summary comparing IonQ, Rigetti, D-Wave, and PsiQuantum—covering key technology, funding, and commercialization details in table and bullet form.
Quick Overview
IonQ (IONQ) • Technology: Trapped-ion, universal gate-based. • Advantages: High-fidelity qubits, longer coherence times, fully connected architecture. • Status: Systems on AWS, Azure, Google Cloud. • Funding: $300–500M in cash (late 2023), burn of $20–30M/quarter. • Runway: Several years at current burn rate. • Use Cases: Machine learning, optimization, chemistry, finance, cryptography.
Rigetti (RGTI) • Technology: Superconducting, universal gate-based. • Advantages: Familiar chip-fab methods, large ecosystem synergy (similar to IBM/Google). • Status: Systems on Rigetti Quantum Cloud, AWS Braket, Azure Quantum. • Funding: Under 100M in cash, $20–30M+/quarter burn. • Runway: Shorter, relies on periodic capital raises and cost controls. • Use Cases: General-purpose quantum algorithms (though behind IBM/Google in scale/fidelity).
D-Wave (QBTS) • Technology: Quantum annealing (main) + in-development gate-based. • Advantages: Thousands of qubits for optimization tasks; real commercial customers. • Status: Annealing systems on D-Wave Leap, AWS Braket; gate-based still in R&D. • Funding: Tens of millions in cash, frequent financing. • Runway: Modest; offset partly by annealing revenue. • Use Cases: Optimization (supply chain, logistics, scheduling).
PsiQuantum (Private) • Technology: Photonic (light-based), universal gate-based, aiming for fault tolerance. • Advantages: Photons can have lower noise; targeting a million+ qubits with error correction. • Status: No public system yet, working with GlobalFoundries for chip production. • Funding: 700M–11B+ in venture capital from top investors. • Runway: Significant, but extremely high R&D costs. • Use Cases: Any universal quantum algorithm, once system is operational (late 2020s target).
Comparison Table
Category IonQ (IONQ) Rigetti (RGTI) D-Wave (QBTS) PsiQuantum (Private) Tech Approach Trapped-ion (gate-based) Superconducting (gate-based) Quantum annealing + gate-based R&D Photonic (gate-based) Key Advantages High fidelity, long coherence, connectivity Familiar chip fabrication Best for large-scale optimization, real commercial annealing use Photons more robust to noise; aiming for fault tolerance Challenges Scaling qubit counts, maintaining fidelity Competes with IBM/Google; shorter coherence times Annealing is not universal; gate-based still early Very complex optical alignment, no public system yet Commercial Availability Yes (AWS, Azure, Google Cloud) Yes (Rigetti Cloud, AWS, Azure) Yes for annealing (Leap, AWS), gate-based not yet Not yet (research-focused, planned late 2020s) Cash Position (approx.) $300–500M Under 100M Tens of millions (needs frequent funding) 700M–11B+ from VCs Cash Burn Rate $20–30M/quarter $20–30M+/quarter High, partly offset by annealing revenue Not public, likely sizable Runway Several years Shorter, depends on raises Limited, reliant on new financing Large war chest but huge R&D Use Cases Broad quantum algorithms (ML, chemistry) Broad quantum algorithms, smaller scale Optimization (logistics, scheduling), exploring universal gate-based Universal fault-tolerant quantum (once built)
Key Takeaways • IonQ: Strongest public balance sheet among quantum pure-plays. Already offering universal gate-based computers via the cloud. • Rigetti: Similar universal approach (superconducting) but with tighter finances and tough competition from IBM/Google. • D-Wave: Leading in quantum annealing for optimization, has real customers, but gate-based progress is early. • PsiQuantum: Privately funded “moonshot” aiming for a million-qubit, fault-tolerant photonic system. High potential and high risk.
All data are approximate as of late 2023. Always check official disclosures for the most current figures.
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