EarningsQ2 2026 Earnings Report
PSQL Q2 2026 EPS Results
Actual EPS-$8.25
Consensus EPS-$0.17
Beat/MissMissed by -$8.07
One Year Ago EPS―
PSQL Q2 2026 Revenue Results
Actual Revenue$0.00
Expected Revenue$3.10M
Beat/MissMissed by -$3.10M
YoY Revenue Growth―
Earnings Announcement Details
QuarterQ2 2026
Date09/24/2026
TimeBefore Open
Conference CallThursday, September 24, 2026
PSQL Upcoming Earnings
Pasqal Holding SA's next earnings date is estimated for November 6, 2026, based on past reporting schedules.
Q2 2026 Earnings Call Audio
PSQL Q2 2026 Earnings Call
0:00 / 0:00
Q2 2026 Earnings Slide Deck
No slide deck is available for this earnings event.
Q2 2026 Earnings Call Summary
Earnings Call Sentiment|Positive
The call was predominantly positive, emphasizing 14% revenue growth, 34% growth in QPU-related services, EUR 70.4 million of booked and awarded business, major technology demonstrations, strategic customer expansion, improved liquidity, Nasdaq listing, and substantial new financing. The principal negative factors were the wider reported operating and net losses, higher operating cash use, lower cryostat and grant revenue, and the timing and qualification requirements for converting deployments into revenue. Management attributed much of the expense increase to one-off financing, listing, and share-based compensation charges and maintained its technology and commercial roadmap.Company Guidance
Revenue and QPU Services Growth
First-half 2026 revenue increased 14% year-over-year to EUR 4.9 million from EUR 4.3 million. QPU-related services revenue rose 34%, or EUR 1 million, to EUR 3.9 million, primarily driven by EUR 0.8 million of revenue recognized from the QPU upgrade under the Jülich contract.
Deployed Quantum Computing Fleet
Pasqal stated that it operates the world's second-largest fleet of high-complexity quantum computers after IBM, with seven systems deployed across three continents and three additional systems in production. Five of the seven systems are currently accessible externally, while two are used for internal development and application planning.
Strategic Customer Expansion
Pasqal advanced strategic customer relationships across energy, financial services, and high-value materials. The company commissioned the QPU under its Aramco contract in May, with applications including reservoir simulation, well placement optimization, rig scheduling, and demurrage prediction. Its Crédit Agricole relationship progressed from portfolio optimization and credit risk modeling toward production-oriented applications involving capital reserve consumption and risk-weighted assets.
Saudi Arabia Commercial Joint Venture Initiative
In August, Pasqal announced a memorandum of understanding with Eleven Ventures to establish a commercial joint venture intended to deploy, commercialize, and scale Pasqal systems across Saudi Arabia and the broader region.
Booked and Awarded Business
Booked and awarded business stood at EUR 70.4 million as of June 30, 2026. Approximately EUR 37 million was described as commercial revenue, with around EUR 12 million expected to convert into revenue between the call date and the end of 2026; the remainder consists primarily of grants and tax credits that will convert progressively as work is executed.
Quantum Advantage in Materials Simulation
Pasqal performed a one-to-one simulation of the rare-earth magnetic material TMGO, reproduced experimentally observed behavior, and generated consistent results across five independently deployed QPUs. The company explored quantum regimes and predicted new properties that were subsequently verified experimentally. The work took hours on Pasqal's systems compared with weeks using classical GPUs, although the company noted that convergence using GPUs was not guaranteed.
Fault-Tolerant Roadmap Progress
Pasqal demonstrated under live conditions an initial capability to construct and operate key building blocks of a logical qubit and used that capability to solve differential equations. The company said its target of achieving fault-tolerant quantum computing in the digital domain by 2029 remains unchanged.
XPRIZE Quantum Applications Finalist
In May, Pasqal was selected as a finalist in the XPRIZE Quantum Applications Competition, supported by Google Quantum AI, Google.org, and GESDA. The competition evaluates quantified impact, hardware feasibility, and demonstrable advantage over classical approaches.
Photonic Integrated Circuit Milestone
Through Aeponyx, Pasqal reported trapping four individual rubidium atoms using optical tweezers generated by a photonic integrated circuit, which it believes is a first for a neutral atom quantum computer. Atom lifetimes matched existing bulk optic systems with no loss in qubit control quality, and the company cited a roadmap to reduce the optical footprint of future processors by up to 50x.
Agentic GenAI Quantum Workloads
Pasqal demonstrated agentic GenAI systems that converted a plain-English description of a quantum simulation into a running result on a live neutral-atom QPU. The agent accounts for hardware constraints, encodes the problem, validates code on a classical emulator, and runs it on the QPU while a researcher remains responsible for scientific decisions. The work was reported by Nature.
Neutral-Atom Architecture and Power Profile
The company stated that its neutral-atom architecture uses identical rubidium atoms, which connect without wiring, and supports roadmaps to tens of thousands of high-quality qubits by 2030. Current QPUs consume approximately 3.5 kW and are designed for conventional data-center environments.
Analog-to-Fault-Tolerant Platform Strategy
Pasqal said its platform supports analog quantum computing today while providing a path toward digital fault-tolerant quantum computing on the same underlying hardware architecture. The company described this as enabling customers to develop applications, workflows, integrations, and expertise before large-scale fault tolerance arrives.
Expanding Customer Use Cases
For strategic accounts such as Aramco, Pasqal indicated that the number of use cases can expand from one or two to well over a dozen, representing more than a 6x increase in the number of use cases.
Capital Raised and Improved Liquidity
Pasqal ended June 2026 with EUR 110.8 million of cash and cash equivalents, up from EUR 73.8 million at December 31, 2025, and reported a net liquidity position of EUR 83.8 million. It issued Series C shares for gross proceeds of EUR 69.7 million in January and February, while EUR 95.2 million of redeemable bonds converted into Series C shares in March.
Nasdaq Listing and Additional Financing
Following the reporting period, Pasqal completed its business combination on August 27, 2026, and began trading on Nasdaq under the symbol TSQL. At closing, it received $27.7 million from the SPAC trust account and substantially concurrently received $250 million of aggregate subscription proceeds through senior unsecured convertible bonds and associated warrants.
Strengthened Equity Position
Total equity increased to EUR 129.1 million from negative EUR 4.4 million at year-end 2025, reflecting the Series C financing and conversion of redeemable bonds into equity.
Manufacturing Capacity and Execution
Pasqal stated that it operates facilities in France and Canada to build and deliver systems as demand develops, and management said the company is not experiencing manufacturing constraints.
Capital Allocation Priorities
The company plans to direct capital toward core research and development, acceleration of its fault-tolerant roadmap, commercial expansion, manufacturing and deployment capacity, software and middleware, and capabilities required to scale the business.
Cloud and Hybrid Computing Expansion
Pasqal plans to expand access through on-premise deployments, cloud services, and partnerships integrating its systems into the broader high-performance computing ecosystem. The company also plans to ensure its systems work with traditional computation based on TPUs and GPUs.
Materials and Rare-Earth Research
Pasqal said its work with USA Rare Earth combines material simulation with graph machine learning and other algorithms to improve the efficiency of the rare-earth separation process.