Dynamic compute fabric · workload-aware routing · verified results
Mesh any workload with the right compute
QMeshing is a distributed computing fabric that dynamically meshes quantum, AI, and classical compute resources into a verified execution route for each workload
AI, HPC, and quantum workloads
QPU, simulator, GPU, HPC, and edge
Evidence before acceptance
Live QMeshing fabric
QMESHING / 01
QMESHING
QMeshing
Discover · compose · execute · verify
QPU poolQuantum acceleration
Cloud GPUAI training and inference
Private HPCSensitive workloads
Community computeContributed CPU / GPU
Current meshing stepDiscover eligible resources
A process, not a static topology
QMesh is the network · QMeshing forms it
A mesh describes what the network looks like. Meshing describes the continuous process of discovering, composing, adjusting, and optimizing quantum, AI, and classical resources for each workload
Discover
Read the workload, security boundary, quality target, deadline, and available capacity
Compose
Mesh classical preprocessing, model APIs, simulators, QPUs, GPUs, HPC, and edge into one execution route
Adapt and verify
Re-route when conditions change, verify output, and turn evidence into the next better decision
Industry scenarios
Real workloads · dynamically matched compute
Start with the job to be done. QMeshing discovers and composes the right mix of classical, AI, simulation, and quantum resources for each workload.
Biotech01
Drug candidate screening
Rank molecular candidates with classical chemistry, AI scoring, simulation, and quantum experiments where they can be measured.
AIGPUHPCQPU / SIM
Verified outputRanked candidates with reproducible evidence
Biotech02
Protein and genomics analysis
Compose sequence models, structure prediction, secure HPC, and statistical checks without moving sensitive data to public compute.
AIPRIVATE HPCGPU
Verified outputTraceable predictions and confidence checks
Finance03
Portfolio optimization
Compare classical solvers, QUBO formulations, simulators, and eligible QPUs under the same risk, cost, and deadline constraints.
QUBOCPU / GPUQPU / SIM
Verified outputFeasible portfolios with benchmark deltas
Finance04
Financial risk stress testing
Run Monte Carlo, scenario generation, tail-risk models, and independent validation across private and elastic compute.
MONTE CARLOGPUHPC
Verified outputAuditable VaR, CVaR, and stress scenarios
Materials05
New materials and battery R&D
Screen compounds with simulation, surrogate models, HPC, and bounded quantum chemistry experiments.
SIMULATIONAIHPCQPU
Verified outputCandidate properties with validated baselines
Energy06
Grid and storage scheduling
Optimize dispatch, storage, demand response, and contingency plans against live constraints and forecast uncertainty.
FORECASTMILP / QUBOHPC
Verified outputConstraint-safe schedules with cost comparison
Logistics07
Logistics route optimization
Re-plan fleets and warehouse flows as traffic, capacity, service windows, and costs change.
SOLVERGPUEDGEQPU / SIM
Verified outputFeasible routes with SLA and cost evidence
AI systems08
Multi-model AI routing and evaluation
Route each request among commercial APIs, open models, local inference, and human review by quality, privacy, latency, and cost.
APILOCAL AIGPUHUMAN
Verified outputAccepted answers with quality and cost evidence
Not every workload needs a QPU. QMeshing compares classical, AI, simulation, and quantum paths, then selects the route with the strongest evidence.
Explore scenarios in the demo
Result-first economics
Expected contribution marginP(success) × result revenue − compute − review − error loss
Compute contribution net earningsAccepted-work income − power − depreciation
Accepted AI training result cost
Optimize the cost of accepted AI training results—not the lowest compute unit price.
Minimum effective AI training result cost
=
Model / QPU cost+Classical compute+Verification+Retries+Human review
Accepted AI training results
Interactive model
Effective-result cost calculator
Illustrative values only. Replace them with your own baseline.
Cost per accepted AI training result3,958.33illustrative cost units / accepted result
Operational QMeshing
QMeshing in action
Watch the fabric form around a live workload: publish, discover, compose, execute, verify, and settle
Four operational views
Publish tasks
Describe the workload and start from a verifiable strategy. QMeshing turns requirements into a routable execution plan
WEBAPIERPAGENT
123
＋QASM · PYTHON · QUBO · NOTEBOOK · CSV
Four operational views
Find paid tasks
Discover paid workloads whose requirements match your verified compute
FITREWARDDEADLINEPROOF
SAMPLE95HIGH FIT32PAYMENTOFF
AITraining evaluationGPU · Private · Auto acceptance
98%QQuantum chemistry batchGPU + QPU · Trusted · Proof
96%ΣPortfolio constraintsHPC · Private · Review
93%Four operational views
Compose the route
Discover eligible resources, combine classical preprocessing with QPU, simulator, GPU, HPC, edge, or API execution, and adjust as conditions change
QPUGPUHPCEDGE
QMeshFORMED
43 LIVE18 ms ROUTE
Four operational views
Verify delivery
Bind workload, route, runtime, output, verifier decision, acceptance, and settlement into one auditable chain
HASHRUNTIMECHECKSLEDGER
✓
RESULT ACCEPTEDQMX-4821 / PROOF-03Input · route · runtime · output · verifier · settlement
100%
Current system interfaces
Connect every layer of the fabric
Combine OpenAI-compatible APIs, local models, Windows or Linux Agents, QPUs, simulators, GPUs, and HPC under one QMeshing policy
The demo includes 95 sample tasks. Sample tasks never create real payments.
Compute is assigned only after an execution plan is submitted.
OpenAI-compatible API
ResponsesChat CompletionsEmbeddings
Windows & Linux agents
OllamaLM StudiovLLMWindowsLinux
Execution controls
HeartbeatLeaseExecution proofAuto / Manual acceptance
Task publishing center
Publish from the web, API, enterprise systems, or scheduled agents with editable task strategies.
Professional template library
Start from 12 templates covering quantum, science, optimization, AI/data, and financial investment.
Paid task marketplace
Compare fit, reward, deadline, security zone, acceptance method, and task terms.
QMeshing orchestration
Dynamically compose model APIs, local models, QPUs, simulators, GPUs, HPC, edge, and community compute
Result verification engine
Use schema checks, hidden benchmarks, independent models, replication, and policy gates.
Settlement and audit ledger
Bind task, route, runtime, output, evidence, acceptance, cost, and settlement.
The QMeshing lifecycle
Workload in · verified result out
The fabric forms around the task, adapts as conditions change, and closes only when the result is verified
Professional task templates
Start with a verifiable strategy, not a blank form
Each template defines purpose, inputs, routing constraints, verification, and output so QMeshing can form the right compute route
Quantum algorithms
VQEQAOACIRCUITMITIGATION
Scientific computing
PROTEINSIMULATIONPARAMETER
Optimization
ROUTINGLOGISTICSSCHEDULING
AI & data
TRAININFERENCEEVALCLASSIFY
Financial investment
PORTFOLIOVaRDERIVATIVEBACKTEST
12
Explore templates in the demo12 professional templates. Each includes purpose, inputs, compute route, verification, expected output, and an editable strategy
Resource fabric
Resources form around the workload
Register GPU, HPC, QPU, simulator, or edge capacity. QMeshing discovers what is eligible and composes only the resources each workload needs
Hardware, driver, and capability evidence
Benchmark and workload profile
Security zone and network policy
Availability and minimum reward
Execution proof, reputation, and earnings
Join the QMeshing fabric
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
One fabric · aligned incentives
Publishers buy verified outcomes. Compute and model providers supply eligible capacity. QMeshing composes the route, proves delivery, and settles accepted results
QMeshingCompose · prove · settle
Task publishers
Buy accepted AI training results, software, orchestration, or private deployment.
Compute contributors
Earn for accepted classical work, model execution, simulation, or verification.
Model and QPU providers
Supply specialized model APIs or quantum hardware through controlled integrations.
Software subscription
CI/CD, cost dashboards, permissions, audit, APIs, and reports.
Classical-compute fee
Contributors receive the majority; QMeshing retains verification and service fees.
Model and QPU orchestration
Route selection, budget control, retries, evidence, and unified billing.
Private deployment
Customer VPC, internal HPC/GPU integration, SLA, and vertical workflows.
Zero-trust by design
Public · Trusted · Private
Sensitive workloads stay inside trusted or private compute. Public compute receives only bounded, sanitized, verifiable work units under explicit policy.
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 production pilot
Validate a real workload
Connect one task source, one or more compute routes, and an acceptance policy. Measure cycle time, acceptance rate, routing mix, and cost per accepted AI training result.
Acceptance rate
Task cycle time
API and compute routing mix
Cost per accepted AI training result
FAQ
FAQ
Understand QMeshing, workload routing, compute participation, verification, cost, and security
Open Help CenterQMeshing is a distributed computing fabric that dynamically meshes quantum, AI, and classical compute resources into a working route for each workload
QMesh is the working network formed for a workload. QMeshing is the ongoing process that discovers, connects, adjusts, and optimizes the resources that form it
Choose a professional template, add inputs, security, budget, and acceptance rules, review the quote, then submit. Compute is allocated only after an execution plan is accepted
CPU or GPU work such as inference, evaluation, simulation, compilation, parameter search, reconstruction, and verification. Physical quantum hardware remains with QPU providers
QMeshing compares workload fit, model and hardware capability, security zone, location, availability, deadline, expected quality, verification cost, and total accepted-result cost, then re-routes when conditions change
QMeshing binds input hashes, route, runtime, output, and verifier decisions into proof. Settlement starts only after policy checks or an authorized review accepts the result
QMeshing uses caching, batching, rules, local or open models, and confidence gates for suitable work, and calls commercial APIs only when they add value. A pure API proxy does not reduce cost
Not by default. Sensitive work stays in trusted or private compute. Community compute receives only public or sanitized, bounded, verifiable work units
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