Quantinuum Inc. Class A Common Stock

Quantinuum Inc. Class A Common Stock

QNT
Quantinuum Inc. Class A Common StockUS flagNASDAQ Global Market
38.65
USD
+2.97
- -
10.18BMarket Cap
2024 Y
2025 Y
Revenue per Share
0.66
0.93
Basic EPS, GAAP
-4.16
-2.15
Free Cash Flow per Basic Share
-3.49
-5.13
Dividend per Share
- -
- -
Book Value per Share
9.42
5.28
Tangible Book Value per Share
-14.4
-22.11
Basic Weighted Avg Shares
35
33
Sales/Revenue/Turnover
23
31
Operating Margin (%)
-664.75
-1,713.66
Depreciation Expense
32
30
Net Income, GAAP
-144
-72
Effective Tax Rate (%)
- -
- -
Profit Margin (%)
-626.97
-231.26
Working Capital
168
746
LT Debt
5
7
Total Equity
999
1,688
Return on Invested Capital (%)
- -
- -
Return on Capital (%)
- -
- -
Return on Common Equity (%)
- -
-29.61

Capital Structure

FRC

•

in mil. unless spec.
No data availableFinancial data will appear here once available

Working Capital

FRC

•

in mil. unless spec.
No data availableFinancial data will appear here once available

Growth Rates

FRC

•

in mil. unless spec.

(avg. rate of change)

10 years
5 years
1 year
Total Equity
- -
- -
68.99%
Free Cash Flow
- -
- -
40.83%
Net Income, GAAP
- -
- -
-50.35%
Sales/Revenue/Turnover
- -
- -
34.61%
Total Cash Common Dividend
- -
- -
- -

Quarterly Revenue

FRC

•

in mil. unless spec.

Year

Q1
Q2
Q3
Q4
FY
2024
- -
- -
- -
- -
23
2025
19
2
- -
- -
31
2026
5
8
- -
- -
- -

Quarterly Earnings Per Share

FRC

•

in mil. unless spec.

Year

Q1
Q2
Q3
Q4
FY
2024
- -
- -
- -
- -
-4.16
2025
-0.88
-1.64
- -
- -
-2.15
2026
-0.48
-1.93
- -
- -
- -

Quarterly Dividends Per Share

FRC

•

in mil. unless spec.

Year

Q1
Q2
Q3
Q4
FY
2024
- -
- -
- -
- -
- -
2025
- -
- -
- -
- -
- -
2026
- -
- -
- -
- -
- -

Company Description

CEO
Rajeeb Hazra
Full Time Employees
700
Sector
Technology
Industry
Computer Hardware
Address
303 S Technology Court Broomfield CO United States of America 80021
IPO Date
Jun 4, 2026
Business
Quantinuum Inc. is a leading quantum computing company specializing in a vertically integrated platform that combines advanced trapped-ion quantum hardware, middleware, and software tools to deliver deployable quantum解决方案 across real-world applications. Core offerings include high-performance quantum processors (H-Series trapped-ion systems), hardware platforms with all-to-all qubit connectivity, quantum software development tools and SDKs, application libraries, and security-oriented solutions such as quantum-origin cryptography. The company markets TKET, a hardware-agnostic quantum software development kit, and InQuanto for quantum computational chemistry workloads, alongside Quantum Origin for quantum-enhanced cybersecurity through true randomness. Quantinuum also provides Nexus, an end-to-end platform to access and manage quantum resources, and InQuanto for acceleration of molecular and materials simulations, all designed to enable hybrid quantum-classical workflows for enterprise clients. The firm operates globally with a multi-region footprint spanning North America, Europe, and Asia, supporting customers in finance, manufacturing, energy, pharmaceuticals, and research sectors. Founded in 2021 through the merger of Honeywell Quantum Solutions and Cambridge Quantum Computing, the company is headquartered in Broomfield, Colorado, USA, and remains at the forefront of scalable quantum computing through ongoing hardware iterations, software ecosystem expansion, and strategic collaborations. Recent developments include partnerships with technology and industry players to expand access to its quantum platform, continued product roadmap advancement for higher qubit counts and lower error rates, and the launch of expanded solutions targeting practical industry use cases such as quantum-enabled AI and secure quantum communications. Quantinuum’s business segments encompass hardware manufacturing, software development, platform services, and industry-specific solution offerings, with a focus on delivering integrated quantum computing capabilities that enable clients to solve complex computational problems more efficiently than with classical methods.

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