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kicked off in Santa Clara, CA where Ģ����Ƶ presented among important influencers in the quantum computing industry. Our experts presented on a range of topics including industry trends, use cases for quantum hardware, middleware and software and technology updates for an audience of global top academics, industry end users, government representatives and quantum computing vendors. Here’s a recap of Ģ����Ƶ presentations:

Industry Trends:

Great Mashups: Steady Progress meets Exponential‍

President and COO Tony Uttley‍

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Across the Pond: How UK-US partnerships are delivering on the promise of Quantum Computing

President and COO Tony Uttley

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Quantum Computing Innovation: The 2023 International State of Play

Chief Legal Officer and Chief Compliance Officer, Kaniah Konkoly-Thege��

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Use Cases for Automotive, Computational Chemistry, Finance, and More:

Entangling the Ecosystem: 5 Diverse Stories of Quantum Collaborations,

Technical Solutions Specialist, Mark Wolf, Ph.D.

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Computational chemistry on near-term quantum computers and beyond

Scientific Project Manager, Michal Krompiec

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Quantum Computing Technology:

Ģ����Ƶ H-Series features and capabilities powered through Azure Quantum hybrid quantum computing stack

Sr. Director of Offering And Program Management, Jennifer Strabley and Microsoft’s Principal PM Lead, Azure Quantum, Fabrice Frachon

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If you’d like to learn more about these presentations and topics, please reach out.��

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Ģ����Ƶ also celebrated its first anniversary with attendees. At Q2B 2021, Ģ����Ƶ announced the combination of Cambridge Quantum Computing and Honeywell Quantum, so it was only fitting to celebrate its one-year anniversary at the 2022 event.��

From Ģ����Ƶ’s launch in late 2021 as the world’s first fully integrated quantum tech company, its momentum quickly continued with the launches of and , achieving 20 fully connected qubits, expanding our work with major partners, and reaching a quantum volume of 8192, just to name a few milestones. Learn more about Ģ����Ƶ in its first year below.

When we launched InQuanto™, our computational chemistry platform for quantum computing, we explained that its origins lay at least as much with our industrial partners as it did with us. We revealed that its development was the culmination of many important scientific collaborations with some of the world’s leading industrial names in energy, automotive, pharmaceuticals, industrial materials, and other sectors.

Today, we announce the next version of our state-of-the-art platform. Just as before, it is important to us that InQuanto 2.0, while being more versatile, more extensible, and more applicable for those who have not yet explored the use of quantum computers, is the result of precisely the same spirit of collaboration.

In close collaboration with our industrial partners, we have designed, developed, and discovered methods using InQuanto for exploring the application of near-term quantum technology to material and molecular problems that remain challenging or intractable for even the most powerful classical computers.

What’s inside InQuanto 2.0?

InQuanto continues to be built around the latest quantum algorithms, advanced subroutines, and chemistry-specific noise-mitigation techniques. In the new version, we have added new features to enhance efficiency, such as new protocol classes that can speed up vector calculations by an order of magnitude, and integral operator classes that exploit symmetries and can reduce memory requirements.

We have introduced new tools for developing custom ���Բ�ä�ٳ���, new embedding techniques and novel hybrid methods to improve efficiency and precision, which in some cases have only recently been described in the scientific literature. And these rapid advances are supported by new ways for computational chemists to build InQuanto into their workflow, whether that is by improving visualization and interoperability with other chemistry packages, or by demonstrating the ability to run it in the cloud, for example, through a .

The most exciting progress, of course, is reflected in the diverse work of our partners. We know that some of the work being done today will be reflected in future methods and techniques incorporated into InQuanto, fulfilling the ever more advanced needs of our partners tomorrow.

Please book a demonstration of InQuanto 2.0 today.

InQuanto 2.0 brings together a range of new features that continue to make it the right choice for computational chemists on quantum computers:

Efficiency

• Workflow improvements in protocol classes for more efficient small test calculations — up to 10x speed-ups in some state vector calculations
• Symmetry-exploiting integral operator classes for efficient handling of the two-electron integral for a chemistry Hamiltonian using ~50% less memory
• Optimized computables for n-particle reduced density matrices

Algorithms

• Wide range of restructured ���Բ�ä�ٳ��� to support multi-reference calculations to enable new types of variational quantum algorithms — with improved custom ansatz development tools
• Generalised variational quantum solvers to perform imaginary and real-time evolution simulations
• Added Fragment Molecular Orbital embedding method
• New QRDM-NEVPT2 method to measure 4-particle reduced density matrices and add corrections to VQE energy

User Experience

• FCIDUMP read/write for improved integration with other quantum chemistry packages
• Unit cell visualization extensions, and support for trotterization in the operator level
• Improved resource cost estimation on H-Series quantum computers, Powered by Honeywell��

What to read next:

Ford battery researchers used ���Բϳܲ��Գٴ�™ to study how quantum computers could be used to model lithium-ion batteries.

Among other research, the Global Technology Applied Research (GTAR) Center at JPMorgan Chase is experimenting with quantum algorithms for constrained optimization to perform Natural Language Processing (NLP) for document summarization, addressing various application points across the firm.��

Marco Pistoia, Ph.D., Managing Director, Distinguished Engineer, and Head of GT Applied Research recently led the research effort around a constrained version of the Quantum Approximate Optimization Algorithm (QAOA) that can extract and summarize the most important information from legal documents and contracts. This work was recently published in Nature Scientific Reports () and deemed the “largest demonstration to date of constrained optimization on a gate-based quantum computer.”��

JPMorgan Chase was one of the early-access users of the Ģ����Ƶ H1-1 system when it was upgraded from 12 qubits with 3 parallel gating zones to 20 qubits with 5 parallel gating zones. The research team at JPMorgan Chase found the 20-qubit machine returned significantly better results than random guess without any error mitigation, despite the circuit depth exceeding 100 two-qubit gates. The circuits used were deeper than any quantum optimization circuits previously executed for any problem. “With 20 qubits, we could summarize bigger documents and the results were excellent,” Pistoia said. “We saw a difference, both in terms of the number of qubits and the quality of qubits.”

JPMorgan Chase has been working with Ģ����Ƶ’s quantum hardware since 2020 (pre-merger) and Pistoia has seen the evolution of the machine over time, as companies raced to add qubits. “It was clear early on that the number of qubits doesn't matter,” he said. “In the short term, we need computers whose qubits are reliable and give us the results that we expect based on the reference values.”����

Jenni Strabley, Sr., Director of Offering Management for Ģ����Ƶ, stated, “Quality counts when it comes to quantum computers. We know our users, like JPMC, expect that every time they use our H-Series quantum computers, they get the same, repeatable, high-quality performance. Quality isn’t typically part of the day-to-day conversation around quantum computers, but it needs to be for users like Marco and his team to progress in their research.”

More broadly, the researchers claimed that “this demonstration is a testament to the overall progress of quantum computing hardware. Our successful execution of complex circuits for constrained optimization depended heavily on all-to-all connectivity, as the circuit depth would have significantly increased if the circuit had to be compiled to a nearest-neighbor architecture.”

Describing the experiment��

The objective of the experiment was to produce a condensed text summary by selecting sentences verbatim from the original text. The specific goal was to maximize the centrality and minimize the redundancy of the sentences in the summary and do so with a limited number of sentences.��

The JPMorgan Chase researchers used all 20 qubits of the H1-1 and executed circuits with two-qubit gate depths of up to 159 and two-qubit gate counts of up to 765. The team used IBM’s Qiskit for circuit manipulation and noiseless simulation. For the hardware experiments, they used to optimize the circuits for H1-1’s native gate set. They also ran the quantum circuits in an emulator of the H1-1 device.

The JPMorgan Chase research team tested three algorithms: L-VQE, QAOA and XY-QAOA. L-VQE was easy to execute on the hardware but difficult to find good parameters for. Regarding the other two algorithms, it was easier to find good parameters, but the circuits were more expensive to execute. The XY-QAOA algorithm provided the best results.��

Looking ahead and across industries

Dr. Pistoia mentions that constrained optimization problems, such as extractive summarization, are ubiquitous in banks, thus finding high-quality solutions to constrained optimization problems can positively impact customers of all lines of business. It is also important to note that the optimization algorithm built for this experiment can also be used across other industries (e.g., transportation) because the underlying algorithm is the same in many cases.����

Even with the quality of the results from this extractive summarization work, the NLP algorithm is not ready to roll out just yet. “Quantum computers are not yet that powerful, but we're getting closer,” Pistoia said.�� “These results demonstrate how algorithm and hardware progress is bringing the prospect of quantum advantage closer, which can be leveraged across many industries.”

The research team behind Ģ����Ƶ's state-of-the-art quantum computational chemistry platform, InQuanto, has demonstrated a new method that makes more efficient use of today's "noisy" quantum computers, for simulating chemical systems.

In a new paper, “Variational Phase Estimation with Variational Fast Forwarding”, , a team led by Nathan Fitzpatrick and co-authors Maria-Andreea Filip and David Muñoz Ramo, explored different methods and the trade-offs required to achieve results on near-term quantum hardware. The paper also assesses the hardware requirements for the proposed method.

Starting with the Variational Quantum Phase Estimation (VQPE) algorithm,��commonly used to calculate molecular ground-state and excited state energies, the team combined it with variational fast-forwarding (VFF) to reduce the quantum circuit depth required to achieve good results.��

The demonstration made use of a , which can be used as a low-cost alternative to the traditional quantum phase estimation algorithm to estimate both the ground and excited-state energies of a quantum many-body system. The Krylov method uses time evolution to generate the subspace used in the algorithm, which can be very expensive in terms of gate depth. The new method demonstrated is less expensive, making the circuit depth required to achieve good results manageable.

The team decreased the circuit depth by using VFF, a hybrid classical-quantum algorithm, which provides an approximation to time-evolution, allowing VQPE to be applied with linear cost in the number of time-evolved states. Introducing VFF allows the time evolved states to be expressed with a lower fixed depth therefore the quantum computing resources required to run the algorithm are drastically decreased.

This new approach resulted in a circuit with a depth of 57 gates for the H₂ Molecule, of which 24 are CNOTs. This is a significant improvement from the original trotterized time-evolution implementation, particularly as the depth of this circuit remains constant for any number of steps. Whereas, the original trotterized circuit required 34 CNOTs per step, with a large number of steps required for high accuracy.

The techniques demonstrated in this paper will be of interest to quantum chemists seeking near-term results in fields such as, excited state quantum chemistry and strongly correlated materials.

The tradeoff involved in this use of VFF is that the results are more approximate. Improving this will be an area for future research.