Quantum computing — July 2026

🖥 Learn Quantum Computing with Python and IBM Quantum, 2nd Edition: Write your own practical quantum programs with Python (2025)

❔ Чему вы научитесь: Вы погрузитесь в революционную сферу квантовых вычислений, научившись писать код для настоящих квантовых процессоров с помощью языка Python и облачной платформы IBM Quantum. Руководство доходчиво объясняет концепции суперпозиции и квантовой запутанности, переводя их в практическую плоскость через разработку квантовых вентилей и цепей с использованием фреймворка Qiskit. Вы освоите реализацию классических квантовых алгоритмов и поймете, как они решают задачи, недоступные для традиционных суперкомпьютеров.

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🧠 Una laptop corriente resolvió un problema cuántico que desafió a una computadora cuántica

Investigadores del Instituto Flatiron demostraron que un cálculo que D-Wave consideraba imposible para computadoras clásicas puede resolverse con hardware ordinario. El equipo utilizó redes tensoriales combinadas con propagación de creencias, un algoritmo de los años 80, para reproducir los mismos resultados que la máquina cuántica. Parte de los cálculos corrió en una laptop personal.

El desafío consistía en simular cientos de qubits interactuantes organizados en estructuras 2D y 3D complejas. En lugar de almacenar directamente la función de onda cuántica —un dato imposiblemente grande—, los investigadores la comprimieron en una representación matemática mucho más eficiente.

El hallazgo no invalida la computación cuántica, sino que eleva el estándar de lo que se considera ventaja cuántica. Cada avance en algoritmos clásicos obliga al hardware cuántico a enfrentar problemas aún más duros, mientras que los avances en computación cuántica siguen inspirando métodos clásicos más inteligentes. Los resultados se publicaron en Science.

🔋 SÍGANOS

🧐 IBM dropped 26.1% between July 12 and 18 — its worst single-session collapse since Black Monday in 1987.

🤓 Why it fell

✅ Q2 revenue came in at $17.2 billion (compared to the expected $17.9 billion).

✅ CEO Arvind Krishna revealed that clients redirected their budgets to chips and servers ahead of the AI supply crunch.

✅ The debut of Anthropic’s “Mythos” AI model stirred corporate cybersecurity fears.

✅ Management admitted it adapted too slowly to the shift. IBM’s implied volatility hit its 99.6th percentile as panic selling drove a historic repricing.

🔍 What’s next? IBM now trades at roughly 17x its earnings with a well-covered 3.2% dividend yield, and its AI pipeline and quantum computing leadership remain intact.

💡 Historic swings and near-record volatility are ideal for short-term traders.

📊 Example FX trade on Olymptrade 💵 Trade amount: $30 ⚡ Leverage: 20x

If IBM moves 26.1%: $30 x 26.1% x 20 = $156.60 (+522%)

Will IBM bounce or keep falling?

👍 IBM will go up 👎 IBM will go down

🧠 An Ordinary Laptop Just Solved a Quantum Problem That Challenged a Quantum Computer

Last year, researchers using D-Wave’s quantum annealer argued that a particularly difficult quantum simulation was beyond the reach of classical computers.

Now physicists at the Flatiron Institute have shown otherwise.

Using tensor networks together with belief propagation—an algorithm first developed in the 1980s—they reproduced the same results using classical hardware, with some of the calculations running on a personal laptop.

The challenge involved simulating hundreds of interacting qubits arranged in complex 2D and 3D lattices. Instead of storing the impossibly large quantum wave function directly, the researchers compressed it into a far more efficient mathematical representation.

The work doesn’t diminish quantum computing. Instead, it raises the bar for what counts as quantum advantage. Every breakthrough in classical algorithms forces quantum hardware to tackle even harder problems—and advances in quantum computing continue to inspire smarter classical methods in return.

Paper (Science): https://www.science.org/doi/10.1126/science.adx2728

#QuantumComputing #Physics #TensorNetworks #Science

Coinbase Builds Post-Quantum Security as Industry Prepares for Future Threat

Coinbase is developing post-quantum custody technology and contributing engineers and funding to broader network security efforts, citing the need to prepare crypto infrastructure before quantum computing becomes a credible threat.

The exchange acknowledges that the timeline for quantum computers reaching the capability to break current cryptographic protections remains uncertain. Despite that uncertainty, Coinbase argues early preparation is essential — waiting until the threat is imminent would leave insufficient time to harden wallets, signing systems, and network-level security across the industry.

Quantum computers, if sufficiently advanced, could theoretically break the elliptic curve cryptography that underpins most blockchain security today. The move reflects a wider shift among security-focused institutions toward adopting post-quantum cryptographic standards before the threat materializes rather than after.

Source

Trade on Binance

⚛️ BITS पिलानी – Quantum Advantage

🏛️ संस्थान: BITS पिलानी 🤝 सहयोग: IBM Quantum 🏆 उपलब्धि: 120-क्यूबिट प्रोसेसर पर Quantum Advantage

📊 मुख्य तथ्य

⚛️ क्वांटम कंप्यूटर: 20 सेकंड 💻 पारंपरिक कंप्यूटर: लगभग 2 घंटे 🟢 QAT Status: Active

🌟 महत्व

🇮🇳 भारत का पहला QAT Active Quantum Advantage दावा

🔑 Quantum Computing

⚛️ Qubit (Bit से अधिक सक्षम)

1️⃣ Superposition – एक साथ कई अवस्थाएँ 2️⃣ Entanglement – क्यूबिट परस्पर जुड़े रहते हैं 3️⃣ Decoherence – बाहरी प्रभाव से क्वांटम अवस्था समाप्त होना

🤝 Crypto's biggest players are joining forces to strengthen Bitcoin's security

BlackRock, Coinbase, Fidelity, Strategy, and several other industry leaders have launched the Bitcoin Security Consortium. 🚀

The initiative will invest $15 million over the next three years to fund Bitcoin security research, support open-source developers, and improve the network's resilience—including preparations for potential quantum computing threats.

As institutional adoption accelerates, protecting Bitcoin's infrastructure is becoming just as important as expanding its adoption. This consortium marks another step toward strengthening the long-term security of the world's largest cryptocurrency. 🔒₿

This Week in Crypto: Bitcoin Rebounds, Institutional Access Expands & Galaxy Prepares for Quantum Risk

Market: BTC ~$65.9K | ETH ~$1.62K | Fear & Greed: ~37 (Fear)

📈 Bitcoin climbed above $66K this week, reaching its highest level since early June. U.S. spot Bitcoin ETFs also extended their run of positive flows to five consecutive trading sessions.

🇪🇺 CoinShares launched a new UCITS investment platform aimed at expanding regulated access to digital asset-related strategies in Europe. Its first product, the CoinShares Bitcoin Mining UCITS ETF, began trading on Deutsche Börse Xetra on July 21.

🛡 Galaxy launched its Bitcoin Quantum Readiness Initiative to support the development of protections against future quantum computing risks. The programme includes up to $5M in developer grants, dedicated research and a Quantum Advisory Council.

🏦 Citadel Securities invested $400M in Crypto.com, valuing the platform at $20B in its first institutional fundraising round. The capital is expected to support Crypto.com’s expansion across areas including tokenised securities and derivatives.

🔍 Pakistan’s Federal Investigation Agency has reportedly established a dedicated cryptocurrency investigation unit to examine suspicious digital transactions and crypto-related financial crime.

That’s this week’s #Tothemoon_Market_Update. See you next week!

💻 Classical Computers Just Overturned a Major Quantum Advantage Claim

In 2025, researchers used D-Wave’s Advantage2 quantum annealer to simulate the dynamics of hundreds of interacting qubits — and argued that the task was beyond the reach of classical computers.

A team from the Flatiron Institute and Boston University has now reproduced those results using classical algorithms and relatively modest hardware. Some of the initial calculations even ran on a personal laptop.

The challenge involved quantum spin glasses: disordered systems in which hundreds of interacting qubits evolve across two- and three-dimensional lattices. Tracking the complete quantum wave function directly would require an amount of memory that grows exponentially with the number of qubits.

The researchers avoided that explosion using tensor networks — mathematical structures that compress a quantum state by retaining its most important correlations.

Think of it as a highly specialized zip file for quantum information.

They combined tensor networks with belief propagation, an algorithm developed in the 1980s and recently adapted for quantum systems. The calculations were implemented using ITensor, a high-performance tensor-network software library.

The classical simulations:

• reproduced the quantum computer’s results • matched theoretical predictions and exact benchmarks • scaled to systems containing hundreds of qubits • captured quantum dynamics in complex 3D lattice geometries

There is one important caveat: the entire study was not completed on an ordinary laptop. A laptop was sufficient for some early calculations, while the largest simulations required more powerful conventional processors and GPUs.

Still, the result directly challenges the earlier claim that this particular problem was beyond classical computation.

It does not mean quantum computers are useless. It means that demonstrating genuine quantum advantage requires beating the best classical algorithms available — not merely the best ones researchers happened to test previously.

The boundary between classical and quantum computing is not fixed. Every improvement in quantum hardware gives classical researchers a new target, while every better classical algorithm raises the bar for quantum advantage.

Sometimes the strongest competitor to a quantum computer is not another quantum computer.

It is better mathematics.

📄 Paper: Dynamics of disordered quantum systems with two- and three-dimensional tensor networks https://www.science.org/doi/10.1126/science.adx2728

#QuantumComputing #Physics #TensorNetworks #ClassicalComputing #QuantumPhysics