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IQM Quantum Computers launches long-term incentive plan for staff

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September 30, 2026 by Ivy Delaney

IQM Quantum Computers Plc will establish three four-year performance periods spanning 2026 to 2032, signaling a sustained commitment to employee incentives and long-term growth. The company’s new Performance Share Plan prioritizes Absolute Total Shareholder Return (aTSR) as its primary metric for the initial 2026-2030 period, adjusted by a Relative Total Shareholder Return (rTSR) of +/- 20 percent. Rewards from the plan, delivered free of charge within six months of each period’s conclusion, could total approximately 1,996,000 shares if maximum targets are met, according to the company.

Performance Share Plan 2026-2032 Targets Absolute Total Shareholder Return

IQM Quantum Computers Plc has established a performance-based incentive structure that ties employee rewards directly to company valuation, with a target of up to 1,996,000 shares allocated for the first four-year period ending June 2030. This dual-metric approach indicates a focus on both overall growth and competitive performance against industry peers, providing a detailed assessment of shareholder value creation.

The estimated aggregate gross value of the plan is approximately EUR 18.2 million, calculated using the closing share price of EUR 9.11 on September 29, 2026, though the final value will fluctuate based on share price and performance against targets. The target group for the first performance period includes all employees, encompassing the Chief Executive Officer and members of the Leadership Team, demonstrating a broad-based commitment to aligning employee interests with shareholder returns.

Alongside the Performance Share Plan, IQM has also established a Restricted Share Unit (RSU) Pool 2026-2030, intended as a broader remuneration and commitment program for all group employees, the company says. The RSU allows the Board of Directors to allocate rewards until June 30, 2030, providing flexibility in recognizing and retaining talent and addressing specific needs like attracting new personnel. Rewards from the RSU will vest over individual schedules determined by the Board, but will not begin vesting for at least twelve months and will be fully vested by the end of 2031.

Both the Performance Share Plan and RSU will distribute rewards as a combination of company shares and cash. Participants who leave the company before the end of their respective performance or vesting periods will generally forfeit their rewards, reinforcing the long-term commitment expected from employees.

The structure of these plans reflects a deliberate strategy to incentivize long-term value creation, with the three four-year performance periods demonstrating a commitment to sustained growth over nearly a decade. The company’s decision to link rewards to shareholder return, rather than solely to internal metrics, highlights its focus on delivering value to investors. The Board of Directors will annually resolve on the commencement and details of each new performance period, allowing for adjustments based on evolving market conditions and company objectives.

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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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