Verifiable quantum–classical infrastructure
Quantum Distributed (MESH) Compute Network
QMesh connects institutional QPUs, cloud capacity, private clusters, and contributed CPU/GPU compute capacity through one control plane for task analysis, routing, verification, and settlement.
Classical preflight before QPU
Three security zones
Result-based economics
Hybrid control plane
QM-ORCH / 01
CONTROL
QMesh
Analyze · route · verify
QPU poolScarce quantum hardware
Cloud GPUElastic simulation
Private HPCSensitive workloads
Community computeContributed CPU / GPU
Live routing policyPreflight on trusted GPU
The missing coordination layer
Connect compute.
The opportunity is not to pretend every PC is a quantum computer. It is to send divisible, verifiable classical work to the right compute resource—and reserve QPU time for the part that truly needs it.
Stop unproductive QPU runs
Compile, simulate, estimate noise, and screen parameters before expensive hardware execution.
Convert idle compute into supply
Individuals, universities, and enterprises complete bounded work units and earn only for accepted results.
Build evidence around every result
Signed tasks, hidden benchmarks, replicated execution, and reputation scores create an auditable result chain.
Result-first economics
Expected contribution marginP(success) × result revenue − compute − review − error loss
Compute contribution net earningsAccepted-work income − power − depreciation
Effective-result cost.
Optimize the cost of valid results—not the lowest compute price.
Minimum effective-result cost
=
QPU cost+Classical compute+Verification+Retries+Human review
Accepted experiments
Interactive model
Effective-result cost calculator
Illustrative values only. Replace them with your own baseline.
Cost per accepted experiment3,958.33illustrative cost units / result
Core product · Quantum Preflight
Control QPU calls.
Upload a circuit or hybrid algorithm. QMesh determines what can be validated classically, which hardware fits, how much budget is justified, and how the result should be verified.
Preflight Analyzer
Inspect circuit scale, entanglement, gate set, sensitivity, and cut boundaries.
Hybrid Scheduler
Route work across community compute, trusted HPC, cloud GPUs, and QPUs.
Verification Engine
Use hidden benchmarks, replication, statistics, and reputation to accept outputs.
Quantum CI/CD
Trigger compilation, simulation, regression, and budget checks from code changes.
Cost Guardrails
Set budgets, retry limits, timeouts, provider policies, and fallback behavior.
Result Ledger
Link code, compilation, calibration, evidence, mitigation, and billing.
One auditable workflow
Code to result.
The control plane makes every decision explicit: where work ran, why it ran there, how it was verified, and what it cost.
Community compute network
Contribute compute.
Personal and institutional compute resources handle parallel, retryable, non-sensitive work: simulation, compilation, parameter search, circuit-cut reconstruction, mitigation, and verification.
Compilation seeds and topology mapping
State-vector, noise, and tensor simulation
Parameter sweeps and classical optimization
Circuit-cut reconstruction and statistics
Benchmarking, mitigation, and cross-checks
Register compute interest
01Community
Public contributed compute
Public or sanitized jobs, signed sandboxes, restricted networking, and default re-checking.
Personal desktops · workstations · open research02Trusted
Verified institutional compute
Identity, hardware evidence, operating baselines, higher reputation, and restricted research workloads.
Universities · labs · certified partners03Private
Customer-controlled compute
Workloads remain inside the customer VPC, data center, or designated cloud account.
Proprietary code · sensitive data · enterprise SLAThree-sided business model
Orchestrate for results.
QMesh links customers, classical-compute contributors, and QPU providers. The defensible asset is not hardware inventory—it is workflow integration, routing data, verification, and trusted settlement.
QMeshControl plane
Enterprise & research customers
Pay for software, experiments, orchestration, or private deployment.
Compute contributors
Earn cash or Q-Credits for accepted classical work units.
QPU providers
Supply real hardware through official APIs and capacity agreements.
Software subscription
CI/CD, cost dashboards, permissions, audit, APIs, and reports.
Classical-compute fee
Contributors receive the majority; QMesh retains verification and service fees.
QPU orchestration fee
Backend selection, budget control, retries, records, and unified billing.
Private deployment
Customer VPC, internal HPC/GPU integration, SLA, and vertical workflows.
Zero-trust by design
Zero-trust execution.
Security is not a single container. It is a continuous chain of least privilege, signed workloads, bounded execution, result verification, and complete audit evidence.
Discuss private deploymentVerified execution
01Least exposureOnly the data and time required
02Signed sandboxNo personal mounts; limited network
03Result proofBenchmarks, replicas, statistics
04Full audit trailCode, runtime, evidence, settlement
8–12 week design partnership
Pilot a workload.
The pilot does not promise “quantum advantage.” It establishes a baseline, connects one QPU backend, runs preflight and verification, and produces an evidence-backed before/after review.
Experiment cycle time
Invalid QPU runs
Engineering hours
Cost per accepted result
FAQ
FAQ
QMesh is designed around explicit limits: what community compute can do, what must stay private, and how results become trustworthy.
Not physical qubits. It can contribute classical CPU/GPU work required by quantum workflows, such as simulation, compilation, parameter search, reconstruction, and verification.
Uptime does not create customer value. Paying for accepted work aligns the customer, contributor, and platform around verified output.
No by default. Sensitive workloads are locked to trusted or private zones. Community compute resources receive only public or sanitized, verifiable work units.
No. The commercial claim is narrower and measurable: reduce waste, improve reproducibility, control budgets, and lower the cost of an accepted experiment.