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QTUM Quantum Computing update 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 sizableRunway 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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