while this has been a subject of mild interest and i have discussed this with friends, i wanted to verify whether my conclusions are valid or outdated. so i asked deepseek if quantum computing is real. however, note the red highlights (mine) which is the difference between possibility and presently available. -------------------------------------------- Quantum computing is very real, but it’s still in its early experimental stages. While practical, large-scale quantum computers don’t exist yet, researchers have built small-scale prototypes that demonstrate the potential of quantum mechanics to solve problems that are intractable for classical computers. Here’s a breakdown of its current reality and real-world applications: Is Quantum Computing Real? Yes, but with caveats: Prototypes exist: Companies like IBM, Google, Rigetti, and IonQ have built quantum processors with dozens of qubits (quantum bits). For example: IBM’s Osprey (433 qubits, 2022) and Condor (1,121 qubits, 2023). Google’s Sycamore (53 qubits) achieved "quantum supremacy" in 2019 by solving a specific task faster than the world’s best supercomputer. Noisy and error-prone: Current quantum computers are NISQ (Noisy Intermediate-Scale Quantum) devices. They lack error correction and are prone to decoherence (losing quantum states). Specialized use cases: They’re not yet general-purpose machines but excel at specific tasks suited to quantum mechanics. Real Problems Solved (or Demonstrated) by Quantum Computers While quantum computers haven’t yet revolutionized industries, they’ve achieved proof-of-concept milestones: 1. Quantum Supremacy/Advantage Google’s Sycamore (2019): Solved a random circuit sampling problem in 200 seconds that would take a classical supercomputer ~10,000 years. This demonstrated that quantum devices can outperform classical ones for artificial (but mathematically well-defined) tasks. Chinese Jiuzhang (2020): Achieved quantum advantage using photon-based quantum computing for Gaussian boson sampling. 2. Chemistry Simulations IBM (2020): Simulated the molecular structure of lithium hydride (LiH) using a quantum computer, a step toward modeling complex molecules for drug discovery or materials science. Honeywell (2021): Modeled the Mayer potential (quantum interaction between two electrons) with high accuracy. 3. Optimization Problems D-Wave Systems: Used quantum annealing (a type of quantum computing) to solve optimization tasks like: Traffic flow optimization in Lisbon, Portugal (with Volkswagen). Protein folding research for medical applications. 4. Cryptography Factoring Small Numbers: In 2023, a Chinese team used a quantum algorithm to factor 21 using just 10 qubits. While trivial for classical computers, this demonstrated Shor’s algorithm (which could break RSA encryption if scaled up). 5. Machine Learning Quantum Machine Learning (QML): Small-scale experiments have shown speedups in tasks like: Classification: Differentiating between handwritten digits (MNIST dataset) using quantum neural networks. Pattern Recognition: Identifying features in data faster than classical methods. Key Limitations Today Qubit Count and Quality: Current quantum computers have ~50–1,000 qubits, but millions of error-corrected qubits are needed for practical problems. Error Rates: Noise and decoherence limit computation time. Cryogenic Requirements: Most quantum computers require near-absolute zero temperatures (−273°C) to operate. When Will Quantum Computers Solve Real-World Problems? Experts estimate 5–10 years for quantum computers to tackle impactful challenges like: Breaking RSA/ECC encryption (if large-scale, error-corrected quantum computers are built). Accurate molecular simulations for drug discovery (e.g., Alzheimer’s, cancer). Logistics optimization (e.g., global supply chains, airline scheduling). Quantum AI: Accelerating machine learning for climate modeling or financial forecasting. A Practical Example: Quantum vs. Classical Today Classical Supercomputer: Simulating a caffeine molecule (C₈H₁₀N₄O₂) requires ~10^48 operations and is infeasible. Future Quantum Computer: Could simulate it in hours, unlocking new materials or medicines. The Bottom Line Quantum computing is real but embryonic. While today’s devices are scientific curiosities, they’ve proven that quantum mechanics can solve problems beyond classical reach. The next decade will determine whether they evolve into practical tools or remain niche instruments. For now, they’re primarily used for: Researching quantum algorithms. Exploring physics (e.g., quantum entanglement). Preparing industries for a post-quantum cryptography era. ----- let us see.
This reminds me of how they struggle to explain (or can’t explain) the double-slit experiment. It appears to be an interesting read. Thanks for sharing Maulana.
Recent leap in AI technology has made quantum computing feel less like a sci-fi fantasy and more like something achievable in our lifetime. It’s exciting to think about how this tech could reshape industries. Banking, social security, energy, weather prediction, and even pharma. The potential to change the entire scientific landscape is massive. But let’s be real! this isn’t just about progress. It’s also about power. States, corporations, and influential groups like BlackRock and Rothschilds won’t hesitate to use quantum computing to tighten their grip. I don’t expect all the benefits of this technology to be shared with the public. Instead, it might be leveraged to systematically control people, just like we've seen with other disruptive technologies. Look at what's happening with AI. People like Peter Thiel are already using advanced AI models for military and strategic applications, tech that’s far ahead of what’s available to the public. It’s a sobering reminder.
tell a 'scientist' that God is transcendent and he/she will make fun of you religious fools. but they will claim anything in the name of science. https://bigthink.com/13-8/quantum-uncertainty-language/ --- related: https://physicsdetective.com/quantum-entanglement-is-scientific-fraud/ https://www.scientificamerican.com/article/quantum-physics-isnt-as-weird-as-you-think-its-weirder/ https://www.newtonphysics.on.ca/faq/qm_why.html http://backreaction.blogspot.com/2019/05/quantum-mechanics-is-wrong-there-ive.html (a viewpoint on sabine: https://www.reddit.com/r/QuantumCom..._are_your_opinions_on_dr_sabine_hossenfelder/) https://arxiv.org/html/2409.10601v1 =========== the reason why i insist on learning reasoning is because of the extreme nonsense spewed in the name of science. hypotheses upon hypotheses based on conjecture ad nauseum and presented as cutting edge 'research'. you can keep asking valid questions on those hypotheses, conjectures, assumptions, artificially introduced constants - and you will see why this is nonsense. do nto be afraid to be laughed off as stupid, or ignorant or old-fashioned or whatever pejorative they may hurl at you. i demonstrated one such argument from dawkins: https://sunniport.com/index.php?threads/evolution-occurring-right-now.12649/#post-54627 i stumbled on a few relevant threads: https://sunniport.com/index.php?threads/meaning-of-life.14760/#post-73399 https://sunniport.com/index.php?threads/the-chimera-of-objectivity.14053/#post-71471 --- kuch kati himmat e su'aal mein umr kuch ummeed e jawaab mein guzri...
Interestingly, I too had this same question (as did many others). What exact problem is this supposed to solve? Here, the founder of Google's Quantum AI Department briefly touched upon this topic. As per their explanation, they created a mechanism to reduce the error rate (by developing tunable qubits) and increase the number of qubits to 105. (In comparison, China currently has a maximum of 504 qubits.) To replace current supercomputers, quantum computers need a higher quantum coherence time (which has been increased to 40ms), a higher qubit count, reduced error rates, and a way to function in a more natural environment rather than an absolutely controlled lab setting. It seems there is still a long way to go.
https://www.swissinfo.ch/eng/sci-te...-explainer-to-the-laws-of-the-absurd/48275520 FTA: “The impressive power of human intelligence has managed to combine observation of nature with mathematical constructions,” said Nicolas Gisin In a quantum processor, information is stored on particles, which become qubits. However, these are very unstable and generate a lot of errors. To function properly, the machine needs to be completely protected from any vibrations, electric or magnetic fields and light sources. It must also be placed in a super freezer, since its ideal operating temperature is close to absolute zero (-273°C). It is only at these temperatures that the particles remain still enough to be “manipulated”.
this is from IEEE. skeptics. some of these are dated articles, but useful to understand the challenges. https://spectrum.ieee.org/the-case-against-quantum-computing https://spectrum.ieee.org/quantum-computing-skeptics https://gilkalai.wordpress.com/2022/05/26/waging-war-on-quantum/ https://www.techrxiv.org/users/6825...y-we-should-be-skeptical-of-quantum-computing https://www.quantamagazine.org/the-argument-against-quantum-computers-20180207/ https://unfashionable.blog/p/quantum/ PDF paper: https://arxiv.org/pdf/2009.05045 https://physics.stackexchange.com/q...ogles-quantum-supremacy-experiment-impressive and oh yeah, quantum entanglement. i marvel at how hard-evidence demanding physicists will believe n number of hypotheses and iterations! for example, if a religious muslim told you about how evil eye is true - they will scoff and fall of their chairs laughing. but the next moment, talk about quantum entanglement and they will speak eloquently on how something far away can have an effect without any physical connection - but then, their claims are backed by a lot of math, so it should be true isn't it? --- amazing how scientists, in their zeal to prove their point, lose their grip on reasoning. he deals with fallacies in order to 'refute' fallacies. sigh. https://www.scottaaronson.com/democritus/lec14.html https://scottaaronson.blog/?p=8329
i read a lot of hype infested 'reports' about how google's willow solved a problem in 5 minutes that would take a current supercomputer 10^25 years. but what exactly was the problem? or is it just another of those projections. for so-called exactness of 'science' they talk in "could" and "cans" and "what it would take" but why not clearly say: this is what we did. https://quantumcomputingreport.com/...quantum-error-correction-below-the-threshold/ https://blog.google/technology/research/google-willow-quantum-chip/ read the blog about - there is so much of bragging and useless information and results, but precious little about the actual problem itself. perhaps it is a trade secret. ibm and other players will challenge it very soon. again, the nature of the problem, the problem itself, and measurement of accuracey is very very important because - unlike classical systems, quantum systems has a lot of airs like a spoilt daughter of a billionaire. it doesn't behave as you want to and then there is the quantum entanglement fear [personally, i am skeptical about all of this - and similar to altman's claims about AI. but then, it is heresy to say it aloud. but see a follow up post on quantum computing skeptics. our old fashioned training in classical math and logic doesn't add up with these claims...] --- for those who are new, a similar claim was made 3 years ago about google sycamore, and this was based on a set of assumptions. https://research.google/blog/quantum-supremacy-using-a-programmable-superconducting-processor/ and then there is the benchmark - which is a misnomer because it is not a straightforward measurement. https://research.google/blog/valida...s-a-benchmark-for-measuring-quantum-progress/ ==== so if anyone can get hold of a paper or a write-up on the actual problem was, please share. am curious to know.
