Deeptech startup BQP raises $4.9 million in seed round led by Monta Vista Capital

BQP, a quantum-accelerated simulation software company, has raised $4.9 million in an oversubscribed seed funding round led by Monta Vista Capital.

The round also saw participation from New York Ventures, Arc Ventures, Emergent Ventures, Alumni Ventures and others.

The funding comes after a partnership with the Air Force Research Laboratory’s Aerospace Systems Directorate through a CRADA, aiming to enhance modeling and simulation using quantum computing.

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The company said it will use the funds to build its quantum-powered digital twin technology with partners and grow its reach in aerospace, defense and semiconductors.

“Our quantum-inspired solvers are setting a new benchmark in simulation technology, bridging today’s computational limits and the quantum-ready future,” said Abhishek Chopra, BQP’s Founder, CEO, and Chief Scientific Officer. “The traction we're experiencing from AFRL/RQ and industry-leading design partners validates the immediate impact and substantial ROI our platform delivers today. We're also making significant R&D progress in quantum-native solvers for Computational Fluid Dynamics (CFD) and Machine Learning (ML), paving the way for future simulation workload for the next generation of data centers where HPC and quantum computers operate side by side.”

“BQP directly tackles critical bottlenecks in the rapidly evolving $22 billion simulation industry, where conventional technologies have struggled to keep pace with innovation demands in sectors like Aerospace & Defense, Semiconductor, and Energy,” said Roger Krakoff, Partner at Monta Vista Capital. “Their quantum-powered framework is positioned to become foundational infrastructure in the simulation ecosystem.”

About BQP 

Founded in 2020 by Abhishek Chopra, Jash Minocha and Rut Lineswala, BQP develops simulation platforms to enhance engineering decisions in aerospace, defense, semiconductors and energy.

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Its main product, BQPhy, blends quantum-inspired probabilistic algorithms with proprietary solvers and fits into existing engineering workflows. While it runs on classical computing systems, it supports optimization, machine learning and physics-based simulations.

These solvers can be used on their own or as part of a digital twin framework. BQP aims to establish BQPhy as the go-to backend for simulation workflows across hybrid systems—including CPUs, GPUs and future quantum processors.