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Israeli startup Quantum Art nets $100m series A
Quantum Art, an Israeli company spun off from the Weizmann Institute of Science that develops full-stack quantum computers based on multi-core trapped-ion qubits, has closed a US$100 million series A funding round led by Bedford Ridge Capital.
The round included Battery Ventures, Destra Investments, Lumir Growth Partners, Disruptive AI, Harel Insurance, Karen W. Davidson, GTV, Yasmin Lukatz, Corner Capital, Qbeat Ventures, and continued support from seed investors such as Amiti Ventures, StageOne Ventures, Vertex Ventures, Entrée Capital, and the Weizmann Institute of Science, bringing total funding to US$124 million.
Quantum Art will use the funds to accelerate development of its 1,000-qubit multi-core system called Perspective, and to prototype its third-generation 2D architecture targeting thousands of qubits.
Its technology uses reconfigurable, multi-core trapped-ion chains to preserve high connectivity as systems scale.
🔗 Source: Quantum Art
🧠 Food for thought
Implications, context, and why it matters.
The 200-ion demo lacks useful metrics for rival comparisons
- Quantum Art’s 200-ion chain announcement shares no data on two-qubit gate fidelity or state preparation and measurement (SPAM) errors. Gate speeds or coherence times are missing for comparisons with Quantinuum or IonQ.
- No quantitative benchmarks or peer-reviewed papers cover error rates or crosstalk in its multi-core design. So readers cannot judge scaling claims that face heating rates rising with ion number N. Spectral crowding makes gate times scale as N^2 1.
- High connectivity claims via reconfigurable multi-core chains need proof. Publish specs on optical segmentation methods that use laser-created barriers to partition ion chains, as in numerical studies on similar trapped-ion designs 1. Back it with gate infidelity below 1e-4 plus crosstalk control between cells.
Traffic pilots open near-term paths for quantum software
- Quantum Art works with Ayalon Highways on congestion amid government funding. Focus areas include air traffic management optimization. Examples cover trajectory planning or traffic flow problems that are NP-hard (non-deterministic polynomial-time hard; problems that become intractable for classical computers as size grows) 2.
- Software integrators plus operations research firms should track transport optimization RFPs. The EU has calls for quantum sensing or computing in air traffic management (ATM) with criteria on feasibility plus advancement 3.
- Teams skilled in quantum annealing or NVIDIA CUDA Quantum (CUDA-Q) integrations should pursue these transportation RFPs while budgets across municipal or aviation bodies are being set. Public programs signal potential for quantum methods on large-scale optimization despite limited availability 2.
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