Quantum-AI cannot cure cancers

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Quantum computer is useless
AI is useless for cancers.

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 ) 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) 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.

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