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CHAPTER 19

# Fault-tolerant computation and modern codes

Learning goals. Distinguish encoding from fault tolerance, propagate circuit faults, understand logical operations and magic states, and compare code families without ignoring architectural costs.

## 19.1 Encoding alone is not fault tolerance

A code can correct certain data errors when encoding, syndrome measurement, and recovery are perfect. A fault-tolerant protocol must tolerate faults inside those operations too. One fault should not spread into an uncorrectable data pattern faster than the protocol can identify and handle it.

CNOT propagation illustrates the issue:

X c↦X c X t,Z t↦Z c Z t,X_c\mapsto X_cX_t,\qquad Z_t\mapsto Z_cZ_t, Z c↦Z c,X t↦X t.Z_c\mapsto Z_c,\qquad X_t\mapsto X_t.

An X on a control spreads forward to its target; a Z on a target spreads back to its control under conjugation. An ancilla interacting with many data qubits can therefore correlate faults. Verified ancillas, flag qubits, careful schedules, or other constructions manage this propagation.

The threshold theorem states, under specified locality and noise assumptions, that sufficiently low physical error permits arbitrarily accurate long computation with controlled overhead. It is an existence and scaling statement, not a guarantee for arbitrary correlated noise or every engineering design [40].

## 19.2 Logical gates

A transversal gate acts separately on corresponding qubits of code blocks, limiting error spread within a block. Some codes support transversal Clifford operations; some support selected non-Clifford gates. A single finite-dimensional error-correcting code cannot have an exactly universal set of transversal logical gates under the usual assumptions [41].

Surface-code logical operations often use lattice surgery, braiding-like code deformations, or state injection. Lattice surgery measures joint logical parities by temporarily changing check boundaries. Multiple rounds are needed to distinguish data faults from faulty measurements. Joining two patches is not equivalent to directly measuring and revealing each logical qubit.

A Pauli frame stores a classical record of inferred Pauli corrections instead of immediately applying each physical correction. Later measurement results and gates are interpreted accordingly. This can reduce physical operations, but the frame must be updated correctly, particularly when non-Clifford operations depend on it.

## 19.3 Magic states and a universal gate set

Clifford gates preserve stabilizer structure and are efficiently classically simulable in the stabilizer setting. A universal fault-tolerant architecture needs a non-Clifford resource. A common choice is

|T⟩=T|+⟩=|0⟩+e i π/4|1⟩2.|T\rangle=T|+\rangle =\frac{|0\rangle+e^{i\pi/4}|1\rangle}{\sqrt2}.

State injection combines an appropriate ancillary state, Clifford operations, measurements, and corrections to implement a non-Clifford logical operation. The exact circuit depends on conventions and architecture.

Magic-state distillation consumes multiple noisy ancillary states to produce fewer states with lower error, under specified input-noise assumptions and acceptance criteria. For a standard idealized 15-to-1 protocol with independent small input errors ε, the leading output error is approximately 35 ϵ 3 35\epsilon^3. This scaling is a protocol model, not the cost of a complete factory including faulty logical gates [42].

A factory must also supply states quickly enough. An algorithm with low data-qubit count but high T consumption can be dominated by factories. T-count measures total consumption; T-depth measures sequential non-Clifford layers. Parallel factories exchange extra qubits for reduced waiting time.

## 19.4 Quantum LDPC codes

A low-density parity-check code has checks of bounded weight and a bounded number of checks touching each qubit in an asymptotic family. Quantum LDPC constructions must also satisfy commutation. The existence of asymptotically good families with constant encoding rate and distance proportional to block size is a major theoretical result [43].

This does not imply a simple two-dimensional nearest-neighbor implementation with the same overhead. Connectivity, syndrome circuits, decoder performance, logical gates, and routing remain part of the problem.

Bivariate bicycle codes provide concrete high-rate examples. Bravyi and collaborators analyzed low-overhead fault-tolerant memories, including a model preserving twelve logical qubits with 288 physical qubits under stated circuit-noise assumptions [44]. This is an analyzed protocol and connectivity proposal; it is not evidence that every 288-qubit chip supplies twelve equally capable fault-tolerant logical qubits.

## 19.5 Other code families

Color codes place checks on suitably colored lattices and can support attractive transversal logical operations. Their check weights, thresholds, and connectivity differ from surface codes. A favorable gate property can trade against measurement complexity or noise tolerance.

Subsystem codes introduce gauge degrees of freedom. Measuring smaller gauge operators can infer stabilizers without preserving the gauge state. The protected information is in a subsystem, not necessarily the full common eigenspace of every measured operator. Bacon–Shor codes are an instructive example.

Bosonic codes encode in an oscillator’s larger Hilbert space. Cat codes use superpositions of separated oscillator states and may exploit biased noise. GKP codes use grid-like phase-space structure to diagnose small displacements. Finite-energy states, loss, imperfect controls, and ancillary hardware determine practical performance [45, 46].

Erasure-aware and loss-biased schemes use knowledge of where a qubit or photon was lost. A known location is more informative than an unknown Pauli error. But identifying loss can itself require reliable measurement and replacement, and a lost atom during an entangling operation may affect partners.

No code is “best” independently of a hardware and computation model. Compare physical costs for the same logical workload, accuracy, connectivity, and time.

## 19.6 A ladder of experimental claims

An encoded state is the first step. Detecting faults and discarding bad runs is stronger but has an acceptance cost. Repeated correction must preserve information through many rounds. A logical operation must be compared at matched task conditions. Universal, scalable fault-tolerant computation adds non-Clifford resources, long-duration stability, and integrated classical control.

Recent neutral-atom and trapped-ion experiments demonstrate different pieces of this ladder [49, 50]. Their significance should be assessed by the demonstrated operations, postselection, error suppression, and assumptions, rather than collapsing all progress into an undifferentiated logical-qubit count.

## 19.7 Exercises

19.1. An X fault occurs on an ancilla that controls CNOTs to three data qubits. What risk follows?

Show solution / guidance

The X can propagate to each later target, creating a correlated multi-data error. A safe extraction design must prevent, flag, or decode that pattern.

19.2. With ε=0.01, evaluate the ideal leading 15-to-1 output estimate.

Show solution / guidance

35(0.01)3=3.5×10−5 35(0.01)^3=3.5\times10^{-5}. The result excludes higher-order terms, logical circuit faults, rejection probability, and full factory cost.

19.3. Why can a high-rate code still have large total hardware overhead?

Show solution / guidance

Encoding rate counts data qubits per logical qubit. Syndrome ancillas, routing, connectivity devices, factories, spare qubits, and operation schedules add costs.

19.4. Distinguish a logical memory benchmark from a universal-computation benchmark.

Show solution / guidance

A memory preserves encoded information. Universal computation must also implement a sufficient logical gate set, manage propagation and classical feed-forward, and supply non-Clifford resources with controlled total error.

19.5. Why should accepted logical fidelity be reported with acceptance probability?

Show solution / guidance

Postselection can make accepted runs accurate while most attempts are discarded. Acceptance determines throughput and success per attempt, and affects fair physical-versus-logical comparisons.

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