Who we are
Quantum computing promises to revolutionize technology, but achieving fault tolerance requires understanding how quantum errors actually behave in real hardware—not just how theory predicts they should.
Quantum-Clarity was founded to answer fundamental questions about quantum error dynamics on commercial processors. Our experimental research has revealed that quantum errors are neither independent nor memoryless—they correlate spatially across qubits (4.86σ) and persist temporally through environmental memory (~30 microseconds), even after complete qubit reset.
These discoveries challenge core assumptions in quantum error correction theory and point to a new paradigm: fault tolerance is an environmental engineering problem.
Founded by Silicon Valley veteran Amit Brahmbhatt, Quantum-Clarity combines decades of experience building large-scale distributed systems with rigorous experimental physics methodology. Our approach emphasizes hardware-verified results, statistical validation at discovery-threshold significance levels (approaching 5σ), and complete reproducibility through open datasets published on Zenodo.
Our Mission: Advance the transition from NISQ-era quantum computing to fault-tolerant systems by identifying fundamental hardware limitations and translating discoveries into actionable engineering insights—from correlation-aware QEC decoders to substrate and cavity optimization targeting the ~30μs environmental memory timescale.
Our Work: We conduct independent experimental research on commercial quantum processors (IBM Quantum), develop novel error characterization methodologies (patent-pending stabilizer diagnostics), and engage with researchers, hardware teams, and industry partners to drive the field toward environmental reliability.
Quantum-Clarity operates at the intersection of experimental discovery and practical engineering—proving what's fundamentally limiting today's quantum computers and showing how to engineer around it.
Products & Platforms
• QuantaCore MBQC — Hardware-native measurement-based quantum computing framework featuring YZ-stabilizer resource states with 94% experimental fidelity on IBM Quantum. Enables gate-error-free quantum computing through GPU-accelerated protocol simulation and validation.
• PyTran — A hybrid Python–Fortran runtime for HPC, AI, and quantum-classical workflows
Developer platform: https://www.quantum-clarity.com/developer-platform
• Cyber-Circuit — An enterprise-grade, quantum-enhanced cybersecurity and security-reasoning platform, built on Quantum-Clarity's hybrid compute and reasoning stack
Product site: https://www.cyber-circuit.com/
Quantum-Clarity LLC develops foundational technology that spans quantum computing research, hybrid runtimes, MBQC resource frameworks, and production-grade security systems.
Research Infrastructure
Quantum-Clarity operates across two complementary research platforms: experimental quantum error characterization on commercial superconducting processors and computational quantum chemistry for catalyst design and materials discovery.
Quantum Hardware Access
We leverage world-class quantum computing platforms through the IBM Quantum Network:
IBM Quantum Processors:
IBM Fez - 156-qubit Heron r2 processor (state-of-the-art error characterization platform)
IBM Torino - 133-qubit heavy-hex lattice (210K CLOPs)
IBM Brisbane - 127-qubit Eagle r3 processor (180K CLOPs)
All quantum experiments are executed on IBM Quantum's production hardware via Qiskit Runtime, with complete job IDs and reproducibility protocols published on Zenodo for independent verification.
Computational Quantum Chemistry Platform
Hardware:
NVIDIA A100 GPU optimized for variational quantum eigensolver (VQE) calculations
High-performance NVMe storage for large-scale molecular Hamiltonian operations
Software Stack:
PySCF 2.12.0 - Quantum chemistry calculations with effective core potentials (ECP) for transition metal systems, ROHF/UHF methods, CASCI/CASSCF active space solvers
TorchQuantum - GPU-accelerated quantum circuit simulation with automatic differentiation and gradient-based optimization
PyTorch - Deep learning framework with CUDA GPU acceleration for variational optimization
Qiskit & OpenFermion - Quantum algorithm development and fermion-to-qubit transformations (Jordan-Wigner, Bravyi-Kitaev)
TensorFlow Quantum - Hybrid quantum-classical machine learning workflows
CUDA Toolkit - GPU acceleration for large-scale Hamiltonian operations and circuit simulation
NumPy/SciPy - Numerical linear algebra, optimization algorithms (Adam, BFGS), and statistical analysis
Matplotlib/Seaborn - Scientific visualization and publication-quality plotting
Development Environment:
Python 3.10 with Conda environment management
Git version control with systematic experiment tracking
Jupyter notebooks for exploratory analysis and result visualization
Capabilities:
Active space calculations: 12-electron, 10-orbital systems (expandable to 20 qubits)
Transition metal catalyst screening (cuprate superconductors, nitrogen fixation catalysts)
Mixed basis sets: lanl2dz (ECP) for metals + 6-31G for ligands
Chemical accuracy validation: <1 kcal/mol error vs. CASCI benchmarks
Recent Applications:
Project HELIOS - Systematic screening of 4d/5d transition metal dopants in cuprate superconductor lattices
Validated bilateral regulation hypothesis: 4d metals (Ru, Rh, Pd) show 56% reduced correlation energy vs. 3d metals (Ni, Cu)
Computational confirmation of molybdenum's superior electronic regulation in nitrogen fixation catalysis
Cloud Computing & Analysis
Google Cloud Platform - Cloud computing credits support large-scale data analysis, statistical validation pipelines, and experimental result aggregation across 500k+ quantum measurements.
Analysis Stack: Python scientific computing (NumPy, SciPy, Matplotlib, pandas) for statistical validation, SPAM error control, and convergent cross-validation achieving particle-physics-grade significance levels.
Meet the Team
Amit Brahmbhatt
Founder-CEO
Amit discovered that quantum errors have environmental memory—a finding that challenges core assumptions in quantum error correction and reveals why achieving fault tolerance requires engineering the environment, not just fixing qubits.
Working on IBM's 156-qubit Heron processor, he characterized non-Markovian error correlations (4.86σ spatial, 3.6σ temporal) persisting ~30 microseconds. His experimental work, published openly on Zenodo with full reproducibility protocols, is approaching the 5σ discovery threshold used in particle physics.
Before founding Quantum-Clarity, Amit spent 15+ years building large-scale distributed systems and data platforms—experience that now drives the company's rigorous approach to experimental validation and scalable quantum research.
Contact us
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