Self-Serve Platform · Built for quantum researchers and developers
Sign up. Buy credits. Run jobs. That's it. From zero to a production-grade quantum simulation running on cloud GPU hardware — in under five minutes.
The Problem
You have a quantum algorithm that could change your project's trajectory. You need to simulate it — at scale, on serious hardware — by next week. Here's what actually happens:
You submit a request to internal IT for cloud compute access. It enters a queue.
IT asks for a cost estimate, a project code, a risk assessment, and a meeting with the cloud governance team.
Procurement starts vendor onboarding for a GPU cloud provider. Legal reviews terms. Finance creates a PO.
You finally get access to a raw cloud environment. Now you need to install CUDA, configure cuQuantum, set up Python, debug driver compatibility, and figure out job scheduling.
You run your first simulation. Three months after you had the idea.
By then, the window has closed. The grant deadline passed. The leadership review moved on. Your competitor published first. This isn't a technology problem. It's a bureaucracy problem.
The Offer
A self-serve quantum simulation platform. Everything you need, nothing you don't.
The same 12+ backend engine used by enterprise Dedicato customers. Statevector, MPS, tensor network, stabilizer, Pauli propagation — with intelligent auto-routing.
Access NVIDIA GPU compute on demand. Circuits that take hours on your laptop finish in minutes.
Python-native. Build VQE, QAOA, custom variational workflows, and noise-aware simulations with a clean, documented API.
No CUDA installation. No driver debugging. No Docker. No Kubernetes. You write Python. We handle everything else.
| Step | Time |
|---|---|
| Create account | 2 minutes |
| Purchase credits | 1 minute |
| Install SDK | 30 seconds |
| Submit first simulation | 1 minute |
| Total: Idea → Running job | Under 5 minutes |
No meetings. No tickets. No approvals.
pip install qoro-divi
| You Do | We Handle |
|---|---|
| Write quantum circuits in Python | GPU drivers, CUDA, cuQuantum |
| Choose your algorithm | Backend selection (Maestro Auto) |
| Submit jobs via SDK or API | Auto-scaling, job queuing, result delivery |
| Analyze results | Infrastructure monitoring, security, uptime |
You never see a cloud console. You never SSH into a server. You never configure a firewall rule.
Frictionless Pricing
Prepaid credit model. Buy credits with a credit card or P-Card. Consume them as you run jobs. No contracts. No vendor onboarding. No PO required.
| Pack | Credits | Best For |
|---|---|---|
| Starter | 3,500 | Initial exploration, small circuits, SDK familiarization |
| Prototype | 11,000 | Proof-of-concept for a single algorithm or research question |
| Accelerator | 35,000 | Multi-week project with GPU-accelerated simulation |
| Sprint | 75,000 | Full research sprint — large parameter sweeps, noise benchmarking, publication-ready results |
Every pack is priced to fly under standard P-Card and departmental expense limits. No procurement cycle required.
| Compute Tier | Burn Rate | 1,000 Credits Buys | Typical Use |
|---|---|---|---|
| Basic CPU | 0.0075 credits/s | ~37 hours | Prototyping, small circuits, educational use |
| Mid CPU | 0.045 credits/s | ~6 hours | Production CPU simulation, mid-depth circuits |
| GPU | 0.065 credits/s | ~4.3 hours | GPU-accelerated simulation, large parameter sweeps |
Example: A researcher running a 30-qubit VQE optimization with 200 iterations on the GPU tier, averaging 45 seconds per iteration, would consume approximately 585 credits — well within a Starter pack.
See exactly how many credits remain, what each job consumed, and projected runway.
Jobs are quoted before execution. If a job would exceed your balance, it's held until you top up.
Notifications at 50%, 25%, and 10% credit thresholds.
The Bottom Line
| Internal Enterprise IT | Qoro Solo | |
|---|---|---|
| Time to first simulation | 8–12 weeks | Under 5 minutes |
| Setup effort | Cloud account, GPU setup, CUDA, cuQuantum, job scheduler | Install SDK, run script |
| Procurement required | Vendor onboarding, PO, legal review | Credit card purchase |
| Approvals needed | IT, finance, procurement, legal, cloud governance | Your P-Card limit |
| DevOps headcount | At least 1 FTE (part-time) | Zero |
| GPU environment | Self-managed (driver, CUDA, container, scheduler) | Fully managed by Qoro |
| Cost model | Monthly cloud bill (variable, unpredictable) | Prepaid credits (fixed, visible) |
| Minimum commitment | Annual cloud contract or reserved instances | No contract. |
| Risk if project cancelled | Sunk infrastructure and cloud commitments | Only spend what you use. Credits never expire. |
Qoro Solo exists because the best quantum research shouldn't be blocked by paperwork.
When You Outgrow Solo
When your quantum program scales — multiple researchers, recurring workloads, enterprise compliance — the path forward is Dedicato, Qoro's fully managed enterprise platform. The transition is seamless:
Your code doesn't change.
Your results are consistent.
Private VPC, SLAs, and a Technical Account Manager.
Route circuits to real quantum processors from IBM, AWS Braket, IQM, and LRZ.