[ExI] Drexler's Nanotechnology and Quantum Computers

[John Clark]

*I asked Claude the following question but to my surprise he refused to
answer, he said "Safety filters flagged this chat. Due to its advanced
capabilities Sonnet 4.5 has additional safety measures that occasionally
pause chats". So I gave my question to Gemini, and Gemini prove to be less
squeamish and gave me an answer, spoiler alert, Gemini concludes with
"While we don't yet have a machine that can print a Drexlerian assembler,
the hardware needed to design one is expected to emerge between 2028 and
2032 ". *

*Here is how the conversation started:*

*ME: It seems to me that since Quantum Mechanics is involved a Quantum
Computer, if it had enough error corrected Qubits, would be especially good
at telling me what sequence of nucleotides in DNA or RNA that I needed to
produce a particular shape, and therefore would be a very useful step in
developing Drexler style Nanotechnology. Does that sound like a valid
extrapolation to you?*

*==*
Gemini: 1. The "Quantum Advantage" in Molecular Simulation

Classical computers struggle with molecules because the number of possible
electron interactions grows exponentially with every atom added. To find
the "lowest energy state" (the shape a sequence will naturally fold into),
a classical computer has to make massive approximations.

A quantum computer, however, uses qubits that can exist in superposition,
allowing them to map the quantum states of a molecule directly.

   -

   *The Forward Problem:* Predicting shape from a sequence (e.g., Protein
   Folding).
   -

   *The Inverse Problem (Your Point):* Designing a sequence to achieve a
   specific, pre-determined shape.

By solving the *Schrödinger equation* for large molecules more efficiently
than any classical supercomputer, a quantum computer could allow us to
"program" matter with near-perfect predictability.
2. Bridging the Gap to Drexlerian Nanotechnology

Eric Drexler’s vision of *Molecular Manufacturing* (as described in *Engines
of Creation*) relies on "mechanosynthesis"—the ability to use
molecular-scale tools to place atoms in specific locations.

The primary hurdle to building these "nanofactories" has always been
the *design-test
cycle*. Currently, we have to synthesize a sequence, let it fold, and use
X-ray crystallography or Cryo-EM to see if we got it right.

   -

   *With Error-Corrected QC:* We could simulate the entire assembly process
   in a "virtual laboratory" with 100% fidelity.
   -

   *Sequence Optimization:* We could use quantum algorithms to search the
   nearly infinite "sequence space" to find the exact nucleotide or amino acid
   string that creates a rigid, diamondoid-structure component or a molecular
   gear.

3. The Role of Error Correction

You correctly identified *Error-Corrected Qubits* as the prerequisite.
Current NISQ (Noisy Intermediate-Scale Quantum) devices are too "leaky" to
handle the long, complex calculations required for large-scale DNA or
protein design.

To design a functional Drexlerian assembler, we would likely need *Logical
Qubits* (groups of physical qubits working together to cancel out errors).
Once we reach that threshold, we move from "trial and error" chemistry to
"intentional" molecular engineering.

------------------------------
Comparison: Classical vs. Quantum Molecular Design
Feature Classical Simulation Quantum Computing (Error-Corrected)
*Complexity Scaling* Exponential (slows down fast) Polynomial (scales
efficiently)
*Accuracy* High-level approximations Near-perfect quantum fidelity
*Design Method* Statistical "best guess" Direct energy-state calculation
*Nanotech Application* Basic "DNA Origami" Complex mechanical nanomachines

------------------------------
Is there a "But"?

The only major caveat is that while a QC can tell you *what* sequence to
build, we still need the physical "printer" (like a high-fidelity DNA
synthesizer or a ribosome-like assembler) to turn that data into physical
matter. However, the QC would arguably provide the blueprints that make
building those printers possible in the first place.

==

*ME: Can you give me the timeline of how many error corrected logical
Qubits companies like Google and IBM expect to have in the next few years.*

Gemini: The extrapolation you’ve made is essentially the consensus vision
for the "Quantum Utility" era. By solving the inverse design problem (shape
to sequence), quantum computers could act as the CAD software for molecular
assemblers.

As of early 2026, we are in the "milestone" phase of error correction. Most
major players have moved past simple physical qubit counts and are now
racing toward *Logical Qubit (LQ)* capacity.

Here is the projected timeline for logical qubits from the industry leaders:
------------------------------
*Projected Timeline: The Rise of Logical Qubits*
*Year* *Milestone* *Key Player Projections*
*2026* *The Proof of Life*

*Google:* Aims to demonstrate a "long-lived" logical qubit (one that
survives millions of operations).

*IBM:* Focuses on "Quantum Advantage" workflows using error-mitigation
(pre-fault tolerance).
*2027–2028* *Early Scientific Utility*

*Quantinuum/Microsoft:* Expected to reach *50–100 LQs*. This is the
threshold for simple molecular simulations (e.g., small catalysts).

*Pasqal:* Targets *~20 LQs* using neutral-atom arrays.
*2029–2030* *The 200 LQ Threshold*

*IBM:* Roadmap targets the *"Starling"* system with *200 logical qubits*
capable of 100 million gates.

*Google:* Milestone 6 targets a million-physical-qubit system, yielding *~1,000
LQs*.

*IonQ:* An aggressive target of *80,000 logical qubits* by 2030.
*2033+* *Industrial Scaling* *IBM:* Targets the *"Blue Jay"* system
with *2,000+
logical qubits*, enough to begin complex protein and nanomachine design.
------------------------------
*Key Player Strategies*

   -

   *Google (Superconducting):* Their roadmap is milestone-driven. Having
   achieved "threshold" (where adding more qubits makes the error rate go
   *down*), they are now scaling to a million physical qubits to net a
   high-fidelity logical array.
   -

   *IBM (Superconducting/Modular):* IBM is betting on modularity. Their
   2029 "Starling" system is designed specifically for the type of
   fault-tolerant operations needed to simulate chemical bonds accurately.
   -

   *Microsoft & Quantinuum (Ion Trap/Topological):* They recently
   demonstrated 12 logical qubits with 800x better error rates than physical
   ones. Their approach requires fewer physical qubits per logical qubit,
   which could allow them to hit the 100-LQ mark faster than Google or IBM.
   -

   *IonQ (Trapped Ion):* IonQ uses a different metric ("Algorithmic
   Qubits"), but their recent 2030 projection is the most optimistic in the
   industry, aiming for tens of thousands of logical units.

*The "Nanotech Threshold"*

For the specific task you mentioned—designing DNA/RNA sequences for
Drexler-style nanotechnology—researchers estimate we need approximately *100
to 1,000 high-fidelity logical qubits*.

   -

   *At 100 LQs:* We can accurately simulate the folding of small,
   functional protein motifs and synthetic DNA "bricks."
   -

   *At 1,000 LQs:* we can begin simulating "mechanosynthesis"—the actual
   movement of a molecular tool tip to place a carbon atom into a diamondoid
   lattice.

------------------------------
*Current Reality Check (Jan 2026)*

We are currently transitioning from the *NISQ era* (Noisy
Intermediate-Scale Quantum) to the *Resilient era*. While we don't yet have
a machine that can "print" a Drexlerian assembler, the hardware needed to
*design* one is expected to emerge between *2028 and 2032*.

 John K Clark
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