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Qunova Computing

Category: AI Chips / Semiconductors

A quantum computing software platform providing high-precision chemical and material simulation solutions that deliver 'Quantum Advantage' on current NISQ-era hardware. Qunova Computing was founded in 2021. The company is led by June-Koo Kevin Rhee. Based in Daejeon, South Korea. Team size: 10-50. Total funding raised: $10M. Latest round: Series A. Key investors include Korea Development Bank (KDB), Company K Partners, GS Ventures, Bluepoint Partners, Hyundai Motor Group (Zerolst), POSCO Capital, ID Ventures, SJS Investment.

Founded
2021
Headquarters
Daejeon, South Korea
Team size
10-50
Total funding
$10M

Value proposition

Enables industrial-grade chemical simulations with 'chemical accuracy' on existing noisy quantum computers without requiring complex error mitigation, reducing computational overhead by up to 1,000x compared to traditional methods.

Products and solutions

HI-VQE (Handover Iterative Variational Quantum Eigensolver): Core proprietary algorithm for high-accuracy molecular energy calculations., Milky Way: A comprehensive design platform for discovering new drug candidates and advanced materials., Pulsar: An analysis platform focused on interpreting complex molecular data and optimization problems., HI-VQE Chemistry (Qiskit Function): A cloud-native implementation available via IBM Quantum Network.

Unique value

The first startup globally to demonstrate 'chemical accuracy' on commercial quantum hardware using a hardware-agnostic algorithm that bypasses the need for resource-intensive error mitigation.

Target customer

R&D departments in pharmaceutical companies, chemical manufacturers, battery material developers, and advanced materials laboratories.

Industries served

Pharmaceuticals & Drug Discovery, Materials Science & Chemical Engineering, Energy Storage & Battery Technology, Semiconductor Materials

Technology advantage

Reduces qubit and gate requirements significantly by removing Pauli word measurements and utilizing a Hybrid Quantum-Classical (HQC) model, achieving 1,000x greater efficiency than standard Variational Quantum Eigensolvers (VQE).

How they differentiate

Proprietary HI-VQE (Handover Iterative Variational Quantum Eigensolver) algorithm that achieves chemical accuracy on current NISQ-era quantum hardware with up to 1,000x computational efficiency by removing Pauli word measurements and minimizing error mitigation needs.

Main competitors

Algorithmiq, Riverlane, QC Ware, SandboxAQ

Key partnerships

IBM Quantum: Integration of HI-VQE into the Qiskit Functions Catalog and member of the IBM Quantum Network., AWS: Availability of HI-VQE on the AWS Marketplace for Amazon Braket integration., Hardware Partners: Collaboration with IQM Quantum Computers and others to validate hardware-agnostic performance., Industrial Partners: Strategic ties with Hyundai Motor Group and POSCO for material and battery innovation.

Notable customers

Hyundai Motor Group, POSCO Holdings, E.ON (via IBM partnership), Yonsei University

Major milestones

Founded in 2021 as a KAIST spin-off by Prof. June-Koo Kevin Rhee, Secured Pre-Series A funding in June 2023, Winner of the CES 2024 Innovation Award, Selected for Forbes Asia 100 to Watch 2024, Closed $10M Series A funding round in August 2025, Presented global commercialization strategy at ITU Quantum World Tour in January 2026

Growth metrics

First Korean startup integrated into the IBM Qiskit Functions Catalog; demonstrated 1,000x computational speedup on commercial quantum processors.

Market positioning

Specialized quantum software provider focused on high-precision chemical and materials simulation for industrial R&D.

Geographic focus

South Korea, North America, Europe, Japan

Patents and IP

At least 4 patents filed globally (as of 2025) covering quantum algorithmic structures and high-efficiency chemical simulation methods.

About June-Koo Kevin Rhee

June-Koo (Kevin) Rhee is a Professor at the School of Electrical Engineering at KAIST (since 2005) and a globally recognized expert in quantum computing. He holds a Ph.D. in Electrical Engineering from the University of Michigan, Ann Arbor (1995). Before founding Qunova Computing, he led the KAIST IT Research Center of Quantum Computing for AI and founded the Quantum Techniques in Machine Learning (QTML) conference. He has over 30 years of experience in quantum algorithms, communications, and photonics.

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