The Future of Quantum Computing

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Before claiming a Quantum advantage, what can classical computers already solve?
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Before claiming a Quantum advantage, what can classical computers already solve?

When evaluating quantum algorithms for combinatorial optimization, the comparison is only meaningful if the classical baseline is taken seriously. I created a technical walkthrough examining how Gurobi, a state-of-the-art classical optimization solver, handles QUBO problems. The purpose is to establish a practical classical reference before moving on to quantum annealers and variational quantum algorithms. The video begins with weighted Max-Cut, derives its QUBO representation, and implements the resulting quadratic binary model in Python using gurobipy. It then explores: - exact versus heuristic approaches to QUBO; - Gurobi’s branch-and-bound search and bound convergence; - primal heuristics for finding high-quality incumbents; - why finding a solution and proving optimality are different tasks; - how MIPGap trades optimality guarantees for runtime; - why runtime depends heavily on the specific problem instance; - the effect of dense versus sparse QUBO matrices; - deterministic behavior under fixed parameters and hardware; - and practical access through Gurobi’s academic licensing. The larger point is not that classical solvers make quantum optimization unnecessary. It is that claims of quantum utility require carefully designed comparisons against highly optimized classical methods. A useful benchmark should consider more than wall-clock runtime: - solution quality and optimality gap; - instance distribution and graph density; - preprocessing and model-conversion costs; - time to the first good solution; - total time required to certify optimality; - solver parameter tuning; - hardware and reproducibility; - and end-to-end execution overhead. Video: https://youtu.be/TB1ny8o4ImQ I’d be interested in the community’s view: which classical baselines and metrics should be considered essential when benchmarking quantum annealing or variational algorithms on QUBO problems? submitted by /u/Future_Ad7567 [link] [comments]

Aug 8, 2026

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TuringQ files for IPO, a first for Chinese Quantum Computing
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TuringQ files for IPO, a first for Chinese Quantum Computing

On July 9, 2026, a spectator examined TuringQ’s Gen2 large-scale hybrid integrated photonic quantum computer at the HIECO 2026 Intelligent Computing Application Conference in Zhengzhou, Henan Province; now, the Shanghai-based firm is seeking to become China’s first publicly listed quantum computing company. TuringQ entered IPO tutoring with the Shanghai branch of the China Securities Regulatory Commission on Wednesday, with Guotai Haitong Securities sponsoring the offering. An industry expert stated that the company’s move “reflects the quantum computing sector’s growing interest in capital markets and a gradual shift from research and early-stage financing toward commercialization,” despite the industry remaining in its early stages. TuringQ’s IPO Filing Signals Quantum Sector Commercialization This move signals a significant shift within the sector, moving beyond foundational research and early-stage funding toward demonstrable commercial viability, even as the industry remains in its nascent stages. The company’s filing reveals registered capital of 1.92 million yuan ($282,000), with Jin Xianmin designated as its legal representative and Shanghai Siliang Quantum Technology Co holding a 34.10 percent stake. Guotai Haitong Securities is serving as TuringQ’s IPO sponsor, indicating a degree of confidence from financial institutions in the company’s prospects. Founded on February 19, 2021, TuringQ distinguishes itself as one of the first Chinese companies to successfully commercialize both photonic quantum chips and complete quantum computers; its business encompasses the design and manufacture of photonic chips alongside the integration of full quantum computing systems. This focus on photonics represents a divergence from more common approaches like superconducting or trapped-ion systems, offering advantages such as room-temperature operation, scalability, and compatibility with existing optical communication networks, according to multiple media reports. TuringQ h

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SEC Declares Registration Statement Effective for Pasqal’s Business Combination with Bleichroeder Acquisition Corp. II
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SEC Declares Registration Statement Effective for Pasqal’s Business Combination with Bleichroeder Acquisition Corp. II

SEC Declares Registration Statement Effective for Pasqal’s Business Combination with Bleichroeder Acquisition Corp. II French neutral-atom quantum computing developer Pasqal Holding SAS has announced that its joint Form F-4 Registration Statement with Special Purpose Acquisition Company (SPAC) Bleichroeder Acquisition Corp. II (NASDAQ: BBCQ) was declared effective by the U.S. Securities and Exchange Commission (SEC) on August 5, 2026. Bleichroeder has scheduled an extraordinary general meeting for August 25, 2026, where shareholders will vote to approve the transaction. Upon closing, the combined enterprise will operate as Pasqal Holding SA and trade on the Nasdaq stock exchange under the ticker symbol PSQL. Founded in 2019 by Nobel laureate Alain Aspect and leading quantum physicists, Pasqal builds neutral-atom quantum processors operating in standard data center environments. With over $300 million in private funding raised to date, the company has deployed system architectures exceeding 1,000 physical qubits while targeting a roadmap of over 10,000 physical qubits and 200 fault-tolerant logical qubits. [ Pasqal NASDAQ Public Listing Roadmap ] │ ┌─────────────────────────────────┴─────────────────────────────────┐ ▼ ▼ Transaction Milestones Commercial & HPC Operational Base • SEC Form F-4 Declared Effective (Aug 5, 2026). • CINECA Leonardo Supercomputer Integration (140 Qubits). • Shareholder Vote Date Set for August 25, 2026. • LANL Materials Simulation Quantum Advantage Milestone. • Ticker Symbol PSQL Reserved for NASDAQ Debut. • Partnerships: Aramco, Crédit Agricole, MegazoneCloud. The regulatory clearance follows several commercial and technical milestones for Pasqal: Materials Science Quantum Advantage: Collaborated with Los Alamos National Laboratory (LANL) to demonstrate quantum advantage in materials simulation workloads. European HPC Integration: Launched Italy’s first neutral-atom QPU at CINECA, integrating a 140-qubit Pasqal system directly with the

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization
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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization

--> Quantum Physics arXiv:2608.05240 (quant-ph) [Submitted on 5 Aug 2026] Title:One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Authors:Yuma Ichikawa, Moeto Mishima View a PDF of the paper titled One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization, by Yuma Ichikawa and 1 other authors View PDF HTML (experimental) Abstract:One-bit post-training quantization represents each weight using only its sign, requiring all deployment contexts to share the same binary weight matrix even when their activation statistics favor different sign patterns. We study this shared-sign constraint and introduce Quantum Random Access Quantization (QRAQ). This framework encodes context-dependent signs in a quantum random-access code and retrieves them via context-matched Pauli measurements. Under an explicit fresh-copy logical readout model, QRAQ produces an unbiased, context-specific binary surrogate with a tractable shot-noise penalty. We prove a row-wise separation from shared-sign one-bit PTQ with signed per-row scales. When the optimal context-wise signs are incompatible, QRAQ achieves a strictly lower ideal reconstruction risk. We also derive finite-shot and calibrated-noise conditions under which this separation is retained. Fixed-readout quantum schemes are classically simulable, so the relevant resource in this model is measurement incompatibility rather than quantization alone. Finally, we characterize the role of scale granularity, provide finite-sample certificates, and evaluate the predicted ideal, finite-shot, noisy, and multi-context regimes in simulator experiments. Comments: Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.05240 [quant-ph]   (or arXiv:2608.05240v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.05240 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuma Ichikawa

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India National Quantum Mission

Explore India's ₹6,003 Crore quantum initiative: 4 thematic hubs, leading startups, and the latest developments in India's quantum ecosystem

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