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Compare · IONQ vs QBTS · 2026

IonQ vs D-Wave Quantum

A year of returns, risk, and volatility, compared.

IonQ (IONQ) and D-Wave Quantum (QBTS) are compared across trailing return, volatility, drawdown, and risk-adjusted metrics.

Gale Finance Team
Written by Gale Finance Team
Sid Kalla
Reviewed by Sid Kalla CFA Charterholder
Quick answer

Which is a better investment: IONQ or QBTS?

Over the past year, QBTS outperformed IONQ. QBTS returned +156.4% compared with IONQ’s +51.0%. QBTS had the better risk-adjusted return, with a Sharpe ratio of 1.31 versus IONQ’s 0.85. IONQ was less volatile than QBTS, and IONQ had a smaller max drawdown than QBTS.

Total Return
IONQ +51.0%
QBTS +156.4%
Sharpe Ratio
IONQ 0.85
QBTS 1.31
Annualized Volatility
IONQ 96.6%
QBTS 119.5%
Max Drawdown
IONQ -67.6%
QBTS -71.0%

Metric winners: Total Return: QBTS; Sharpe Ratio: QBTS; Annualized Volatility: IONQ (less volatile); Max Drawdown: IONQ (smaller drawdown).

IONQ Total Return
+51.0%
QBTS Total Return
+156.4%

Relative Performance of IONQ vs QBTS (Normalized to 100)

IONQ QBTS

Normalized to 100 at start date for comparison

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Key Takeaways

  • Total Return: IONQ delivered a +51.0% total return, while QBTS returned +156.4% over the same period. QBTS outperformed on total returns.
  • Risk-Adjusted Return (Sharpe Ratio): QBTS had a higher Sharpe (1.31 vs 0.85), indicating better risk-adjusted performance.
  • Volatility (Annualized): QBTS was more volatile, with 119.5% annualized volatility, versus 96.6% for IONQ.
  • Maximum Drawdown: IONQ's maximum drawdown was -67.6%, while QBTS experienced a deeper drawdown of -71.0%.
  • Tail Risk (VaR & Expected Shortfall): At the 5% level (daily log returns), IONQ's VaR was -8.49% and its Expected Shortfall (CVaR) was -10.23%; QBTS's were -9.36% and -11.45%. VaR is the cutoff; Expected Shortfall is the average move on the worst days.
  • Skew & Kurtosis: Skew: IONQ 0.98 vs QBTS 1.27. Excess kurtosis: IONQ 3.44 vs QBTS 4.48. Negative skew leans downside; higher excess kurtosis means fatter tails.
  • Tail Days & Extremes: 2σ tail days (down/up): IONQ 2/8, QBTS 2/9. Worst day: IONQ -14.37% (2025-11-20) vs QBTS -15.22% (2025-10-22). Best day: IONQ +36.52% (2025-05-22) vs QBTS +51.23% (2025-05-08).
  • Risk ratios: Sortino - IONQ: 1.47 vs. QBTS: 2.50 , Calmar - IONQ: 0.76 vs. QBTS: 2.22 , Sterling - IONQ: 1.57 vs. QBTS: 5.19 , Treynor - IONQ: 0.27 vs. QBTS: 0.44 , Ulcer Index - IONQ: 35.71% vs. QBTS: 37.06%

Investment Comparison

If you invested $10,000 in each asset on April 25, 2025:

IONQ $15,096.89 +51.0%
QBTS $25,644.09 +156.4%

Difference: $10,547.2 (QBTS ahead)

IonQ vs D-Wave Quantum Performance Over Time

Metric IONQ QBTS
30 Days 33.4% 21.2%
90 Days -7.7% -24.7%
180 Days -27.6% -40.9%
1 Year 51% 156.4%

Shorter time frames can show different leaders as market conditions change. Consider your investment horizon when comparing performance.

IonQ vs D-Wave Quantum Correlation

Average Correlation
strongly correlated
0.74
Current (30-day) 0.95
30-day rolling range +0.50 to +0.95

IonQ and D-Wave Quantum are strongly correlated over the past year. With a correlation of 0.74, these assets tend to move together, limiting diversification benefits.

For portfolio construction, this strong correlation means holding both IONQ and QBTS provides limited risk reduction — they're likely to decline together in downturns.

Metric Value
Current (30-day) 0.95
Average (full period) 0.74
Minimum (30-day rolling) 0.50
Maximum (30-day rolling) 0.95

Correlation measures how closely two assets move together. Values near +1 indicate strong co-movement, near 0 indicates independence, and negative values indicate inverse movement. Current, minimum, and maximum figures are 30-day rolling correlations on shared daily returns.

Drawdown

Maximum Drawdown
IONQ
-67.6%
QBTS
-71.0%

IonQ experienced its maximum drawdown of -67.6% from 2025-10-13 to 2026-03-30. It has not yet recovered to its previous peak.

D-Wave Quantum experienced its maximum drawdown of -71% from 2025-10-15 to 2026-03-30. It has not yet recovered to its previous peak.

Smaller drawdowns and faster recoveries indicate lower downside risk and greater resilience during market stress.

IonQ vs D-Wave Quantum Volatility (IONQ vs QBTS)

IONQ Volatility
96.6%
±6.08% 1-day vol
QBTS Volatility
119.5%
±7.53% 1-day vol
1-day volatility (1σ)
IONQ
±6.08%
QBTS
±7.53%

IonQ's 96.6% annualized volatility translates to about ±6.08% one-standard-deviation daily volatility.

D-Wave Quantum's 119.5% annualized volatility translates to about ±7.53% one-standard-deviation daily volatility.

QBTS had the wider volatility profile over this window. That means its day-to-day return distribution was broader; IONQ was calmer, but lower volatility does not by itself mean better returns.

Treat the ± daily figure as a one-standard-deviation estimate from historical returns, not a forecast or expected absolute daily move. For context, 15-18% annualized volatility is roughly ±1% one-standard-deviation daily volatility.

Risk-adjusted ratios

Sharpe Ratio of IONQ and QBTS

Sharpe Ratio: IONQ vs. QBTS

Return per total volatility

Sharpe gives us excess return per unit of risk. Upside and downside volatility both count as risk.

Higher is better
Excess return Annualized volatility 0 150% vol 96.6% · excess +81.8% vol 119.5% · excess +157.1%
excess return / total volatility
Formula Sharpe=E[R]RfσR\displaystyle \mathrm{Sharpe} = \frac{\mathbb{E}[R] - R_f}{\sigma_R}

Sharpe ratio measures return per unit of risk (volatility). A higher Sharpe indicates better risk-adjusted performance. QBTS had a higher Sharpe (1.31 vs 0.85), indicating better risk-adjusted performance.

A Sharpe above 1.0 is generally considered good, above 2.0 is excellent. Negative Sharpe means the asset underperformed the risk-free rate. Calculated on each asset's full 365-day lookback of available prices and annualized using the asset calendar (365 for crypto, 252 trading days for equities/ETFs/metals).

Sortino Ratio of IONQ and QBTS

Sortino Ratio: IONQ vs. QBTS

Return per downside volatility

Sortino keeps the return-over-risk idea, but only returns below the target rate count as volatility.

Higher is better
Frequency (days) Daily return (%) target -17.9% +53.9% 47 0
excess return / downside volatility
Formula Sortino=E[R]Rfσdown\displaystyle \mathrm{Sortino} = \frac{\mathbb{E}[R] - R_f}{\sigma_{\mathrm{down}}}

Sortino ratio measures return per unit of downside risk. Unlike Sharpe, it only counts downside deviation (returns below the target return). QBTS had better downside-adjusted returns.

A higher Sortino is better. It's useful when upside volatility is common (crypto is the obvious example). Downside deviation: IONQ 55.6% vs QBTS 62.8%. Calculated on each asset's full 365-day lookback of available prices, using the daily risk-free rate as the target return, and annualized using the asset calendar (365 for crypto, 252 trading days for equities/ETFs/metals).

Calmar Ratio of IONQ and QBTS

Calmar Ratio: IONQ vs. QBTS

CAGR per worst drawdown

Calmar compares CAGR against the single deepest peak-to-trough loss over the period.

Higher is better
0% IONQ +51.4% -67.6% QBTS +157.9% -71.0%
CAGR / max drawdown
Formula Calmar=CAGRMaxDD\displaystyle \mathrm{Calmar} = \frac{\mathrm{CAGR}}{|\mathrm{MaxDD}|}

Calmar ratio compares CAGR to maximum drawdown. Higher Calmar means more return per unit of worst drawdown. QBTS posted the higher Calmar ratio.

Calmar is computed on each asset's full 365-day lookback and uses the max drawdown over that same window.

Sterling Ratio of IONQ and QBTS

Sterling Ratio: IONQ vs. QBTS

Return per average drawdown

Sterling smooths the drawdown penalty by using average drawdown events instead of only the worst one.

Higher is better
0% -19% -37% -56% -75% 10% drawdown threshold
excess annual return / average deep drawdown
Formula Sterling=CAGRRfD>10%\displaystyle \mathrm{Sterling} = \frac{\mathrm{CAGR} - R_f}{\overline{D}_{>10\%}}

Sterling ratio measures excess return per unit of average drawdown (typically drawdowns worse than 10%). QBTS posted the higher Sterling ratio.

Sterling uses average drawdown events deeper than 10% and subtracts the risk-free rate to report excess return.

Treynor Ratio of IONQ and QBTS

Treynor Ratio: IONQ vs. QBTS

Excess return per market beta

Treynor divides excess annualized return by beta — the sensitivity of the asset to broad-market moves. The slope shown is each asset’s beta vs SPY.

Higher is better
Asset return Market return 0 0 β 2.97 β 3.56
excess return / market beta
Formula Treynor=E[R]Rfβ\displaystyle \mathrm{Treynor} = \frac{\mathbb{E}[R] - R_f}{\beta}

Treynor ratio measures excess return per unit of market risk (beta) instead of total volatility. QBTS posted the higher Treynor ratio.

Treynor uses beta vs the S&P 500 (SPY) on shared dates and the average 3-month Treasury rate as the risk-free rate.

Ulcer Index of IONQ and QBTS

Ulcer Index: IONQ vs. QBTS

Drawdown pain

Ulcer Index is a risk index, not a return-over-risk ratio. Lower means smaller and shorter drawdowns.

Lower is better
0% -19% -37% -56% -75%
root-mean-square drawdown
Formula UI=E[Dt2]\displaystyle \mathrm{UI} = \sqrt{\mathbb{E}[D_t^2]}

Ulcer Index captures drawdown depth and duration. Lower Ulcer Index means less drawdown pain. IONQ had the lower Ulcer Index (less drawdown pain).

Ulcer Index is computed from each asset's drawdown series over the full lookback window.

Tail Risk & Distribution Shape (1-Year): IonQ vs. D-Wave Quantum

This section looks at the shape of daily returns, not just the average. Tail stats are computed per asset on its own daily series (crypto includes weekends). We use daily log returns ln(PtPt1)\ln\left(\frac{P_t}{P_{t-1}}\right) so multi-day moves add cleanly.

Definitions: Value at Risk (VaR), Expected Shortfall, skew, kurtosis, and fat tails.

Tail Risk & Distribution Shape: IONQ vs. QBTS (1-Year)

Actual daily return tails

The bars are real daily log-return observations from the article window. Darker bars are observations at or beyond each asset’s 5% VaR cutoff.

Observed returns
IONQ VaR 5% ES 5% QBTS VaR 5% ES 5% -47.2% 0% +47.2% Daily log return
VaR marks the 5th percentile loss cutoff; Expected Shortfall averages the observations beyond that cutoff.
Formula VaR5%=Q0.05(rt),ES5%=E[rtrtVaR5%]\displaystyle \mathrm{VaR}_{5\%}=Q_{0.05}(r_t),\quad \mathrm{ES}_{5\%}=\mathbb{E}[r_t\mid r_t\le \mathrm{VaR}_{5\%}]
Metric (1-Year) IONQ QBTS
5% VaR (daily log return) -8.49% -9.36%
5% Expected Shortfall (CVaR) -10.23% (worst 13 days) -11.45% (worst 13 days)
Skew 0.98 1.27
Excess kurtosis 3.44 4.48
2σ tail days (down / up) 2 / 8 2 / 9
Worst day -14.37% (2025-11-20) -15.22% (2025-10-22)
Best day +36.52% (2025-05-22) +51.23% (2025-05-08)

Downside co-moves (2σ) — 1-Year

Computed on shared dates only (n=249). A “2σ downside move” means a shared-close log return more than 2 standard deviations below that asset’s own mean on this shared-date series. Dates below show simple returns (%) for readability.

Downside co-move map: IONQ vs. QBTS (2σ)

Shared-close daily returns

Dots mark actual downside days: asset-colored dots are one-sided downside moves, and red dots are joint downside days. Grey dots add sampled shared-return context when available. The shaded lower-left zone shows where both IONQ and QBTS crossed their own 2σ downside threshold.

-2σ QBTS -2σ IONQ Joint downside zone -47.2% 0% +47.2% +21.7% 0% -21.7% QBTS daily log return IONQ daily log return
Show downside tail dates

Dates below are shared-date observations. The “Date” is the period end (close). Tail thresholds are computed on log returns, but the table shows simple returns (%) for readability. Returns are computed from the previous shared close to this one (for example, Friday → Monday includes weekend moves).

Days when both IONQ and QBTS had a big down day (2σ)

Date (interval) IONQ QBTS
2026-02-05 -13.89% -14.42%

Days when IONQ had a big down day

Date (interval) IONQ QBTS
2025-11-20 -14.37% -12.50%
2026-02-05 -13.89% -14.42%

Days when QBTS had a big down day

Date (interval) IONQ QBTS
2025-10-22 -6.81% -15.22%
2026-02-05 -13.89% -14.42%

Read this as “how ugly the ugly days get”, not as a precise forecast. One-year samples are small, so tail estimates are inherently noisy.

Full Comparison of IonQ vs. D-Wave Quantum (1-Year)

Metric IONQ QBTS
Total Return +51.0% +156.4%
Annualized Volatility 96.6% 119.5%
Sharpe Ratio 0.85 1.31
Sortino Ratio 1.47 2.50
Calmar Ratio 0.76 2.22
Sterling Ratio 1.57 5.19
Treynor Ratio 0.27 0.44
Ulcer Index 35.71% 37.06%
Max Drawdown -67.6% -71.0%
Avg Correlation to S&P 500 0.36 0.39
5% VaR (daily log return) -8.49% -9.36%
5% Expected Shortfall (CVaR) -10.23% -11.45%
Skew 0.98 1.27
Excess kurtosis 3.44 4.48
2σ tail days (down / up) 2 / 8 2 / 9
Audit this calculation

Formulas, inputs, and conventions used to compute the metrics on this page.

Inputs & conventions

Shared window for pair metrics
2025-04-25 → 2026-04-23 (last shared close).
Rolling correlation sample (shared closes)
220 rolling 30-day values (from 249 shared daily returns).
Annualization (days/year)
IONQ: 252 days/year; QBTS: 252 days/year.
Risk-free rate
Uses the 3-month U.S. Treasury yield (FRED: DGS3MO), averaged over each asset’s window:
  • IONQ: 4.17% over 2025-04-25 → 2026-04-23.
  • QBTS: 4.17% over 2025-04-25 → 2026-04-23.
Volatility drag (rule of thumb)
Estimated from annualized volatility (simple returns). For the log-return framing, see Log returns.
  • IONQ: ≈ -46.7%/yr
  • QBTS: ≈ -71.4%/yr
Data alignment
No forward fill. Correlation and tail co-moves are computed on shared closes only.
For cross-calendar pairs (e.g., crypto vs stocks), weekend/holiday moves roll into the next shared close.
Return conventions
Volatility/Sharpe/Sortino use simple daily returns. Tail-risk uses daily log returns for distribution stats (but tables show simple returns). Log returns.

Formulas

Daily simple return
rt=PtPt11r_t = \frac{P_t}{P_{t-1}} - 1
σann=σ(rt)A\sigma_{ann} = \sigma(r_t)\sqrt{A}
drag12σann2\text{drag} \approx \tfrac{1}{2}\sigma_{ann}^2
S=Arˉrfσ(rt)AS = \frac{A\,\bar{r} - r_f}{\sigma(r_t)\sqrt{A}}
So=ArˉrfE[min(0,rtrf/A)2]ASo = \frac{A\,\bar{r} - r_f}{\sqrt{\mathbb{E}[\min(0,\,r_t - r_f/A)^2]}\,\sqrt{A}}
MDD=mint(PtmaxstPs1)MDD = \min_t\left(\frac{P_t}{\max_{s \le t} P_s} - 1\right)
ρ=cov(rA,rB)σAσB\rho = \frac{\operatorname{cov}(r^A,\,r^B)}{\sigma_A\,\sigma_B}
t=ln(PtPt1)\ell_t = \ln\left(\frac{P_t}{P_{t-1}}\right)
Notation
PtP_t
Price on day t.
rtr_t
Simple daily return.
t\ell_t
Log daily return.
rˉ\bar{r}
Average daily return.
σ(rt)\sigma(r_t)
Standard deviation of daily returns.
AA
Annualization factor (days/year).
rfr_f
Annual risk-free rate.

IonQ vs D-Wave Quantum: Frequently Asked Questions

Which has higher volatility: IONQ or QBTS?

QBTS showed higher volatility at 119.5% annualized, compared to 96.6% for IONQ Over the past year. Higher volatility means larger price swings in both directions.

Does IONQ provide diversification when held with QBTS?

IONQ and QBTS are strongly correlated over the past year, with an average correlation of 0.74. This strong correlation limits diversification benefits.

How bad are the worst 5% days for IONQ vs QBTS?

Over the past year, IONQ's 5% VaR was -8.49% and its 5% Expected Shortfall was -10.23% (worst 13 days). QBTS's were -9.36% and -11.45% (worst 13 days).

Do IONQ and QBTS crash together on bad days?

On shared dates (n=249), when QBTS has a 2σ down day, IONQ also does 50.0% (1/2 days). In the other direction, when IONQ has one, QBTS also does 50.0% (1/2 days).

Which has better risk-adjusted returns: IONQ or QBTS?

QBTS showed better risk-adjusted performance with a Sharpe ratio of 1.31 versus IONQ's 0.85 Over the past year.

Can IONQ and QBTS be combined in a portfolio?

Yes, though allocation sizing matters. Their strong correlation provides limited risk reduction since they tend to move together. QBTS's higher volatility (119.5%) means even small allocations can materially impact overall portfolio risk.

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