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Horizon Quantum Goes Public via SPAC, Raises $120 Millionquantum-computing

Horizon Quantum Goes Public via SPAC, Raises $120 Million

Insider Brief Horizon Quantum completed a SPAC merger with dMY Squared Technology Group, securing about $120 million and listing on Nasdaq under the ticker “HQ” to expand its quantum software business. The company is focused on building hardware-agnostic software infrastructure, including its Triple Alpha development environment, to enable developers to run applications across different quantum computing platforms. Executives said the funding will support R&D and system development as advances in quantum hardware and error correction push the industry toward broader practical use. PRESS RELEASE — Horizon Quantum Computing Pte. Ltd. (“Horizon Quantum”), a pioneer of software infrastructure for quantum applications, today announced that it has completed its previously announced business combination (the “Business Combination”) with dMY Squared Technology Group, Inc. (“dMY”) (OTC: “DMYY”, “DMYYU” and “DMYYW”), a publicly traded special purpose acquisition company. The Business Combination was approved by dMY’s shareholders at dMY’s special meeting held on March 17, 2026. On March 20, 2026, the combined company’s Class A ordinary shares and warrants will begin trading on Nasdaq under the ticker symbols “HQ” and “HQWWW,” respectively. Horizon Quantum is building software infrastructure that empowers developers to use quantum computing to solve the world’s toughest computational problems. The closing of the Business Combination provides Horizon Quantum with gross proceeds of approximately $120 million, before transaction expenses, which the company plans to use to accelerate its investments in research and development, strengthen its hardware testbed, and further advance its integrated development environment Triple Alpha. “Recent rapid progress in advancing quantum computing hardware and breakthroughs in error correction mean that the field is reaching an inflection point. With today’s closing and our Nasdaq listing, Horizon Quantum is positioned to deliver the softw

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QTUM: The Oldest Quantum Computing ETF And Its Newest Rivalquantum-computing

QTUM: The Oldest Quantum Computing ETF And Its Newest Rival

Sean Daly1.74K FollowersFollow5ShareSavePlay(9min)CommentsSummaryQuantum computing is positioned to revolutionize industries by addressing problems traditional computing cannot solve.The sector is projected to generate a $2 trillion economic impact by 2035.The current market sentiment is heavily influenced by hype, but long-term potential remains significant.While QTUM and WQTM both offer interesting, if differing, investment strategies for the sector, Defiance Quantum ETF is the better bet in our present market conditions. Olemedia/E+ via Getty Images Quantum computing has been hyped to the gills, but the nascent platform does have the potential to revolutionize industries. By dropping bits to leverage qubits via “entanglement” and “superposition,” quantum could offer up solution setsThis article was written bySean Daly1.74K FollowersFollowSean Daly writes on ETFs, biotech and FINTECH solutions in the banking space.  He teaches international finance and financial risk management at Pace University and was a visiting lecturer at Princeton University from 2005 to 2009.  He was educated at Columbia University.  He has also written extensively on real estate and  economic development, exploring issues as diverse as Chinese urbanization, CMI multilateral currency swap arrangements, energy geopolitics, and Asia's sovereign wealth funds.    Global strategy and private equity background. Equity Approach: long/short, event-driven, with a focus on small cap biotech and the emerging markets.Analyst’s Disclosure: I/we have a beneficial long position in the shares of INFQ either through stock ownership, options, or other derivatives. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. Seeking Alpha's Disclosure: Past performance is no guarantee of future results.

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Alice & Bob Reduces Quantum Error Correction Decoding Time via NVIDIA CUDA-Q Integrationquantum-computing

Alice & Bob Reduces Quantum Error Correction Decoding Time via NVIDIA CUDA-Q Integration

Alice & Bob Reduces Quantum Error Correction Decoding Time via NVIDIA CUDA-Q Integration Alice & Bob has reported a 9.25x speedup in quantum error correction (QEC) decoding simulations by utilizing the NVIDIA CUDA-Q platform. The runtime for decoding simulated syndrome data was reduced from 18 hours and 2 minutes on a CPU-based implementation to 1 hour and 57 minutes using GPU-accelerated simulation. This benchmark compared the performance of an NVIDIA GH200 Grace Hopper system against an AMD Ryzen™ 9 9950X 16-core CPU across 100,000 simulated shots. The results indicate that the GPU-accelerated approach maintains identical logical error performance compared to the CPU version, with no degradation in decoding accuracy. The simulation focused on “Elevator Codes,” a concatenation-based error correction architecture developed by Alice & Bob specifically for biased noise cat qubits. These codes are designed to achieve the low logical error rates required for large-scale quantum applications, such as Shor’s algorithm and molecular simulations for quantum chemistry. By leveraging the parallelism of GPUs, the CUDA-Q platform allows for the simultaneous processing of independent decoding tasks. This reduction in computational overhead is intended to facilitate the study and refinement of fault-tolerant architectures during the design phase. Quantum error correction requires classical processing to interpret syndromes, which are measurement outcomes that signal the presence of errors without destroying quantum information. In research settings, large-scale simulations are necessary to validate code performance and estimate failure rates under realistic noise models. Utilizing GPUs for these “offline” decoding workflows addresses a primary bottleneck in QEC research, enabling more frequent iterations on architectural variations. The speedup allows researchers to explore complex code designs that would otherwise be constrained by the limitations of sequential

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