INVARIANT Partner with Invariant

Invariant // QEC Systems System Architecture / Rev 0.1

Fault tolerance for quantum computers.

Invariant is building the real-time software layer that turns streams of physical quantum errors into reliable logical computation.

Partner with Invariant Explore the architecture
  • 00QEC Runtime
  • 01Real-Time Decoding
  • 02Logical State
  • 03Fault-Tolerant Computation
Fig. 00 — Fault-tolerance pipeline Simulated
01 / The Bottleneck Fault-Tolerance Research · Fig. 01

Quantum hardware creates errors faster than useful computers can tolerate them.

Fault-tolerant quantum computing requires logical qubits to be encoded across many physical qubits and continuously protected from noise. That creates a second computational system running alongside the QPU.

Quantum-error-correction measurements must be interpreted fast enough to identify errors, maintain logical state, and keep computation moving.

If the classical fault-tolerance layer cannot keep pace, the quantum computer cannot scale effectively.

01.1Physics

Continuous Error

Quantum states accumulate errors during execution. Error correction must operate continuously, every cycle, for the entire lifetime of a computation.

01.2Timing

Classical Latency

Syndrome processing and decoding must remain ahead of the QEC cycle. A decoder that falls behind accumulates a backlog the computation cannot wait for.

01.3Resources

System Overhead

Fault tolerance consumes qubits, time, bandwidth, and classical computation. Every layer of the stack pays for it.

Fig. 01 — Accumulated physical error events vs. logical state Illustrative / No Scale
Physical error events (accumulating) Logical state (corrected) Correction applied

Schematic chart: physical error events accumulate step by step over QEC rounds while the logical state remains flat because corrections are applied continuously. No numeric scale is shown.

02 / Invariant Runtime Internal System Model · Rev 0.1

A real-time control plane for logical qubits.

Invariant sits between the logical circuit and the QPU control stack. It ingests stabilizer measurements as they are produced, decodes them under strict latency constraints, maintains the logical state as a Pauli frame, and adapts to the hardware as its noise changes.

Every component is designed to be measured. Hover or select a module to trace its data path.

Fig. 02 — System architecture Logical Computation Layer
Runtime · Real-Time
Measurements ↑
02.1 / Core Systems Four Subsystems · Decode Track Adapt Verify

Four systems, one runtime.

I / IV · Streaming Inference

DECODE

Infer physical error history from high-volume syndrome streams under strict latency constraints.

  • Streaming
  • Parallel
  • Low-Latency
  • Decoder-Agnostic
Syndrome matrix → decoder graphSimulated
II / IV · Logical State

TRACK

Maintain logical state without forcing higher layers to reason about every physical correction.

  • Logical Qubits
  • Pauli Frame
  • Execution History
  • Fault State
Logical-state timelineSimulated
III / IV · Noise Response

ADAPT

Respond to changing hardware conditions and evolving noise characteristics.

  • Noise-Aware
  • Telemetry
  • Adaptive Parameters
  • Drift Response
Noise estimate / parameter updateSimulated
IV / IV · Measurement

VERIFY

Measure logical error suppression, decoding latency, throughput, resource use, and backlog stability.

  • Logical Error Rate
  • P50 / P99
  • Throughput
  • Backlog
Benchmark panelAwaiting Data
Logical error rate
—
Pending
Decode latency · P50
—
Pending
Decode latency · P99
—
Pending
Throughput
—
Pending
Backlog growth
—
Pending
Values published once measuredRev 0.1

Runtime observability console (simulated)

Invariant / Runtime Observability Mode Simulated · —
System metricsIllustrative
System
Simulated
QEC code
—
Code distance
—
Physical error rate
—
Logical error rate
—
Decoder throughput
—
Median latency
—
P99 latency
—
Backlog growth
—
Logical state
Stable
Syndrome streamRound · ID · Bits · Weight · State
    Latency traceScale withheld
    t − 60sNow
    Event logSession time
      StateLive
      Decoder
      Active
      Backlog
      0
      Frame
      Updated
      Logical state
      Stable
      Interface establishes the observability language. No benchmark values are shown until they are measured. Rev 0.1
      03 / The Abstraction Physical → Logical

      Physical qubits are components. Logical qubits are computation.

      Physical LayerComponents
      • qubit q[0]
      • qubit q[1]
      • qubit q[2]
      • qubit q[3]
      • qubit q[4]
      • noise continuous
      • drift slow
      • measurement imperfect
      • failure expected
      Logical LayerComputation
      • logical q0 encoded
      • logical q1 encoded
      • logical q2 encoded
      • protected ●
      • tracked ●
      • programmable ●

      Invariant is focused on the software between those two worlds.

      The goal is not to hide quantum physics. It is to turn fault tolerance into a programmable systems interface.

      04 / Systems Teams Intended Collaborators

      Built for the teams building quantum computers.

      04.1Hardware

      QPU Manufacturers

      Teams building superconducting, trapped-ion, neutral-atom, photonic, bosonic, or future quantum architectures.

      • Real-time decoding
      • Logical error characterization
      • QEC runtime integration
      • Fault-tolerant architecture testing
      04.2Control

      Quantum Control Teams

      Teams responsible for control electronics and hardware-software integration.

      • Syndrome streaming
      • Low-latency feedback
      • Runtime telemetry
      • Hardware-in-the-loop testing
      04.3Research

      QEC Research Teams

      Researchers developing codes, decoders, and fault-tolerant protocols.

      • Decoder benchmarking
      • Noise-model experiments
      • Code comparison
      • Reproducible simulation
      04.4Infrastructure

      National Labs / HPC Centers

      Teams researching future large-scale quantum infrastructure.

      • Architecture evaluation
      • QEC benchmarking
      • Classical/quantum integration
      • Resource planning
      05 / Validation Development Model · Fig. 05

      Build against physics. Validate against simulation. Integrate with hardware.

      The runtime can be developed and validated before it requires proprietary access to a physical quantum processor. Deterministic tests, QEC simulation, Monte Carlo sampling, and recorded syndrome data exercise the same code paths that will later run against live hardware.

      Hardware-in-the-loop integration and live QPU operation are the final stages, not the first.

      1. Stage 101Deterministic TestsFixed inputs, exact expected outputs. Every decoder path is checked against known error histories.No QPU access
      2. Stage 202QEC SimulationStabilizer circuits simulated under configurable noise models to produce realistic syndrome streams.No QPU access
      3. Stage 303Monte CarloLarge sampled runs to estimate logical error behaviour and latency distributions with confidence intervals.No QPU access
      4. Stage 404Recorded Syndrome DataReplay of captured measurement streams to test the runtime against real noise structure offline.Data access
      5. Stage 505Hardware-in-the-LoopRuntime connected to control electronics with timing and interface constraints of a real system.Control access
      6. Stage 606Live QPUClosed-loop operation on a physical quantum processor, measured against the same telemetry.QPU access
      Stages 01–04 · Independent of proprietary QPU access Stages 05–06 · Hardware partnership
      06 / Systems Software Execution Stack · Fig. 06

      Built close to the machine.

      1. L5Python Research API Experiments · Simulation · InterfacesPY
      2. L4Invariant Runtime Scheduling · State · TelemetryC
      3. L3C Decoder Core Latency-Critical InferenceC
      4. L2Parallel / SIMD Execution Memory Control · Vectorized PathsC
      5. L1Control Interface Defined Boundary To Control StackIF
      6. L0QPU Physical Qubits · MeasurementsHW
      Top: research surfaceBottom: hardware boundary

      C handles latency-sensitive decoding, memory control, and system execution. Python handles research workflows, simulation, experimentation, and developer interfaces.

      Latency path
      Syndrome ingest, decoding, and frame updates run in C with explicit memory layout and no allocation on the hot path.
      Research path
      Codes, noise models, decoders, and experiments are composed in Python and exercised against the same core.
      Boundary
      A defined control interface so the runtime can target different control stacks without rewriting the decoder core.
      Principles How We Work

      Four constraints on everything we build.

      P.1

      Correctness

      Correctness comes before performance claims.

      P.2

      Measurement

      Every optimization should be measurable and reproducible.

      P.3

      Hardware Awareness

      Fault-tolerance software must understand real noise, topology, timing, and control constraints.

      P.4

      Hardware Independence

      The long-term runtime should not depend on one QPU architecture or vendor.

      07 / Research Technical Notes · In Preparation

      Trust should be earned in public.

      We are preparing technical notes that document how the runtime is designed, how it is benchmarked, and how its results can be reproduced. Nothing is listed here until it is written.

      1. R.01Benchmark methodologyTechnical NoteRev 0.1Coming soon
      2. R.02Architecture notesTechnical NoteRev 0.1Coming soon
      3. R.03Decoder researchTechnical NoteRev 0.1Coming soon
      4. R.04Simulation methodologyTechnical NoteRev 0.1Coming soon
      5. R.05Fault-tolerance experimentsTechnical NoteRev 0.1Coming soon
      No publications are claimed. Entries are placeholders for work in progress.Invariant // Research

      Quantum hardware creates qubits.

      Invariant creates reliable computation.

      We are building the fault-tolerance software layer between physical quantum hardware and logical quantum machines.

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