Our Approach

Why superconducting?

Among many physical platforms, superconducting quantum hardware is well-suited for scaling the number of qubits and improving their fidelity while maintaining connectivity and thus becomes a preferred technology in the NISQ (Noisy Intermediate Scale Quantum) era with roadmaps towards fault tolerance. This is an easy and cost-effective way to establish a quantum program based on the existing expertise in microwave electronics.
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 As superconducting quantum hardware remains the leading platform for large industry vendors, this becomes a must-have experience for future quantum talent. The investment in the overall quantum computing market was $2.3 billion in 2022, this number is expected to grow at a compound annual growth rate of 11.5% from 2023 through 2027, reaching approximately $16.4 billion by the end of 2027.

Quantum computing is now

Quantum computers are no longer just a theoretical concept. At IQM, we are already constructing these machines, and they are being used for research and education at institutions around the world. While quantum computers aren't yet ready to replace traditional computing, the technology is advancing rapidly, and industrial applications of quantum computing may be possible in the near future.

That's why we're also working with partners from industry and academia to unlock the algorithms for this era of quantum utility, which would mean we could solve some of these very hard problems faster, better, or with fewer resources than with traditional computing. And because quantum algorithms are so different from traditional algorithms, there are new skills to master. That is why we are committed to driving quantum education and creating the workforce needed to ensure that we all benefit from advances in quantum computing.

Applications and Algorithms

Quantum computing can revolutionize our ability to simulate the behavior of complex quantum systems, such as molecules and materials.

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Quantum simulation has the potential to lead to breakthroughs in fields such as:

Drug discovery: Simulating the interactions of molecules could help scientists design new drugs with improved efficacy and reduced side effects.
Material science: Simulating the behavior of materials could lead to the development of new materials with enhanced properties, such as superconductors, batteries, and catalysts.
Energy research: Simulating the behavior of molecules involved in energy production could lead to the development of more efficient and sustainable energy sources.

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Quantum computing can also be used to solve optimization problems, which are problems that involve finding the best solution from a set of possible solutions.

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Quantum optimization has the potential to improve a wide variety of algorithms and decision-making processes, including:

Route planning: Quantum computers could be used to find the most efficient routes for transportation networks, such as shipping routes or delivery schedules.
Financial modeling: Quantum computers could be used to optimize investment portfolios and risk management strategies.
Scheduling tasks: Quantum computers could be used to schedule tasks in a way that minimizes delays and maximizes resource utilization.

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Quantum computing has the potential to revolutionize machine learning, which is the field of computer science that deals with the ability of computers to learn from data.

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Quantum machine learning has the potential to improve a wide variety of tasks, including:

Pattern recognition: Quantum computers could be used to identify patterns in data more efficiently, leading to better classification and prediction models.
Natural language processing: Quantum computers could be used to process and understand natural language more effectively, leading to better machine translation and text summarization.
Recommender systems: Quantum computers could be used to recommend products, services, and content more accurately and personalized.

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Chip design QPU architecture
with tunable couplers

With our latest benchmarks measured on the 20-qubit quantum computer, we have demonstrated a median two-qubit (CZ) gate fidelity of 99.51% across 30 qubit pairs, with maximum fidelity over a single pair reaching as high as 99.8%.

Among the system-level benchmarks IQM obtained:

Quantum Volume (QV) of 2⁵=32
Circuit Layer Operations Per Second (CLOPS) of 2600.
20-qubit GHZ state with fidelity greater than 0.5.
Q-score of 11

Qubit count

Largest GHZ genuinely
entangled stated

1Q fidelity

median 99.92 %

best 99.9440 ± 0.0001 %

2Q fidelity

median 99.51 %

best 99.82 ± 0.02 %

Quantum volume

(Classical simulation complexity)

CLOPS

(Quantum circuit execution speed)

2600

Q-score

(Size of combinatorial optimization task solved)

Our approach

Why superconducting?

Quantum computing is now

Applications and Algorithms

Chip design QPU architecture
with tunable couplers

Latest results for 20-qubit Quantum Processors

Explore further:

Quantum computing
- introduction

IQM KQCircuits - Automate the design of superconducting quantum circuits

Our approach

Why superconducting?

Quantum computing is now

Applications and Algorithms

Chip design QPU architecturewith tunable couplers

Latest results for 20-qubit Quantum Processors

Explore further:

Quantum computing- introduction

IQM KQCircuits - Automate the design of superconducting quantum circuits

Chip design QPU architecture
with tunable couplers

Explore further:  

Quantum computing
- introduction