SemiQon Aims for Trillion Gate Operations as UK Sets Goal

SemiQon’s focus on qubit gate speed and operation aligns with a newly articulated goal within the UK national quantum strategy: achieving a trillion gate operations for computation, the company says. This emphasis on computational volume, rather than qubit fidelity metrics, validates the company’s approach to quantum processor development.

“At SemiQon, we have been saying this since we began our journey: It is all about qubit performance at scale,” a company representative stated. The UK is also examining Finland’s early investment in publicly funded cleanroom infrastructure, like Micronova and the planned Kvanttinova, as a model for reducing development costs and supporting quantum hardware companies.

SemiQon Targets Trillion Gate Operations for Quantum Computation

SemiQon’s ambition extends beyond achieving high performance in a limited number of qubits. The company aims to deliver processors capable of handling the scale necessary for practical computation. To support hardware development, policymakers are now considering a model similar to Finland’s investment in publicly funded cleanroom infrastructure, providing shared access to costly resources and reducing the capital burden on companies like SemiQon. High-quality metrology is also recognized as essential for advancing quantum technologies, as highlighted by Sir Peter Knight during recent presentations.

In the global quantum race, Finland is showing up
In the global quantum race, Finland is showing up by doing more with less — Source: semiqon.com

SemiQon has already taken steps to address this need by establishing its own laboratory within the Finnish Metrology institute MIKES, part of the family, to ensure rigorous testing and characterization of its processors. “We have to deliver more with less. Sounds ambitious and challenging but that is what makes us stand apart in the global quantum race,” a company representative stated, emphasizing the need for efficient, high-throughput quantum processing.

Finland’s Micronova & Kvanttinova Enable Scalable Quantum Hardware Development

Micronova and the forthcoming Kvanttinova facilities demonstrate Finland’s early success in using public investment to create cleanroom microfabrication infrastructure, benefiting multiple startups and companies within the nation’s quantum ecosystem. This approach minimizes capital investment burdens for smaller hardware companies, a model now attracting attention from policymakers in the United Kingdom seeking to replicate similar facilities.

The UK is currently second globally in percentage of GDP allocated to quantum technologies, a level of investment Finland, with its smaller scale, cannot currently match. Finland’s strategy centers on maximizing output with limited resources, a philosophy reflected in the company’s pursuit of high qubit performance at scale. High-precision metrology is central to improving measurement accuracy, and SemiQon intends to use the deep expertise available at MIKES, the Finnish Metrology Institute, to refine its processes.

Other European nations are already actively engaging with the UK’s quantum technology sector, and Finland recognizes the importance of establishing similar partnerships. The company hopes these strengthened ties will allow both nations to accelerate progress in the field, and deliver more with less.

It is all about qubit performance at scale. The first aspect that was gratifying is one of the objectives set in the UK national quantum strategy. The goal is to reach a trillion gate operations – not T1, T2 or qubit gate fidelity but it is about gate operations, which form the basis for computation.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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