Quantum Computing News, Quantum Platforms

UK’s SparQ Access Opens 15 Quantum Platforms for Researchers

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June 6, 2026 by Ivy Delaney

The UK’s quantum research is expanding with the launch of SparQ Access, a program offering researchers access to fifteen distinct quantum computing platforms. Beyond gate-based systems like those from IBM Quantum, including five processors in the US East Region, such as Boston’s Heron R3 and Miami’s Nighthawk R1, the initiative incorporates diverse approaches, including D-WAVE’s Hybrid Solvers for exploring annealing solutions. This concentrated effort, part of the NQCC’s ‘SparQ’ initiative, aims to remove access barriers and accelerate scientific discovery across the UK academic community. Resources are allocated quarterly, with applications received on or before June 30th being considered for the August to October period; the program provides pre-provisioned cloud environments for three-month periods, supporting research activities at UK universities.

SparQ Access Programme Supports UK Quantum Computing

Fifteen distinct quantum computing platforms are now available to UK researchers via the newly established SparQ Access Programme, a concentrated effort to move beyond reliance on single-vendor quantum hardware. This initiative, part of the National Quantum Computing Centre’s (NQCC) ‘SparQ’ initiative, includes gate-based systems and diverse approaches like the hybrid solvers from D-WAVE, including the Hybrid Binary Quadratic Model (version 2p) and the Hybrid Constrained Quadratic Model (version 1p), demonstrating a commitment to exploring the full breadth of quantum computation. Researchers can now access resources from Azure Quantum, Pasqal Simulators, Rigetti, IonQ, Quantinuum, IBM Quantum, Amazon Braket, IQM, QuEra, and AWS, fostering a multi-platform environment for development and discovery.

IBM Quantum’s US East Region offers granular control, with access to five distinct processors: Boston (Heron R3), Kingston (Heron R2), Pittsburgh (Heron R3), Fez (Heron R2), and Miami (Nighthawk R1), allowing researchers to tailor hardware selection within a single cloud region. Resource allocation operates quarterly; submissions received by June 30th are considered for the August 3rd to October 30th period, with award notifications expected in mid-July. The NQCC aims to accelerate scientific research, innovation, skills development, and application discovery through this programme by removing barriers to access and supporting exploration. Programme documentation states that “Users may request access to a range of quantum computing platforms from leading technology providers,” and applications can be submitted via sparqaccess@stfc. ac. uk for general queries or nqcc-qcaas@stfc. ac. uk for technical support, ensuring researchers have the resources needed to advance quantum capabilities within the UK.

Available Quantum Platforms and Resource Allocation

The SparQ Access Programme is supporting exploration of a diverse array of quantum computing technologies, extending beyond the typical focus on single-vendor solutions with access to fifteen distinct platforms. This breadth allows for comparative analysis and the potential to identify optimal architectures for specific computational challenges, enabling targeted experimentation and optimization of algorithms on different hardware configurations. Academic users from the UK can apply for access to these commercial cloud-hosted resources to support research activities at their institutions, with initial allocations provided for three-month periods following a review process.

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Avatar of Ivy Delaney

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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