Quantum-AI cannot cure cancers

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Quantum computer is useless
AI is useless for cancers.
Quantum chemistry-AI is useless

Overhyped quantum-AI-cancer research just used a classical computer and experimental data (= still No effective cancer drugs ) with No quantum computation nor quantum mechanical prediction.

(Fig.1)  Quantum computers and AI are useless for cancers

Quantum computer AI cannot cure cancers, contrary to hypes.

Quantum computer is useless, too error-pone, cannot factor even 21, much less discover cancer drugs, contrary to overhyped fake news.

The recent overhyped fake news falsely claimed a quantum computer with fantasy parallel universes, which still cannot even factor 21, might help developing cancer drugs together with AI.

Actually, this overhyped research heavily relied on a classical computer and experimental molecular data with No quantum computation nor quantum mechanical prediction.

Quantum computer, AI are useless for drug discovery.

This research used deceptive hybrid computer, which is just a classical computer to try to find a molecule inhibiting a mutated KRAS cancer-related protein with No quantum computer advantage.

↑ This same research news on hyped quantum computer AI for cancer drug ↓

2nd-paragraph says  -- Classical computer needed
"The study, published in Nature, demonstrated how combining quantum and classical computational tools could enhance the design of potential drugs for KRAS, a protein.. implicated in various cancers,"

8th-paragraph says  -- QCBM = fake quantum computer
"hybrid classical–quantum approaches.. quantum circuit Born machines (QCBMs)..QCBMs are quantum generative models that leverage quantum circuits to learn complex probability distributions, enabling them to generate new samples that resemble the training data."

10th-paragraph says  -- Using experimental data, No quantum mechanics
"The team first compiled a dataset of 1.1 million molecules, starting with 650 known KRAS inhibitors from the literature. They expanded this set by screening 100 million compounds from a commercial library"  ← Relying on already-known experimental data means No quantum mechanical prediction

11th-paragraph says  -- Useless 16-qubit quantum computer
"The hybrid model combined a quantum circuit-based generative model with a classical machine learning network. A 16-qubit quantum processor (= one qubit can take only 0 or 1, so 16-qubit is still Not a quantum computer ) generated a prior distribution of molecules, which the classical network refined into viable candidates (= classical computer correction was needed )."

15th-paragraph says  -- No quantum computer advantage
"The team stops short of saying this study proves a quantum advantage– achieving results unattainable by classical methods. The model’s success depended on a hybrid approach, suggesting that quantum computing alone is not yet sufficient for drug discovery tasks."

Cancer drugs against KRAS already failed.

This research tried to develop drugs against KRAS cancer proteins that already exist and proved to be ineffective.

This research wasted time in trying to find drugs inhibiting the mutated KRAS cancer-related protein that already existed, and have been proven ineffective ↓

This-5th-paragraph says  -- Already-failed cancer drugs
"Mutations in KRAS drive uncontrolled cell growth and are present in about one in four human cancers, but despite their prevalence and impact, there are currently only two FDA-approved drugs that specifically target mutant KRAS. Moreover, clinical data show existing drugs extend life by only a few months compared to traditional chemotherapy (= KRAS cancer protein drugs already proved to be ineffective )"

Hybrid quantum computer for cancer drug is fake, just a classical computer tried to find molecules inhibiting KRAS cancer proteins based on experimental training data with No quantum computation nor quantum mechanical prediction.

↑ This or this research paper ( this-2nd-paragraph-link ) on quantum computer AI ↓

p.1-right says  -- hybrid = classical computer
"hybrid classical–quantum approaches (= just a classical computer ).. quantum circuit Born machines (QCBMs).. are quantum generative models that leverage quantum circuits to learn complex probability distributions, enabling them to generate new samples that resemble the training data"

p.2-Fig.1-left (or upper or this ) says  -- trained on experimental data
"training dataset, starting with 650 experimentally verified KRAS inhibitors sourced from the literature"
"the dataset was used to train our generative model, consisting of both a classical LSTM network and a QCBM"

p.2-Fig.1-right (or lower ) says  -- Quantum simulator = classical computer
"while the QCBM, trained on the output from the LSTM (= classical computer )"
"Workflow for KRAS inhibitor design,.. A total of 1 million compounds (classical samples from the LSTM, quantum samples from QCBM on quantum hardware and simulated quantum samples on classical hardware) were evaluated by Chemistry42 to filter out unsuitable candidates"

Classical computer corrected and beat useless quantum computer.

Based on experimental data, classical computers had to correct the useless quantum computer's results that were outperformed by a classical computer simulator with No quantum computation nor advantage.

↑ The same fake quantum-AI-cancer research paper ↓

p.3-left-1st-paragraph says  -- Classical computer corrected quantum results
"(1) the QCBM using a 16-qubit (= still Not a quantum computer ) processor to generate a prior distribution;"
"a QCBM that generated samples from quantum hardware in every training epoch and was trained,, using Chemistry42 (= classical computer's software ) or a local filter."  ← A classical computer solftware = Chemistry42 had to correct the quantum computer's results by experimental data with No quantum computation.

p.14-Extendend Data Fig.1-(A)(B) (or this ) shows  -- Classical simulator beat quantum hardware
Quantum simulator QCBM (SIM ) run on a classical computer (= Fig.1d ) had higher success rate (= SR ) than the error-prone quantum computer's hardware QCBM (HW) as shown in this-p.41-S3.2-Supplementary Table 2 both in tests by local and chemistry42 filters

p.16-Extended Data Fig.3(c) says  -- Classical computer optimized parameters
"Quantum prior component described as a QCBM, generating samples from quantum hardware each training epoch and trains with a reward value,.. calculated using Chemistry42 or a local filter (= classical computer optimizer updated training parameters θ instead of the useless quantum computers )"

A classical computer was needed for training.

The useless quantum computer's just 16 qubits were flipped based on a classical computer optimizer and experimental datasets with No quantum computation nor quantum mechanical prediction.

↑ So in this research on deceptive hybrid quantum-classical computer AI for cancer drug, they used the already-known experimentally-obtained molecular data inhibiting KRAS cancer proteins to train a classical computer's optimizer (= which classical optimizer had to iteratively corrected the error-prone 16-qubit quantum computer results ) through classical compuer AI software called Chemistry42.

↑ So the 16-qubit fake quantum computer is irrelevant to overhyped AI training that was conducted by a classical computer optimizer and classical solfware (= chemistry42 ), and relying on experimental molecular ligand data means the useless quantum mechanics cannot predict anything.

Classical computer beat quantum computer.

Success rates of a quantum simulator run on a classical computer (= SIM ) were higher than the error-prone quantum computer hardware (= HW ).

↑ When the useless 16-qubit error-prone quantum computer hardware (= HW ) was replaced by quantum simulator (= SIM ) run on an errorless classical computer, the success rates (= SR ) of predicting KRAS inhibitors based on experimental training data were higher in the quantum simulator (= classical computer ) than the error-prone quantum computer hardware with No quantum computer advantage ( this-p.41-S3.2-Table 2 ).

As a result, the overhyped quantum computers are useless forever.  The quantum-AI must be conducted by a classical computer that also cannot discover cancer drugs due to the current useless quantum mechanical theory.

Quantum mechanics, overhyped AI still cannot make useful discovery in all fields including chemistry.

Quantum mechanics is useless, cannot solve its Schrodinger equations nor predict any multi-electron atoms, much less predict any chemical reactions.

So the overhyped AI and machine-learning try to predict chemical reactions based on training datasets obtained from experiments Not from (useless) quantum mechanical prediction.

But this AI and machine-learning are also useless, cannot make meaningful discovery.

This or this or this-2nd-paragraph says  -- Useless AI
"AI has Not yet gone far enough. One of those areas is chemistry, for which machine-learning tools promise a revolution in the way researchers seek and synthesize useful new substances. But a wholesale revolution has yet to happen — because of the lack of data available to feed hungry AI systems."

Unreal quantum mechanics prevents obtaining useful datasets for AI.

AI, machine-learning are useless because unreal quantum mechanical atomic model prevents scientists from obtaining useful atomic datasets based on atomic force microscopes.

Chemists still rely on old macroscopic experimental procedure without directly manipulating single atoms by atomic force microscopes whose use is prevented by unreal quantum mechanical atomic model lacking shape.

So scientists cannot get new useful experimental datasets that enable them to clarify detailed atomic mechanisms of chemical reactions for practical nano-devices, which keeps AI and machine-learning impractical ( this-figure-middle~lower ).

This or this-lower-challenges and considerations say  -- No datasets
"While AI offers tremendous potential, it's not without challenges. Some key considerations include:
Data Quality: AI models rely on high-quality, well-curated datasets. Poor data can lead to inaccurate predictions."

Overhyped quantum chemistry-AI research

Quantum mechanics cannot solve its Schrodinger equation nor predict any multi-electron atomic energies.

This or this overhyped news on quantum chemistry-AI (7/2026) ↓

2nd-paragraph says  -- Useless quantum mechanics DFT
"The researchers utilized an innovative combination of density functional theory (DFT) and machine learning (ML) to systematically investigate Fe-N-C single-atom catalysts, which serve as earth-abundant, highly promising alternatives to traditional platinum-based catalysts."

The last-paragraph says  -- still No practical use
"This synergistic application of computational chemistry and artificial intelligence precisely unravels the complex interactions within dual-modified single-atom catalysts, paving the way (= still No detailed practical use ) for the development of cheaper, more efficient hydrogen fuel cells."

↑ Quantum mechanics is useless, cannot solve its Schrodinger equations nor predict any multi-electron atomic energies, much less predict catalysts.
Still relying on these impractical old quantum mechanical methods such as DFT shows AI is also impractical.

Quantum mechanical methods are useless.

Useless Schrodinger equations that cannot predict anything make scientists use impractical quantum mechanical DFT approximation treating the whole material as one fake electron, which is also useless, cannot predict anything.

Due to the impractical Schrodinger equation, physicists have to rely on rough incorrect quantum mechanical approximation treating the whole material as one fake electron density functional theory (= DFT ) model with artificially-chosen pseudo-potentials called exchange-correlation energy functionals that are also useless, cannot predict anything.

This or this-Schrodinger and theoretical chemistry-3rd-paragraph says
"However, a precise solution for the Schrödinger equation can only be calculated for hydrogen; because all other atomic, or molecular systems, involve three or more particles, their Schrödinger equations cannot be solved exactly"

Useless quantum mechanics cannot predict anything.  AI is also useless due to lack of useful experimental datasets

This or this or this overhyped news (2026) ↓

11th-paragraph says  -- Impractical quantum mechanics
"In these cases, the simplified approach of DFT or Hartree-Fock breaks down, and more sophisticated methods are needed. As the number of possible electron configurations increases, we quickly reach an exponential wall in computational complexity, beyond which classical methods (= quantum mechanical methods ) become infeasible."

17th-paragraph says  -- AI relies on experimental data
"However, these AI models are only as good as the quality and diversity of their training data.... these data must accurately represent the underlying physical phenomena to ensure reliable predictions. Poor or biased data can lead to misleading outcomes"

3rd-last-paragraph says  -- Quantum-AI is hyped, useless
"By now, you’re probably wondering: When will this transformative future arrive ? It's true that quantum computers still struggle with error rates and limited lifetimes of usable qubits. And they still need to scale to the size required for meaningful chemistry simulations."

↑ All these overhyped AI, quantum mechanics, quantum computers are impractical pseudo-science just worsening inflation now.

Some classical methods of organizing cancer patients' data were misleadingly treated as (unreal) quantum mechanical superposition or faster-than-light entanglement.

This or this overhyped news on quantum-AI improving cancers (2026) ↓

3rd-paragraph says  -- Useless AI
"Current artificial intelligence and machine learning (AI/ML) approaches require massive amounts of training data, and, specifically, vastly more patient samples than genetic features. This makes them poorly suited for predicting patient outcomes in most clinical trials"

6th-paragraph says  -- No quantum mechanics
"The technique.. which Alter built on the quantum mechanical concepts of entanglement and superposition... this approach breaks down a patient's multiple layers of molecular data—such as their tumor and blood genomes and tumor transcriptome (or the RNA messages driving the cancer's growth)—into linked patterns that predict health outcomes."

↑ This research used Not quantum mechanical calculations such as (useless) Schrodinger equations but the misleading "quantum-inspired" method of analyzing patients' data.

Just using some data-sorting methods in meaningless analogy to quantum mechanics.  No real quantum superposition nor entanglement was used for cancers.

↑ So their quantum mechanical superposition and entanglement do Not mean the real quantum mechanical dead-and-alive cat nor faster-than-light spooky action, but just mean some (classical computer's) way of sorting out the existing experimental data (with meaningless analogy with quantum mechanics ) to predict some cancers' patients' outcomes.

This or this-lower-Side bar says  -- No quantum mechanics
"Superposition: A quantum physics principle in which a system can exist in multiple states at once. In this algorithm, it means a patient's molecular data can reflect multiple overlapping patterns simultaneously, and the method identifies which combination of patterns best explains their clinical outcome. (= Not real quantum superposition )"

"Entanglement:.. In this algorithm, it means patterns discovered in one data layer (eg, blood DNA) are mathematically tied to patterns in another (eg, tumor RNA), allowing the layers to inform each other. (= Not real quantum entanglement spooky link )"

↑ Actually, AI (and quantum mechanics ) is useless, cannot develop effective drugs ( this or this-1st-paragraph ).

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