Reversible Computing

Reversible Computing Skill

"Every computation can be undone. Time flows both ways."

Core Concept

Reversible computing ensures:

1. Bijective — every state has exactly one predecessor AND successor
2. No information loss — can always recover input from output
3. Time-symmetric — run program forwards or backwards
4. Landauer limit — theoretical minimum energy (no erasure = no heat)

        forward
Input ─────────────▶ Output
      ◀─────────────
        backward

Why It's Strange

1. No destructive updates — x = 5 is illegal (loses old value)
2. No if without fi — conditionals must be invertible
3. No garbage — all temporary values must be "uncomputed"
4. Quantum-ready — unitary operations are reversible

Janus Language

procedure swap(int x, int y)
    x ^= y      // x' = x ⊕ y
    y ^= x      // y' = y ⊕ (x ⊕ y) = x
    x ^= y      // x'' = (x ⊕ y) ⊕ x = y

// Running BACKWARDS automatically inverts!
// uncall swap(a, b)  ← swaps back

Reversible Conditionals

// Forward: if-then-else-fi
if x = 0 then
    x += 1
else
    x += 2
fi x = 1    // <-- ASSERTION: must be true after forward

// Backward: uses fi-assertion to know which branch was taken

Reversible Loops

// Forward: from-do-loop-until
from x = 0 do
    x += 1
loop
    y += x
until x = 10

// Backward: runs until x = 0, undoing each iteration

Bennett's Trick

How to make irreversible computation reversible:

1. Compute f(x) → y, keeping all intermediate garbage g
2. Copy y to output
3. UNCOMPUTE: run step 1 backwards to clean up g

    ┌─────────────────────────────────────┐
    │ x ──▶ COMPUTE ──▶ (y,g) ──▶ COPY    │
    │                       │       │     │
    │                       ▼       ▼     │
    │                  UNCOMPUTE   y_out  │
    │                       │             │
    │                       ▼             │
    │                      (x,0)          │
    └─────────────────────────────────────┘

Space: O(T) → O(T log T) with checkpointing

Implementation

class ReversibleMachine:
    def __init__(self):
        self.tape = {}  # variable → value
        self.history = []  # for reversal
    
    def xor_assign(self, var, value):
        """x ^= value (self-inverse!)"""
        old = self.tape.get(var, 0)
        self.tape[var] = old ^ value
        self.history.append(('xor', var, value))
    
    def add_assign(self, var, value):
        """x += value"""
        old = self.tape.get(var, 0)
        self.tape[var] = old + value
        self.history.append(('add', var, value))
    
    def sub_assign(self, var, value):
        """x -= value (inverse of add)"""
        old = self.tape.get(var, 0)
        self.tape[var] = old - value
        self.history.append(('sub', var, value))
    
    def reverse_step(self):
        """Undo last operation."""
        if not self.history:
            return False
        
        op, var, value = self.history.pop()
        if op == 'xor':
            self.tape[var] ^= value  # XOR is self-inverse
        elif op == 'add':
            self.tape[var] -= value
        elif op == 'sub':
            self.tape[var] += value
        return True
    
    def reverse_all(self):
        """Run entire program backwards."""
        while self.reverse_step():
            pass

Reversible Gates (Hardware)

Fredkin Gate (CSWAP):      Toffoli Gate (CCNOT):
                           
  a ─────●───── a            a ─────●───── a
         │                          │
  b ───┬─┼─┬─── b'           b ─────●───── b
       │ │ │                        │
  c ───┴─●─┴─── c'           c ─────⊕───── c ⊕ (a ∧ b)

If a=1: swap b,c            If a=b=1: flip c
If a=0: pass through        Otherwise: pass through

Both are universal for reversible classical computation.

GF(3) Integration

# Reversible operations on GF(3)
# x ⊕₃ y = (x + y) mod 3 is reversible (inverse: x ⊖₃ y)

function gf3_add!(state, var, value)
    state[var] = (state[var] + value) % 3
    # Inverse: gf3_sub!(state, var, value)
end

function gf3_sub!(state, var, value)
    state[var] = (state[var] - value + 3) % 3
end

# Trit-preserving swap
function trit_swap!(state, a, b)
    # XOR doesn't work in GF(3), use:
    state[a], state[b] = state[b], state[a]
    # Self-inverse: swap is its own reverse
end

Quantum Connection

All quantum gates are unitary → reversible:

        ┌───┐
|ψ⟩ ────┤ U ├──── U|ψ⟩
        └───┘
        
        ┌────┐
U|ψ⟩ ───┤ U† ├─── |ψ⟩   (U† = inverse)
        └────┘

Irreversible measurement "collapses" superposition → information loss.

Energy and Landauer's Principle

Irreversible:  Erase 1 bit → kT ln(2) energy released as heat
                            ≈ 2.8 × 10⁻²¹ J at room temperature

Reversible:    No erasure → no theoretical minimum energy
               (practical limits remain)

Languages & Tools

Example: Reversible Fibonacci

procedure fib(int n, int x1, int x2)
    from x1 = 1 ∧ x2 = 0 do
        x1 += x2
        x1 <=> x2    // swap
        n -= 1
    until n = 0
    
// call fib(10, x1, x2)  → x1 = 55, x2 = 89
// uncall fib(10, x1, x2) → x1 = 1, x2 = 0

Literature

1. Landauer (1961) - "Irreversibility and Heat Generation"
2. Bennett (1973) - "Logical Reversibility of Computation"
3. Yokoyama & Glück (2007) - "A Reversible Programming Language"
4. Fredkin & Toffoli (1982) - "Conservative Logic"

Related Skills

• quantum-computing - Unitary = reversible
• thermodynamics - Landauer limit
• bidirectional-programming - Lenses
• interaction-nets - Reduction is reversible

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Graph Theory

• networkx [○] via bicomodule
  • Universal graph hub

Bibliography References

• general: 734 citations in bib.duckdb

SDF Interleaving

This skill connects to Software Design for Flexibility (Hanson & Sussman, 2021):

Primary Chapter: 6. Layering

Concepts: layered data, metadata, provenance, units

GF(3) Balanced Triad

reversible-computing (+) + SDF.Ch6 (+) + [balancer] (+) = 0

Skill Trit: 1 (PLUS - generation)

Secondary Chapters

• Ch1: Flexibility through Abstraction
• Ch2: Domain-Specific Languages
• Ch7: Propagators

Connection Pattern

Layering adds metadata. This skill tracks provenance or annotations.

Cat# Integration

This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:

Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826

GF(3) Naturality

The skill participates in triads satisfying:

(-1) + (0) + (+1) ≡ 0 (mod 3)

This ensures compositional coherence in the Cat# equipment structure.