI).

He also announced research projects and results with several partners worldwide, including the development of the world’s first cognition models running on quantum hardware, which could open up improved decision-making methods that mimic human thinking.

Together with the Fidelity Centre for Applied Technology (FCAT), IonQ has also laid the foundations for solving Monte Carlo simulations, which use randomness to simulate the results of complex problems. Financial institutions use Monte Carlo algorithms, for example, to understand the relationship between an outcome and multiple variables in complex systems, but their accuracy is often limited by the time needed to run the same algorithm repeatedly with different values of the variables.

At the beginning of the year, IonQ also acquired operating assets from Entangled Networks, a Toronto-based company focused on distributed quantum processor computing. The initiative is an important step towards future quantum computers being built from multiple processors and also networked. But unlike classical networks, quantum computers allow entanglement between cores to form a single, larger quantum computer.

What is #AQ?

You often hear that the power of a given quantum computer has to do with the number of physical qubits, but IonQ argues that the number of “useful qubits” or algorithmic qubits is the more appropriate metric. In his blog, Peter Chapman, president and CEO of IonQ, makes a comparison: “who would buy a computer today based on the number of transistors?”.

#AQ (Algorithmic Qubits) is a benchmarking model proposed by IonQ based on applications that takes into account the performance of computers running six quantum algorithms that use the most promising short-term use cases: optimisation, quantum simulation and quantum machine learning.

Unlike the benchmarking systems used with classical computers that use speed as a reference, #AQ seeks to determine the size of a programme (circuit) that could be executed on a quantum computer, i.e. its usefulness.

The use cases adopted by #AQ are applicable to sectors of activity such as finance, pharmaceuticals, chemical materials, energy and logistics. The #AQ benchmarking information is summarised in a single graph, with data on the performance of a given system in a specific class of algorithms.