D-Wave Quantum vs Rigetti Computing (QBTS vs RGTI) 2026: Stock Returns, Risk & Volatility

Q: Which is a better investment: QBTS or RGTI?

Over the past year, QBTS looks like the better investment: it had higher total return (QBTS +101.7% vs RGTI +63.8%) and a higher Sharpe ratio (QBTS 1.14 vs RGTI 0.94). On risk, RGTI was less volatile (RGTI 104.9% vs QBTS 108.3%), QBTS had a smaller max drawdown (QBTS -71.0% vs RGTI -77.1%), and RGTI had less severe losses on the worst 5% of days (Expected Shortfall of RGTI -11.04% vs QBTS -11.45%).

Written by Gale Finance Team

Reviewed by Sid Kalla CFA Charterholder

Quick answer

Which is a better investment: QBTS or RGTI?

Over the past year, QBTS outperformed RGTI. QBTS returned +101.7% compared with RGTI’s +63.8%. QBTS had the better risk-adjusted return, with a Sharpe ratio of 1.14 versus RGTI’s 0.94. RGTI was less volatile than QBTS, but QBTS had a smaller max drawdown than RGTI.

Total Return

QBTS +101.7%

RGTI +63.8%

Sharpe Ratio

QBTS 1.14

RGTI 0.94

Annualized Volatility

QBTS 108.3%

RGTI 104.9%

Max Drawdown

QBTS -71.0%

RGTI -77.1%

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

QBTS Total Return

↑ +101.7%

RGTI Total Return

↑ +63.8%

Relative Performance of QBTS vs RGTI (Normalized to 100)

QBTS RGTI

Normalized to 100 at start date for comparison

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Key Takeaways

Total Return: QBTS delivered a +101.7% total return, while RGTI returned +63.8% over the same period. QBTS outperformed on total returns.
Risk-Adjusted Return (Sharpe Ratio): QBTS had a higher Sharpe (1.14 vs 0.94), indicating better risk-adjusted performance.
Volatility (Annualized): QBTS was more volatile, with 108.3% annualized volatility, versus 104.9% for RGTI.
Maximum Drawdown: QBTS's maximum drawdown was -71.0%, while RGTI experienced a deeper drawdown of -77.1%.
Tail Risk (VaR & Expected Shortfall): At the 5% level (daily log returns), QBTS's VaR was -9.36% and its Expected Shortfall (CVaR) was -11.45%; RGTI's were -9.11% and -11.04%. VaR is the cutoff; Expected Shortfall is the average move on the worst days.
Skew & Kurtosis: Skew: QBTS 0.66 vs RGTI 0.76. Excess kurtosis: QBTS 0.99 vs RGTI 1.60. Negative skew leans downside; higher excess kurtosis means fatter tails.
Tail Days & Extremes: 2σ tail days (down/up): QBTS 3/10, RGTI 2/9. Worst day: QBTS -15.22% (2025-10-22) vs RGTI -14.86% (2025-10-16). Best day: QBTS +25.93% (2025-05-20) vs RGTI +30.19% (2025-07-16).
Risk ratios: Sortino - QBTS: 1.96 vs. RGTI: 1.62 , Calmar - QBTS: 1.44 vs. RGTI: 0.83 , Sterling - QBTS: 2.91 vs. RGTI: 1.73 , Treynor - QBTS: 0.33 vs. RGTI: 0.28 , Ulcer Index - QBTS: 38.99% vs. RGTI: 47.07%

Investment Comparison

If you invested $10,000 in each asset on May 14, 2025:

QBTS $20,171.48 +101.7%

RGTI $16,383.16 +63.8%

Difference: $3,788.32 (QBTS ahead)

D-Wave Quantum vs Rigetti Computing Performance Over Time

Metric	QBTS	RGTI
30 Days	56.8%	29.9%
90 Days	13.8%	16.1%
180 Days	-4.4%	-24.3%
1 Year	101.7%	63.8%

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

D-Wave Quantum vs Rigetti Computing Correlation

Average Correlation

strongly correlated

0.84

Current (30-day) 0.96

30-day rolling range +0.52 to +0.96

D-Wave Quantum and Rigetti Computing are strongly correlated over the past year. With a correlation of 0.84, these assets tend to move together, limiting diversification benefits.

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

Metric	Value
Current (30-day)	0.96
Average (full period)	0.84
Minimum (30-day rolling)	0.52
Maximum (30-day rolling)	0.96

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

QBTS

-71.0%

RGTI

-77.1%

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.

Rigetti Computing experienced its maximum drawdown of -77.1% 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.

D-Wave Quantum vs Rigetti Computing Volatility (QBTS vs RGTI)

QBTS Volatility

108.3%

±6.83% 1-day vol

RGTI Volatility

104.9%

±6.61% 1-day vol

1-day volatility (1σ)

QBTS

±6.83%

RGTI

±6.61%

D-Wave Quantum's 108.3% annualized volatility translates to about ±6.83% one-standard-deviation daily volatility.

Rigetti Computing's 104.9% annualized volatility translates to about ±6.61% one-standard-deviation daily volatility.

QBTS had the wider volatility profile over this window. That means its day-to-day return distribution was broader; RGTI 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 QBTS and RGTI

Sharpe Ratio: QBTS vs. RGTI

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 / total volatility

Formula

\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 (1.14 vs 0.94), 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 QBTS and RGTI

Sortino Ratio: QBTS vs. RGTI

Return per downside volatility

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

Higher is better

excess return / downside volatility

Formula

\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: QBTS 62.8% vs RGTI 60.8%. 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 QBTS and RGTI

Calmar Ratio: QBTS vs. RGTI

CAGR per worst drawdown

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

Higher is better

CAGR / max drawdown

Formula

\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 QBTS and RGTI

Sterling Ratio: QBTS vs. RGTI

Return per average drawdown

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

Higher is better

excess annual return / average deep drawdown

Formula

\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 QBTS and RGTI

Treynor Ratio: QBTS vs. RGTI

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

excess return / market beta

Formula

\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 QBTS and RGTI

Ulcer Index: QBTS vs. RGTI

Drawdown pain

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

Lower is better

root-mean-square drawdown

Formula

\displaystyle \mathrm{UI} = \sqrt{\mathbb{E}[D_t^2]}

Ulcer Index captures drawdown depth and duration. Lower Ulcer Index means less drawdown pain. QBTS 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): D-Wave Quantum vs. Rigetti Computing

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 $\ln\left(\frac{P_t}{P_{t-1}}\right)$ so multi-day moves add cleanly.

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

Tail Risk & Distribution Shape: QBTS vs. RGTI (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

VaR marks the 5th percentile loss cutoff; Expected Shortfall averages the observations beyond that cutoff.

Formula

\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)	QBTS	RGTI
5% VaR (daily log return)	-9.36%	-9.11%
5% Expected Shortfall (CVaR)	-11.45% (worst 13 days)	-11.04% (worst 13 days)
Skew	0.66	0.76
Excess kurtosis	0.99	1.60
2σ tail days (down / up)	3 / 10	2 / 9
Worst day	-15.22% (2025-10-22)	-14.86% (2025-10-16)
Best day	+25.93% (2025-05-20)	+30.19% (2025-07-16)

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: QBTS vs. RGTI (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 QBTS and RGTI crossed their own 2σ downside threshold.

2σ

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 QBTS and RGTI had a big down day (2σ)

Date (interval)	QBTS	RGTI
2026-02-05	-14.42%	-12.89%

Days when QBTS had a big down day

Date (interval)	QBTS	RGTI
2025-10-22	-15.22%	-9.85%
2025-11-20	-12.50%	-10.45%
2026-02-05	-14.42%	-12.89%

Days when RGTI had a big down day

Date (interval)	QBTS	RGTI
2025-10-16	-9.65%	-14.86%
2026-02-05	-14.42%	-12.89%

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 D-Wave Quantum vs. Rigetti Computing (1-Year)

Metric	QBTS	RGTI
Total Return	+101.7%	+63.8%
Annualized Volatility	108.3%	104.9%
Sharpe Ratio	1.14	0.94
Sortino Ratio	1.96	1.62
Calmar Ratio	1.44	0.83
Sterling Ratio	2.91	1.73
Treynor Ratio	0.33	0.28
Ulcer Index	38.99%	47.07%
Max Drawdown	-71.0%	-77.1%
Avg Correlation to S&P 500	0.42	0.39
5% VaR (daily log return)	-9.36%	-9.11%
5% Expected Shortfall (CVaR)	-11.45%	-11.04%
Skew	0.66	0.76
Excess kurtosis	0.99	1.60
2σ tail days (down / up)	3 / 10	2 / 9

Audit this calculation

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

Inputs & conventions

Shared window for pair metrics: 2025-05-14 → 2026-05-12 (last shared close).
Rolling correlation sample (shared closes): 220 rolling 30-day values (from 249 shared daily returns).
Annualization (days/year): QBTS: 252 days/year; RGTI: 252 days/year.
Risk-free rate: Uses the 3-month U.S. Treasury yield (FRED: DGS3MO), averaged over each asset’s window:

QBTS: 4.15% over 2025-05-14 → 2026-05-12.

RGTI: 4.15% over 2025-05-14 → 2026-05-12.
Volatility drag (rule of thumb): Estimated from annualized volatility (simple returns). For the log-return framing, see Log returns.

QBTS: ≈ -58.6%/yr

RGTI: ≈ -55.0%/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

r_t = \frac{P_t}{P_{t-1}} - 1

Volatility

\sigma_{ann} = \sigma(r_t)\sqrt{A}

Volatility drag (approx.)

\text{drag} \approx \tfrac{1}{2}\sigma_{ann}^2

Sharpe Ratio

S = \frac{A\,\bar{r} - r_f}{\sigma(r_t)\sqrt{A}}

Sortino Ratio

So = \frac{A\,\bar{r} - r_f}{\sqrt{\mathbb{E}[\min(0,\,r_t - r_f/A)^2]}\,\sqrt{A}}

Maximum Drawdown

MDD = \min_t\left(\frac{P_t}{\max_{s \le t} P_s} - 1\right)

Average Correlation

\rho = \frac{\operatorname{cov}(r^A,\,r^B)}{\sigma_A\,\sigma_B}

Log returns

\ell_t = \ln\left(\frac{P_t}{P_{t-1}}\right)

Notation

$P_t$: Price on day t.
$r_t$: Simple daily return.
$\ell_t$: Log daily return.
$\bar{r}$: Average daily return.
$\sigma(r_t)$: Standard deviation of daily returns.
$A$: Annualization factor (days/year).
$r_f$: Annual risk-free rate.

D-Wave Quantum vs Rigetti Computing: Frequently Asked Questions

Which has higher volatility: QBTS or RGTI?

QBTS showed higher volatility at 108.3% annualized, compared to 104.9% for RGTI Over the past year. Higher volatility means larger price swings in both directions.

Does QBTS provide diversification when held with RGTI?

QBTS and RGTI are strongly correlated over the past year, with an average correlation of 0.84. This strong correlation limits diversification benefits.

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

Over the past year, QBTS's 5% VaR was -9.36% and its 5% Expected Shortfall was -11.45% (worst 13 days). RGTI's were -9.11% and -11.04% (worst 13 days).

Do QBTS and RGTI crash together on bad days?

On shared dates (n=249), when RGTI has a 2σ down day, QBTS also does 50.0% (1/2 days). In the other direction, when QBTS has one, RGTI also does 33.3% (1/3 days).

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

QBTS showed better risk-adjusted performance with a Sharpe ratio of 1.14 versus RGTI's 0.94 Over the past year.

Can QBTS and RGTI 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 (108.3%) means even small allocations can materially impact overall portfolio risk.

Explore our financial glossary

Which is a better investment: QBTS or RGTI?

Share This Comparison of QBTS vs RGTI

Relative Performance of QBTS vs RGTI (Normalized to 100)

Trade QBTS or RGTI

Key Takeaways

Investment Comparison

D-Wave Quantum vs Rigetti Computing Performance Over Time

D-Wave Quantum vs Rigetti Computing Correlation

Drawdown

D-Wave Quantum vs Rigetti Computing Volatility (QBTS vs RGTI)

Risk-adjusted ratios

Sharpe Ratio of QBTS and RGTI

Sharpe Ratio: QBTS vs. RGTI

Sortino Ratio of QBTS and RGTI

Sortino Ratio: QBTS vs. RGTI

Calmar Ratio of QBTS and RGTI

Calmar Ratio: QBTS vs. RGTI

Sterling Ratio of QBTS and RGTI

Sterling Ratio: QBTS vs. RGTI

Treynor Ratio of QBTS and RGTI

Treynor Ratio: QBTS vs. RGTI

Ulcer Index of QBTS and RGTI

Ulcer Index: QBTS vs. RGTI

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

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

Downside co-moves (2σ) — 1-Year

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

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

Days when QBTS had a big down day

Days when RGTI had a big down day

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

Inputs & conventions

Formulas

D-Wave Quantum vs Rigetti Computing: Frequently Asked Questions

Which has higher volatility: QBTS or RGTI?

Does QBTS provide diversification when held with RGTI?

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

Do QBTS and RGTI crash together on bad days?

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

Can QBTS and RGTI be combined in a portfolio?