IONQ vs QBTS: Which Quantum Computing Stock Wins?: AI Score, Valuation, Performance and Upside
IonQ is a gate-based quantum computing pure-play targeting the broadest long-term market through its trapped-ion hardware and multi-cloud distribution, while D-Wave is the most commercially mature quantum company with near-term revenue from quantum annealing but a narrower addressable market. Both are early-stage, cash-burning businesses whose long-term value depends on the pace and direction of quantum computing adoption.
Use this IONQ vs QBTS comparison to evaluate two distinct quantum computing investment theses. IonQ offers exposure to general-purpose gate-based quantum with strong hardware performance; D-Wave offers earlier commercial traction through a more specialized near-term approach. Valuation, cash runway, and confidence in each company's technology path are the key variables.
IONQ and QBTS are closely matched — they split the tracked metrics evenly. IONQ has delivered stronger 1-year price return (+72.89% vs +72.76%), though QBTS trades at the lower forward P/E (-78.08x vs -67.29x). Analyst consensus implies meaningfully more upside for QBTS (+18.99%) than for IONQ (-3.57%).
- →Want exposure to the broadest long-term quantum computing market via gate-based systems
- →Believe trapped-ion technology's fidelity advantage translates into durable hardware leadership
- →Are comfortable with significant cash burn in exchange for potential first-mover positioning
- →Want multi-cloud distribution as a moat as enterprise quantum adoption grows
- →Prefer a quantum play with real commercial revenue and paying enterprise customers today
- →Believe quantum annealing provides durable advantages for optimization in logistics, finance, and manufacturing
- →Want exposure to the longest-operating commercial quantum company with proven deployments
- →Are comfortable with a narrower addressable market in exchange for nearer-term revenue traction
| Metric | IONQ | QBTS |
|---|---|---|
| AI score | 57.3 | 39.2 |
| AI rank | #218 | #1192 |
| Latest close | $70.14 | $29.56 |
| 1M return | +51.82% | +44.27% |
| 6M return | +48.85% | +38.00% |
| 1Y return | +72.89% | +72.76% |
How much would $10,000 be worth today if invested at the start of each period, with all dividends reinvested?
| Period | IONQ | QBTS |
|---|---|---|
| 1Y ago | $17.29K (+72.9%) started 2025-06-02 | $17.28K (+72.8%) started 2025-06-02 |
| 5Y ago | $70.14K (+601.4%) started 2021-06-02 | $29.92K (+199.2%) started 2021-06-02 |
| 10Y ago | $64.94K (+549.4%) started 2021-01-04 | $29.12K (+191.2%) started 2020-12-11 |
Hypothetical — past performance does not guarantee future results.
| Metric | IONQ | QBTS |
|---|---|---|
| Market cap | $26.18B | $10.95B |
| Trailing P/E | 179.85 | N/A |
| Forward P/E | -67.29 | -78.08 |
| Price/Sales | 139.92 | 879.97 |
| EV/Revenue | 127.57 | 823.89 |
| Analyst target | $67.64 | $35.17 |
| Target upside | -3.57% | +18.99% |
| Metric | IONQ | QBTS |
|---|---|---|
| Revenue growth | 754.70% | -80.90% |
| Earnings growth | N/A | N/A |
| EPS growth | N/A | N/A |
| FCF margin | -48.83% | -510.34% |
| Operating margin | N/A | N/A |
| Profit margin | 174.88% | 0.00% |
| ROIC proxy | 11.29% | -55.27% |
| Return on equity | 11.29% | -55.27% |
| Dividend yield | N/A | N/A |
| Beta | 3.05 | 1.95 |
| Debt/equity | 0.61 | 4.17 |
| Current ratio | 14.05 | 21.41 |
| Quick ratio | 13.12 | 21.13 |
Lower drawdown and smaller single-period drops generally indicate a smoother ride, though they do not guarantee lower future risk.
| Period | Metric | IONQ | QBTS |
|---|---|---|---|
| 1Y | Growth | +72.89% | +72.76% |
| CAGR | +72.95% | +72.83% | |
| Sharpe ratio | 1.00 | 0.98 | |
| Max drawdown | 67.61% | 71.01% | |
| Max daily drop | 14.37% | 15.22% | |
| Max wkly drop | 29.63% | 39.06% | |
| 5Y | Growth | +601.40% | +199.19% |
| CAGR | +47.64% | +24.51% | |
| Sharpe ratio | 0.83 | 0.73 | |
| Max drawdown | 90.00% | 96.67% | |
| Max daily drop | 39.00% | 36.13% | |
| Max wkly drop | 42.22% | 58.10% | |
| 10Y | Growth | +549.44% | +191.23% |
| CAGR | +41.34% | +21.57% | |
| Sharpe ratio | 0.79 | 0.69 | |
| Max drawdown | 90.00% | 96.67% | |
| Max daily drop | 39.00% | 36.13% | |
| Max wkly drop | 42.22% | 58.10% |
| Category | IONQ | QBTS |
|---|---|---|
| Company | IonQ, Inc. | D-Wave Quantum Inc. |
| Sector | Technology | Technology |
| Industry | N/A | N/A |
| Core business | Trapped-ion quantum computers accessible via cloud (AWS, Azure, Google Cloud) and on-premises deployments for government and enterprise customers. | Quantum annealing systems and software for combinatorial optimization problems, deployed via cloud (Leap) and on-premises for logistics, finance, and manufacturing customers. |
| Investor focus | Algorithmic qubit performance leadership, cloud revenue growth, government contracts, and the path from today's NISQ-era systems to fault-tolerant quantum computing. | Near-term commercial revenue from quantum annealing applications, Leap cloud platform adoption, and the transition toward gate-based quantum computing. |
- →Leading trapped-ion architecture with superior gate fidelity vs. competing approaches
- →Multi-cloud distribution (AWS Braket, Azure Quantum, Google Cloud) drives broad enterprise access
- →Growing government and defense contract pipeline supports near-term revenue visibility
- →Already generating commercial revenue from real-world enterprise optimization use cases
- →Quantum annealing provides a practical near-term advantage for specific optimization problems
- →Longest-operating commercial quantum computing company with proven customer deployments
- →Quantum computing commercialization timeline remains uncertain — meaningful revenue may be years away
- →Significant cash burn as the company funds hardware and software R&D
- →Competition from IBM, Google, and well-funded startups developing alternative qubit technologies
- →Quantum annealing is specialized — addressable market is narrower than general-purpose gate-based systems
- →Ongoing losses and cash burn require continued capital raises
- →Longer-term risk if gate-based quantum computers displace annealing for optimization workloads
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