Q-CTRL raises $290 million for quantum software and navigation | Magica
Q-CTRL raises $290 million as quantum software faces commercial tests
Editorial Team
••📖7 min read
Salesforce Ventures led a Series C that values Q-CTRL above $1 billion. The funding backs GPS-free navigation and quantum computing software, while undisclosed revenue and contested performance benchmarks leave the commercial case open.
Q-CTRL announced a US$290 million Series C led by Salesforce Ventures at a valuation above US$1 billion.
The funding backs quantum computing software and GPS-free navigation, where the company reports deployments with Airbus and Lockheed Martin.
Revenue and pricing remain undisclosed in the funding announcement; a research challenge shows why computing speed claims need precise comparison terms.
Q-CTRL, the Sydney-based developer of software for controlling errors in quantum computers and sensors, announced a US$290 million Series C on November 12. Salesforce Ventures, enterprise software company Salesforce’s venture arm and an existing Q-CTRL investor, led the round. The financing values Q-CTRL above US$1 billion, according to a report on the deal.
The capital supports two distinct commercial propositions: making quantum computers useful for calculations, and enabling vehicles to navigate without GPS. Q-CTRL reports customers and deployments, but the evidence for each business needs separate scrutiny. A navigation contract does not validate a computing benchmark, and faster computation does not by itself establish lower customer costs.
Q-CTRL’s Robert Love, chief technology officer; Michael Hush, chief scientist; and founder and CEO Michael J. Biercuk, from left. Photo: Q-CTRL. Source: Q-CTRL materials simulation announcement.
More capital behind a strategy dating to 2017
Q-CTRL said in its funding announcement that the oversubscribed round expanded twice and was the largest quantum-sector financing for a software-focused business. Bullhound Capital and Affinity Partners were significant participants. The company plans to expand its software capabilities and global operations across markets including finance and energy, while continuing autonomy and aerospace programs.
The round is about 2.6 times the US$113 million raised across Q-CTRL’s Series B, which ran from 2021 through 2024, according to the . That measures the increase in financing scale, not sales growth. Q-CTRL did not disclose a revenue amount with the round.
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Founder and chief executive Michael J. Biercuk, a quantum physicist who started Q-CTRL in 2017, built the business out of the University of Sydney’s Quantum Science Group. The university’s launch account identified Q-CTRL as the group’s first spin-off and said Biercuk would initially hold company and university positions. Its original work was controlling errors in fragile quantum hardware without changing that hardware.
The company now describes its platform as hardware-agnostic, using AI-powered control software across computing and sensing. The software still needs physical machines: its computing demonstration used IBM’s quantum processors, while navigation requires sensitive sensors to work aboard moving vehicles. In an earlier interview cited by the technology analysis, Biercuk argued that software could reduce the need for heavy, expensive hardware countermeasures against interference.
That is also the lead investor’s rationale. Salesforce Ventures partner Robert Keith said in the funding announcement that Q-CTRL’s control software was why his firm led the round. He cited a materials simulation callable through the IBM Quantum Platform and a navigation system qualified for safety of flight. Those are assessments from a backer with a financial stake in Q-CTRL.
The syndicate also offers potential routes to customers and technology integration. New investors include chipmaker AMD’s venture arm, Abu Dhabi defense group EDGE Group and Australia’s National Reconstruction Fund Corporation; Lockheed Martin Ventures is a returning investor. Q-CTRL says investors can support joint sales, integration and revenue generation. New investor MPower Partners also said it would support expansion in Japan. The announcement does not quantify the sales commitments or financial terms behind those opportunities.
Navigation has deployments and an established rival
Ironstone Opal, Q-CTRL’s flagship navigation system, measures variations in Earth’s magnetic field with quantum sensors and matches them against a map to determine position without GPS, as described in the technology analysis. Q-CTRL’s software is intended to keep those sensitive measurements useful despite interference aboard a vehicle.
Q-CTRL says the system is being deployed on commercial aircraft with Airbus, in autonomous platforms with aerospace and defense company Lockheed Martin, and on unmanned drones. It describes Ironstone Opal as unjammable, unspoofable and undetectable, and claims field performance 100 times better than the best alternative. The funding release does not identify that alternative, define the performance metric or give deployment volumes.
Biercuk identified the U.S. Defense Advanced Research Projects Agency, the Australian and British navies, and Airbus as contract customers in a Bloomberg interview cited by the financing report. That report also relayed Bloomberg’s account of a positioning error no greater than 10 meters, without specifying the test conditions. Such an accuracy figure cannot establish performance across all operating environments. Jamming and spoofing of satellite navigation during conflicts in Ukraine and the Middle East explain the practical demand for alternatives.
The combination of AI, quantum sensors and magnetic maps is already being pursued elsewhere. SandboxAQ, an enterprise AI and quantum technology company whose team originated at Alphabet before becoming independent in 2022, announced its AQNav magnetic navigation system on June 25, 2024.
At that time, SandboxAQ said AQNav had logged more than 200 flight-hours and more than 40 sorties across four aircraft types. It described flight testing with the U.S. Air Force, Boeing and Acubed, Airbus’s Silicon Valley research center. Acubed said its tests were intended to understand performance for commercial use. These were company-reported trials, not evidence that all those aircraft were in routine commercial service.
SandboxAQ also said AQNav operated at room temperature without shielding and could work alongside existing inertial, visual and satellite navigation systems. Its account establishes a competing approach to sensor integration and GPS resilience; it does not supply a common test proving which vendor performs best. Neither company’s cited announcement provides the purchase price or installation economics needed for a cost comparison.
Q-CTRL’s company-reported 3,000-fold speedup contrasts with the preprint’s approximately 36-fold estimate using an improved classical baseline and bare quantum processor time. Neither measures a complete customer workflow; the faster classical run is not the higher-resolution converged reference. Graphic: Magica. Source: Q-CTRL materials simulation announcement.
The computing claim depends on the classical baseline
Q-CTRL’s May 6 materials simulation announcement claimed a 3,000-fold speedup on IBM hardware: two minutes for the quantum algorithm against more than 100 hours for its classical comparison. The experiment used 120 qubits to simulate interacting electrons, a problem relevant to understanding materials for energy applications.
The classical reference used a tensor-network simulation method called the Time-Dependent Variational Principle, or TDVP. Q-CTRL said its quantum results agreed with the classical calculation up to a point, and increasing the classical simulation’s resolution drove up runtime. Its own announcement acknowledged that specialized algorithms or improved GPU acceleration could eventually outperform that classical tool.
A June 3 preprint by researchers affiliated with Multiverse Computing, with additional affiliations at Spanish research institutions, presented just such a challenge. Multiverse develops quantum-inspired AI compression software and announced a Bullhound Capital-led Series B in 2025, giving it a backer in common with Q-CTRL. By exploiting the model’s mathematical symmetries and using GPUs, the authors reported a roughly 100-minute classical run at a representation size comparable to Q-CTRL’s classical baseline. Their comparison reduced the claimed quantum speedup from 3,000-fold to about 36-fold.
That smaller figure still has important limits. The authors compared the classical runtime with 2 minutes 46 seconds of bare quantum processor time, rather than the rounded two minutes in Q-CTRL’s announcement. They said that processor figure excluded readout error mitigation, decay recovery, circuit compilation and post-processing. Their supplementary methods put the quantum run including mitigation and recovery at about 4 minutes 25 seconds, still excluding compilation, cloud data retrieval and post-processing. The 36-fold figure is therefore not a comparison of complete customer workflows.
Accuracy also matters. The preprint’s 100-minute run was comparable to the original classical baseline, which the authors said failed to converge in the hardest part of the simulation. Their higher-resolution calculations supplied converged results across the quantum experiment’s full window and extended beyond it. One such converged run took 4 hours 6 minutes on one NVIDIA H200 GPU, showing that the fastest classical timing and the more accurate reference are different results.
The preprint challenges the size and scope of the speed claim while also providing a classical check on the quantum outputs. Its authors’ findings are not a settled industry verdict, and their data and materials are available on request subject to institutional and commercial constraints. The work concerns computing; it does not test Ironstone Opal’s navigation performance.
What customers and investors still need to see
Q-CTRL’s new capital can fund broader deployment and software development. The next evidence needed differs between the businesses: navigation requires deployment counts, repeat orders, operating conditions and integration costs; computing requires comparisons against current classical methods at matched accuracy, including the steps needed to deliver usable results.
The funding announcement supplies neither product pricing nor a revenue amount. Disclosing those figures, alongside customer deployment evidence and reproducible benchmarks, would let investors distinguish a growing commercial business from technical results whose economic value remains unquantified.
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