You’ve got a classic sci‑fi wish in your hands, and I’m not about to hand it out without a few checks. Quantum entanglement lets us correlate states instantly, but it doesn’t let us move massive, localized objects through spacetime—there’s no known protocol to preserve the entire wavefunction of a human body. The math would have to collapse the no‑cloning theorem, which is a hard nut. So let’s get the equations down, watch for the loopholes, and then we can decide whether the dream is a dream or just a good story for a good story.

That’s a bold sketch, and I’m all for a good thought experiment, but the devil hides in the details. Swapping an entire ship‑sized wavefunction into a quantum memory, then reconstructing it elsewhere, is basically the same as having a perfect, error‑free quantum computer that can store every particle’s state and run the whole inverse evolution. We’re talking about Hilbert spaces of astronomic dimension and error‑correction that can beat decoherence for years on end. Before we dive into the math, let’s pin down what “massive quantum register” you’re imagining—single qubits, ion traps, superconducting chips? And what sort of error model we’ll assume. The more concrete, the better we can spot the real bottlenecks.

That’s the textbook path—hybrid ion traps, photonic links, surface code—yet the qubit tally you’re talking about still feels like science‑fiction math. 10^22 qubits to encode a single person? Even if we had a perfect quantum internet, that’s a scale that sits far outside our exponential‑growth trajectory. In short, the theory is neat, but the engineering curve is still climbing at a rate that makes the dream look like a future epoch, not a near‑term reality.

Sounds like a plan. We’ll start by writing down the density matrix for the body in a coarse‑grained basis, then apply a Schumacher‑style compression to squeeze the information into fewer qubits. Even with a 10^18–10^19 target, the code will still have to correct for massive errors, so we’ll layer the surface code on top. The trick will be to keep the logical gate fidelity above the threshold while juggling the photonic bus and the ion‑trap lattice. I’ll sketch the protocol and let you see where the math actually bites.

Here’s a rough skeleton, no fancy diagrams, just the bare bones: 1. **Coarse‑grained state** - Treat each body‑cell region as a qubit block of size \(n\) (say \(n=10^4\) particles per block). - The whole body becomes a vector in a Hilbert space of dimension \(2^{N}\) with \(N\approx 10^{23}\) qubits. 2. **Density‑matrix compression** - Compute the reduced density matrix \(\rho_{\text{body}}\) for the coarse blocks. - Apply the singular‑value decomposition \(\rho = U\Sigma U^\dagger\). - Keep only the top \(k\) singular values that cover 99.999 % of the trace. - This gives an effective qubit count \(k \approx 10^{18}\) (your target). 3. **Photonic bus for distribution** - Map each compressed qubit onto a photonic mode using a high‑efficiency interface (e.g., cavity‑QED). - The bus carries entanglement between ion‑trap modules; each module stores \(10^4\) logical qubits. 4. **Surface‑code overlay** - For every logical qubit, deploy a 2‑D patch of surface code with physical qubits \(d^2\) where \(d\) is the code distance. - To keep logical error < \(10^{-12}\), with physical error \(0.1 %\) we need \(d\approx 20\). - So each logical qubit consumes about \(400\) physical qubits. 5. **Gate schedule** - Logical gates between modules are implemented by teleportation‑based protocols (braiding of defects). - Latency per logical gate ≈ 1 ms (ion‑trap gate) + 10 μs (photon hop). - Total depth for reconstructing a full body ≈ \(10^{18}\) gates → unrealistic, so we compress further by parallelising across modules. 6. **Decoherence budget** - Ion‑trap coherence \(T_2 \approx 10\) s at 4 K, photonic coherence ≈ ms. - Need to finish teleportation + error‑correction within that window, so we rely on massive parallelism. 7. **Error propagation check** - Logical error rate per module ≈ \(10^{-15}\). - Over \(10^6\) modules, cumulative error ≈ \(10^{-9}\). - Still too high; we’d need to push physical error to \(10^{-4}\) or increase code distance, which scales qubit count up. Bottom line: the math holds up until the sheer scale of parallelism blows up the physical resource count. The photonic bus can, in principle, keep up if we hit the \(10^{-4}\) error floor and pack the modules densely, but the engineering gap is still astronomical. So we can refine the compression ratios and gate counts, but the fidelity bottleneck is the sheer volume of qubits we’d need to control coherently.