QMCO vs QUBT

Quantum Corporation vs Quantum Computing Inc. — Valuation Comparison 2026

QMCO

Computer Hardware
Quantum Corporation
Quality
5.8
out of 10
Value Trap
40
WARN
Price
$9.32
Last close
Models
7/13
Active
VS

QUBT

Computer Hardware
Quantum Computing Inc.
Quality
5.3
out of 10
Value Trap
18
SAFE
Price
$12.24
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType QMCO Fair ValueQMCO Upside QUBT Fair ValueQUBT Upside
Bayesian DCF Intrinsic $4.23 -65.4%
Earnings Power Value Intrinsic $11.58 +54.0% $1.89 -79.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $25.45 +173.1% $11.70 -4.4%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for QMCO vs QUBT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

QMCO vs QUBT — Which Stock Is More Undervalued?

QMCO scores higher with a 5.8/10 quality rating vs QUBT's 5.3/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Quantum Corporation (QMCO) and Quantum Computing Inc. (QUBT) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

QMCO currently trades at $9.32 with a QOC of 5.8/10, while QUBT trades at $12.24 with a QOC of 5.3/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).