Episode 204 of the Data Center Go-to-Market Podcast features an interview with Yuval Boger, Chief Commercial Officer at QuEra Computing. By watching this episode, you will:

  • Distinguish what quantum computers can do today from what remains a future promise.
  • Assess how hype around capabilities and timelines can hinder commercialization.
  • Describe QuEra’s approach to qualifying impactful problems and working collaboratively with customers.
  • Explore how AI may help improve quantum algorithms and maintain quantum systems.
  • Compare the potential role, power needs, and workload applications of QPUs alongside CPUs and GPUs in data centers.
  • Identify why universities and businesses should prepare people for quantum-related work.
  • Recognize the scientific and communication skills QuEra values in commercial hires.
  • Apply lessons on using credible, research-backed content to educate potential buyers.
  • Anticipate how quantum success measures may shift from technical specifications toward cost, power, maintenance, and integration.

 

<p style="line-height: 1.5; text-align: center;">Subscribe to the Data Center GTM Briefing</p>

Host:

Joshua Feinberg, CEO
DCSMI

 

Special Guest:

Yuval Boger, Chief Commercial Officer
QuEra Computing

 

Overview

Quantum computing commercialization should prioritize realistic, outcome-focused customer journeys over hype, feature comparisons, and small proof-of-concept projects.

Market Reality

  • Yuval said current quantum computers remain limited, while QuEra expects its first truly useful computer on AWS in 2028.
  • Neutral-atom systems can use substantially less power and space than some alternatives; QuEra’s current system uses about 20 kilowatts and does not require cryogenic cooling.
  • AI is helping optimize quantum algorithms and maintain systems, while quantum computing may expand datasets for AI applications such as chemistry and drug discovery.

Commercial Strategy

  • QuEra focuses on expert organizations with meaningful business problems, scientific maturity, and a committed business sponsor.
  • The company sells a collaborative journey, translates business problems into quantum approaches, and turns down projects that do not fit current capabilities.
  • Scientific, peer-reviewed results and specific claims are favored over generic educational content or AI-generated material.

 

Outline

Commercial Hurdles

  • Quantum computing is moving from research toward commercialization, but unrealistic timelines and capability claims are slowing trust.
  • Yuval emphasized a “more science, less fiction” positioning and the need to validate whether customer problems are feasible.

Education And Workforce

  • Universities and enterprises should begin training people to code, install, service, and work with quantum systems.
  • QuEra uses a comic book, podcasts, webinars, and customer perspectives to explain quantum concepts to non-physicists.

AI And Infrastructure

  • Quantum processing units may work alongside CPUs and GPUs in high-performance computing centers.
  • AI supports quantum algorithm development and system maintenance; quantum systems may contribute to AI through expanded scientific datasets.
  • Neutral-atom systems can reduce infrastructure requirements compared with cryogenic approaches.

Go-To-Market Model

  • QuEra prioritizes impactful problems, collaborative co-design, and measurable customer outcomes rather than qubit counts alone.
  • Strong commercial hires combine an exact-science background, curiosity, listening skills, and the ability to translate between business needs and quantum capabilities.

Buyer And Content Strategy

  • Ideal customers have quantum expertise, a business sponsor, and a reason tied to future business impact rather than public positioning.
  • Peer-reviewed research, demonstrated results, and honest limitations are more credible than generic content.
  • QuEra expects commercial metrics to shift toward power, space, cost, maintenance, usability, integration, and partnerships.

 

Main Considerations

Quantum computing and commercialization

  • Quantum is promising, but not yet broadly useful. Boger says current quantum computers are limited and generally cannot do much that classical supercomputers cannot. QuEra expects its first “truly useful” computer to be available on AWS in 2028.
  • Hype is a commercial obstacle. Unrealistic claims about quantum capabilities and timelines can lead to disappointment. QuEra’s approach is “more science, less fiction”: communicate limits honestly and support claims with peer-reviewed research and demonstrations.
  • Sell the problem-solving journey, not the machine. QuEra prioritizes meaningful business problems over small proof-of-concepts, assesses feasibility candidly, and works collaboratively with customers so they learn how to think about quantum computing.
  • Business value should eventually matter more than raw technical specs. As the market matures, success will be judged increasingly by cost, power, space, maintenance, programming, and integration with classical computers—not just qubit counts and error rates.

Education

  • It is not too early for universities and businesses to prepare people for quantum computing. The workforce will need skills in coding, installation, servicing, and customer work.
  • QuEra combines broad public education—such as Boger’s Quantum Bits comic book—with a commercial focus on organizations that already have quantum expertise and clear use cases.
  • For commercial roles, QuEra values scientific backgrounds, curiosity, listening, and the ability to translate between business needs and quantum capabilities.

Data-center workloads

  • Boger sees quantum processing units (QPUs) as potential additions alongside CPUs and GPUs—not replacements for classical computers. Quantum systems may address specialized problems, while classical systems remain essential.
  • QuEra describes its systems as comparatively compact and power-efficient: the current system uses about 20 kW, and a next generation might use around 40 kW. Boger suggests that moving suitable workloads to quantum could reduce energy use, depending on the workload and system.
  • Potential applications include molecular analysis for drug discovery, EV-battery research, and optimization, alongside the possibility of accelerating some existing workloads.

Guest Resources

Resources



Do you want to stay up to date about upcoming episodes?

Subscribe to the Data Center GTM Briefing 

Subscribe to the Data Center GTM Briefing

 

 
Data Center Go-to-Market Podcast, Data Center Go-to-Market Podcast | Guest Interview
Like it? Share it:

You may also like