Circuit files grow exponentially with qubit count. Your storage problem scales with your roadmap.
RNDA encodes quantum circuits and discards the raw files. Storage costs approach zero. Proprietary gate sequences stop existing in recoverable form.
Request a Quantum POC →The Problem
Quantum computing generates circuit files that grow dramatically with qubit count and gate depth. A single large-scale circuit on a 433-qubit processor can exceed 90MB. At production scale — thousands of circuits per day across multiple quantum processors — storage, retrieval, and compliance overhead compounds rapidly. Raw circuit data is sensitive IP.
How RNDA Solves It
351,939x peak compression on real IBM circuits
Proven on 243 real quantum circuits verified on IBM Quantum hardware — Burlington, Melbourne, and Paris backends. An 90MB circuit encodes to 256 bytes. Average compression across the full dataset: 7,462x.
Semantic circuit similarity search
Find circuits with similar algorithmic structure across your entire circuit archive — without retaining raw QASM files. Similar algorithms cluster in signature space. Retrieve related circuits by behavioral similarity.
IP protection for proprietary circuits
Quantum algorithm implementations represent significant R&D investment. RNDA encodes the circuit and permanently discards the raw QASM. Proprietary gate sequences and circuit topology never exist in recoverable form.
Scales with IBM's quantum roadmap
As IBM scales to 1,000+ qubit processors and circuit files grow into the gigabyte range, RNDA compression scales proportionally. Larger circuits produce higher compression ratios — the storage problem and the RNDA solution grow together.
How RNDA Applies
Storage Elimination
At 351,939x peak compression, quantum circuit libraries that previously required terabytes of storage reduce to megabytes. A 90MB large-scale circuit encodes to 256 bytes. Research labs running thousands of circuits per day can archive indefinitely at negligible cost.
Privacy Protection
Proprietary quantum algorithm implementations represent significant R&D investment. RNDA encodes circuit topology and permanently discards the raw QASM file — proprietary gate sequences and circuit structure never exist in recoverable form after encoding.
Compliance Management
Quantum research funded by DOE, DARPA, and international agencies carries data handling requirements. RNDA provides an auditable, structured format for circuit data with access logging — compliance requirements met without raw circuit exposure.
Intelligent Retrieval
Semantic search across compressed circuit libraries finds structurally similar circuits — enabling rapid discovery of relevant prior work without downloading raw files. Proven on 243 real circuits verified on IBM Burlington, Melbourne, and Paris quantum backends in ~275ms.
Collaborative Intelligence
IBM Q Network, Google Quantum AI partners, and national labs share circuit libraries as compressed RNDA archives, enabling cross-institution collaboration on circuit design without exposing raw gate-level IP. Quantum research collaboration without IP exposure.
Storage Impact
Industry stat: A single complex quantum circuit simulation generates 86 MB to multi-GB files per run; research labs running thousands of circuit experiments per day accumulate terabytes per year (DOE QIS Roadmap 2024)
10 TB × 20% × $276/TB ÷ 351,939x compression
10 TB quantum lab saves ~$552/year — 351,939x peak compression on real IBM Quantum circuit data (86 MB → 256 bytes)
Proof of Concept Results
Real data. Measured numbers. No synthetic results.
Source: QASMBench (PNNL) — IBM Quantum verified circuits
What Becomes Possible
"A quantum computing research team runs hundreds of circuits per day on IBM Quantum hardware. Each circuit is encoded to a 256-byte signature and the raw QASM file permanently discarded. The signature store enables semantic search across the full circuit history — find all circuits structurally similar to a known high-performance implementation — without storing a single raw circuit file."
Ready to see it on your data?
Every number on this page came from a real POC. Yours will be built the same way — against your actual data type, measured compression, real query latency.
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