QuEra Computing, a Boston-based quantum computing company, has raised $230 million in a round led by Google (GOOGL) and SoftBank (SFTBF)’s Vision Fund 2 to develop “fault-tolerant” quantum computers, designed to overcome the computational errors that have long hindered real-world quantum applications.
The company did not disclose its valuation for this round or how much Google and SoftBank individually invested. Google was also part of QuEra’s prior $47 million round in October 2024. SoftBank is a first-time investor in the company.
QuEra’s key differentiator in quantum tech lies in its use of neutral atoms, such as rubidium and strontium, for computation as qubits. Qubits are data units that can exist in multiple states simultaneously, enabling ultra-fast calculations. This makes quantum systems easier to scale while reducing error rates compared to superconducting or trapped-ion approaches, a path the company is pursuing to achieve fault-tolerant systems.
QuEra was founded in 2018 by top physicists from Harvard and MIT, including Mikhail Lukin, Vladan Vuletić, Markus Greiner and Dirk Englund. In December 2023, in collaboration with Harvard University, the company achieved a major breakthrough by running complex, error-corrected algorithms on 48 logical qubits.
“The 48-qubit milestone brings us closer to fault tolerance, indicating that neutral-atom technology may be competitive in the race toward large-scale quantum computing,” Yuval Boger, chief commercial officer of QuEra, told Observer.
IonQ, which specializes in trapped ion quantum computers, has yet to demonstrate logical qubits, Boger noted, while D-Wave Quantum, a quantum systems developer using the quantum annealing calculation approach, stands out in solving specific optimization problems but lacks versatility.
What’s special about QuEra’s fault-tolerant approach?
The current generation of quantum computers that exist in labs and limited commercial settings, known as Noisy Intermediate-Scale Quantum (NISQ) systems, are prone to errors, limiting their real-world impact. They’re also highly sensitive to environmental noise, which disrupts computations. Fault-tolerant quantum computing aims to fix these issues. QuEra’s neutral atom approach can create stable qubits for scalable fault-tolerant computing. However, such systems demand a massive number of qubits and computing power, making it a challenging engineering problem.
Recently, both Nvidia CEO Jensen Huang and Meta CEO Mark Zuckerberg expressed skepticism about the near-term practicality of quantum computing. Huang projected that “very useful” quantum systems are still 15 to 30 years away, while Zuckerberg said quantum computing is “still quite a ways off from being a truly practical paradigm.”
Boger believes that a fully large-scale, fault-tolerant system likely remains several years away, even if certain specialized or mid-scale applications emerge sooner. “True inflection point will come when several business problems of true commercial value will be solved. We are close, but not there yet,” he said.
What’s in Google and SoftBank’s quantum playbooks?
Google previously backed D-Wave in 2006 and committed $50 million toward university-led quantum research. In 2017, Google Ventures (GV), an investment firm operating independent of Google, led a $20 million Series B funding round in IonQ, which specializes in trapped ion quantum computers. However, QuEra’s neutral-atom systems represent a fundamentally different path than Google’s superconducting qubit approach.
SoftBank’s Vision Fund 2 invested in IonQ ahead of its public debut in 2021. Last month, SoftBank Corp., which runs telecom and tech services in Japan, announced a partnership with U.K.-based Quantinuum to launch a “quantum data center” for overcoming the limits of A.I.-based systems.
Both companies now appear to be broadening their quantum portfolio with QuEra, betting on fault-tolerant computing systems to unlock commercial breakthroughs in drug discovery, materials science and financial modeling. Moreover, quantum computers can also be used to generate high-quality synthetic data for A.I. models and improve analysis capabilities.
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