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Phasecraft says it built the largest VQE dataset without using a quantum computer

Posted on September 14, 2026 By Emily Chen No Comments on Phasecraft says it built the largest VQE dataset without using a quantum computer

Phasecraft Creates Largest VQE Dataset Without Quantum Computer

Phasecraft has produced what it claims as the largest known database of variational quantum eigensolver (VQE) results, with over 3,000 emulations across 13 molecular systems. The study utilized Nvidia Hopper processors hosted at the University of Nottingham, leveraging Nvidia’s cuQuantum toolkit. They report a significant speedup of 15 times over previous results for modeling many-body systems.

The emulations spanned circuits from four to 32 qubits, primarily focusing on the 24-to-28 range. This detail is crucial as it demonstrates the feasibility of simulating quantum computers’ calculations on classical machines. No quantum hardware was employed in this process, as Phasecraft constructed a simulation of quantum computer capabilities on non-quantum computers, currently the only method to generate such datasets at this scale.

While Phasecraft refrains from citing specific papers or preprints for their 15x improvement claim, they highlight their progress alongside Nvidia’s chips and software. This approach aligns with their previous statements advocating for Britain’s role in developing quantum computing technology and securing funding for research.

The company’s focus on building training data for quantum-enhanced density functional theory (DFT) is strategic, aiming to be ready when practical quantum computers become available. The dataset offers a valuable resource for advancing drug discovery through quantum-assisted molecular modeling.

Key Takeaways:

  • Phasecraft generated over 3,000 VQE emulations covering 13 molecular systems using classical hardware.
  • Emulations ranged from 4 to 32 qubits, with a particular focus on the 24-to-28 qubit range.
  • The project aimed to create valuable training data for quantum-enhanced DFT in preparation for future quantum computers.
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