Q (QCLS) Operating Expenses (2013 - 2026)
Q (QCLS) posted Operating Expenses of $1.58 million for Q1 2026, down 33.9% from $2.39 million a year earlier and down 8.8% from the prior quarter.
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Analysis
Q (QCLS) Operating Expenses (2013 - 2026) Analysis & Trends
For the trailing twelve months through Mar 31, 2026, Operating Expenses at Q was $6.4 million, down 17.6% year-over-year; for FY2025, it was $7.21 million, down 5.2% from FY2024.
- Annual Operating Expenses has declined for three consecutive years, though with a five-year compound annual growth rate of 5.9% (FY2020 to FY2025).
- In prior years, Q's Operating Expenses was $7.6 million in FY2024 (-42.9%), $13.31 million in FY2023 (-8.8%), $14.59 million in FY2022 (+10.8%) and $13.17 million in FY2021 (+143.2%).
- Quarterly Operating Expenses has run from a low of $1.32 million in Q3 2025 to a high of $4.41 million in Q3 2021 over five years.
- On a year-over-year basis, Operating Expenses has declined in each of the last three quarters, with an average decline of 26.1% over the last eight quarters.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q2 2021, with growth of 276.5%; the weakest was Q2 2024, with a decline of 65.4%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $1.73 million (Q4 2025), $1.32 million (Q3 2025) and $1.77 million (Q2 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nvidia | 5,226.98 Bn | 5,001.14 Bn | 72.14 Bn | 8.41 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,200.79 Bn | 1,825.66 Bn | 27.22 Bn | 3.13 Bn |
| 3 | Broadcom | 1,570.49 Bn | 1,496.54 Bn | 20.46 Bn | 4.50 Bn |
| 4 | Micron Technology | 1,017.05 Bn | 922.69 Bn | 47.05 Bn | 2.77 Bn |
| 5 | Advanced Micro Devices | 867.62 Bn | 824.37 Bn | 6.20 Bn | 4.21 Bn |
| 6 | Asml Holding | 616.92 Bn | 572.61 Bn | 5.90 Bn | - |
| 7 | Intel | 461.96 Bn | 331.10 Bn | 6.51 Bn | 4.71 Bn |
| 8 | Applied Materials | 307.82 Bn | 273.26 Bn | 4.59 Bn | 1.51 Bn |
| 9 | Lam Research | 296.82 Bn | 272.87 Bn | 4.30 Bn | 1.02 Bn |
| 10 | Q | 3.53 Mn | 3.53 Mn | - | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Mar 31, 2026 | 1.58 Mn |
| Dec 31, 2025 | 1.73 Mn |
| Sep 30, 2025 | 1.32 Mn |
| Jun 30, 2025 | 1.77 Mn |
| Mar 31, 2025 | 2.39 Mn |
| Dec 31, 2024 | 2.15 Mn |
| Sep 30, 2024 | 1.81 Mn |
| Jun 30, 2024 | 1.42 Mn |
| Mar 31, 2024 | 2.27 Mn |
| Dec 31, 2023 | 4.20 Mn |
| Sep 30, 2023 | 3.25 Mn |
| Jun 30, 2023 | 4.10 Mn |
| Mar 31, 2023 | 1.76 Mn |
| Dec 31, 2022 | 3.69 Mn |
| Sep 30, 2022 | 3.36 Mn |
| Jun 30, 2022 | 3.51 Mn |
| Mar 31, 2022 | 4.02 Mn |
| Dec 31, 2021 | 3.13 Mn |
| Sep 30, 2021 | 4.41 Mn |
| Jun 30, 2021 | 3.20 Mn |
API Access
Q Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=QCLS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "QCLS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=QCLS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();