Quantinuum Announces InQuanto – An Advanced Computational Chemistry Platform For Quantum Computers

By Patrick Moorhead - July 25, 2022

Quantinuum was formed in 2021 by the merger of Honeywell Quantum Solutions (quantum hardware) and Cambridge Quantum Computing (quantum software). Quantinuum has released InQuanto, a python-based quantum chemical software platform that performs chemistry algorithms on current quantum hardware, following a series of previous announcements featuring a record quantum volume and an upgrade to Quantum NLP. InQuanto is built on TKET, Quantinuum’s open-source agnostic software development kit used to create and execute programs.

InQuanto's ongoing research began in 2019 and resulted in several papers being published under the research name EUMEN. InQuanto was developed and deployed by Quantum's chemistry team to enable collaboration with global partners exploring quantum computing use cases tailored to their industries. Among the partners were BMW, Honeywell, JSR, Nippon Steel Corporation, and TotalEnergies.

Quantinuum Inquanto Offerings QUANTINUUM

Dr. Jenni Strabley, Senior Director of Offer Management for Quantinuum, emphasized the importance of customer collaboration. 

"InQuanto offers customers collaborative support regardless of the quantum research stage they are in," she said. "If a customer isn’t ready to launch a quantum program on their own, our quantum chemistry team will work side-by-side with =their subject matter experts to investigate the problem, understand their capabilities, and help them develop a research program." 

Dr. Strabley explained that Quantinuum also offers licensing for InQuanto, which includes access to the Honeywell System H1 trapped-ion quantum computer. Customers can optionally use other quantum services with the license.

Quantinuum General Workflow QUANTINUUM

The latest and most essential quantum chemistry algorithms can be found in InQuanto. Thanks to the flexibility of the InQuanto platform and its tools, chemists can create a workflow for their specific use case.

InQuanto includes the Variational Quantum Eigensolver (VQE) algorithm, which is the most used variational algorithm for quantum chemistry simulations on near-term quantum computers. VQE calculates the ground state energy of molecules using shallow quantum circuits in a hybrid classical-quantum looping arrangement.

Other algorithms such as ADAPT-VQE, Quantum Subspace Expansion, and penalty-driven VQE are also included in InQuanto for ground state and excited state calculations. It also allows users to select from Quantinuum's chemistry-specific noise mitigation methods.

For molecules too large to run on near-term quantum machines, InQuanto uses embedding, a classical technique that has been adapted to quantum. Embedding breaks a wavefunction into fragments. Each fragment requires fewer qubits than the whole. Individual fragment are solved quantumly, then all the fragment solutions are recombined as the ground state for the molecule’s entirety. 

Dr. Simon McAdams, Product lead for InQuanto, explained how fragmentation was recently used. 

“In our recent work with a major energy company, we modeled absorption and interaction of a carbon dioxide molecule with an adsorbent known as a metal organic framework. That is the first time this type of system has been modeled using quantum computing algorithms."

If InQuanto has as much wide-spread use as anticipated, it should lead to more user-generated research papers, which will benefit the entire ecosystem as well as advance chemical computational science.

The future of computational chemistry

Fully modeling the simplest of molecules is difficult for today’s supercomputers. As the number of electrons or atoms increases, computation grows exponentially more difficult. 

Calculating quantum interaction between a few electrons within an atom may sound simple, but it’s not. Even a supercomputer that can perform over 500,000 trillion floating point operations per second will not compute a precise solution. Unless a new classical computing technology emerges, only quantum computing has the potential to handle complex computations like that. Here are a few reasons why: 

  • There is no classical computing equivalent to quantum's combination of entanglement (a mysterious quantum force linking qubits together), superposition, and interference (a bias for the correct solution). Together, these properties of quantum mechanics allow quantum computers to process multiple logical states simultaneously. 
  • Quantum computers are inherently powerful. A small quantum computer equipped with 30 qubits is comparable to a classical computer capable of performing 10 trillion floating-point operations per second. Fault-tolerant quantum computers of the future with millions of qubits will likely be able to simulate the largest of many-body systems.
  • Qubits can be used to represent electron spin orbits, and qubit entanglement can represent electron-electron interactions.
  • Quantum computers double in power each time a qubit is added. Each year, most quantum computing companies add multiple qubits to their systems. Qubit count will begin increasing exponentially in the future if physics permits. 

Wrapping up

Quantum computational chemistry possesses the potential to address some of the world’s most complex and important applications. These include climate change, new drugs, new materials, optimization of electrical grids, efficient manufacturing of fertilizers and more.

Using InQuanto, computational chemists and quantum developers can explore the capabilities of today’s quantum computers to model industrially relevant molecules and materials.

Despite progress in error-migration and qubit fidelity, more precise calculations of large complex systems are likely to have their biggest breakthroughs with error-corrected quantum systems.

On the positive side, many experts believe a machine like that will be available within the decade. That makes it essential for enterprises to begin planning and work-force training now.

From that perspective, in addition to being a research platform, InQuanto is also a training tool. It allows researchers to prepare themselves for the eventuality of working with fault-tolerant quantum computers capable of modeling large molecules. 

Patrick Moorhead, CEO and Chief Analyst of Moor Insights and Strategy, has 25 years of experience at high tech companies leading strategy, product management, product marketing, and corporate marketing. He understands the value and importance of early training for emerging technologies. 

“Quantum computing offers a path to rapid and cost-effective development of new molecules and materials that could unlock novel answers to some of the biggest challenges we face,” he said. “The way to ensure progress is to start prototyping now, using real-world use cases, so that methods are tailored to solving actual needs of the industry. InQuanto is built to enable exactly this.”

Follow Paul Smith-Goodson on Twitter for current information on quantum and AI

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

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Patrick founded the firm based on his real-world world technology experiences with the understanding of what he wasn’t getting from analysts and consultants. Ten years later, Patrick is ranked #1 among technology industry analysts in terms of “power” (ARInsights)  in “press citations” (Apollo Research). Moorhead is a contributor at Forbes and frequently appears on CNBC. He is a broad-based analyst covering a wide variety of topics including the cloud, enterprise SaaS, collaboration, client computing, and semiconductors. He has 30 years of experience including 15 years of executive experience at high tech companies (NCR, AT&T, Compaq, now HP, and AMD) leading strategy, product management, product marketing, and corporate marketing, including three industry board appointments.