Qubits and information in the quantum world

In quantum computing, the fundamental unit of information is a qubit, which is physically encoded in a two-level quantum system. These two levels are known as the ‘computational basis states’, and we usually write them as ∣0⟩ and ∣1⟩. These correspond directly to the 0 and 1 states of a classical bit.

However, unlike a classical bit that can only be in one state at a time (either 0 or 1), a qubit can be in a state that is in a linear superposition of basis states. This is unique to quantum computing and doesn’t have an equivalent in classical computing. A general qubit state can therefore be described as |ψ⟩= α₁∣0⟩ + α₂∣1⟩, which is a linear superposition of ∣0⟩ and ∣1⟩ with complex amplitudes α₁ and α₂.

The state of a qubit in a superposition remains in this state as long as the qubit is not measured or disturbed. However, as soon as a measurement is made, the qubit settles into one of its basis states, either ∣0⟩ or ∣1⟩, and the superposition is destroyed. This process is known as ‘collapse’, and it’s a fundamental feature of measurement in quantum mechanics. If we measure the qubit, we obtain the ∣0⟩ state with probability |α₁|²  and the ∣1⟩ state with probability |α₂|² , where the sum of both needs always equal to one (|α₁|²  + |α₂|² = 1) [2].

Types of Qubits

Currently, there are a few qubit implementations that look quite promising for the realization of a quantum computer. The most prominent examples are superconducting qubits, ion traps and spin qubits and photons.

1. Spin: Spin-based qubits use the spin of charge carriers, such as electrons, confined in a semiconductor material as the basis for information storage. The spin degree of freedom of that electron provides a natural two-level system that is insensitive to electric fields, leading to relatively long quantum coherence times. The first spin qubit quantum computer was proposed by Daniel Loss and David P. DiVincenzo in 1997 [3]. Intel has raised efforts to develop quantum computers based on silicon spin qubits in recent years [4].

2. Trapped Atoms and Ions: In trapped-ion quantum computing, ions are confined and suspended in free space using electromagnetic fields. Qubits are defined by two distinct energy states of the ions, and quantum information can be transferred through the collective quantized motion of the ions in a shared trap. The fundamental operations of a quantum computer have been demonstrated experimentally with high accuracy in trapped-ion systems [5,6].

Neutral atoms, on the contrary, are trapped by highly focused laser beams, so-called optical-tweezers. Qubits are encoded in distinct electronic energy states of the atoms, and their quantum state may be manipulated via laser pulses. As for breakthroughs, researchers at ETH Zurich have managed to trap ions using static electric and magnetic fields and perform quantum operations on them. This new ion trap could be used to realize quantum computers with far more quantum bits than have been possible up to now [7]. On the neutral atoms side, a team of researchers at Harvard created the first programmable, logical quantum processor, capable of encoding up to 48 logical qubits and executing hundreds of logical gate operations [8].

3. Photons: Photons, the quantum particles of light, can serve as qubits, too. They are natural candidates for quantum communication due to their favorable properties in speed, coherence, and low propagation loss. This makes them ideal for transmitting quantum information over long distances. Moreover, photons offer various degrees of freedom where quantum information can be encoded. For example, two distinct propagation paths, or the polarisation degree of freedom, which is the orientation of the photon’s electric field, may serve as a two-level system to encode and manipulate a qubit [9].

4. Superconducting Circuits: Superconducting circuits are among the leading physical implementations of qubits. They are tiny circuits made out of superconducting materials that can carry an electric current without resistance when cooled to very low temperatures. These circuits can behave like artificial atoms, absorbing and emitting energy at specific frequencies, which makes them suitable for use as qubits [10].

In a superconducting circuit, the quantum state of the circuit (the qubits) can be controlled using microwave pulses. These pulses can manipulate the qubit into a superposition of states, flip its state, or entangle it with other qubits. This is how quantum operations, or gates, are performed in a superconducting quantum computer. Companies like IBM, Google, and Rigetti are using superconducting circuits to build their quantum computers. These companies have already built quantum processors with hundreds to thousands of qubits and are working on scaling up their technology to create larger, more powerful quantum computers [11].

Visualization of Qubits

Qubit states can be visualized in different ways. Below are the most common ones.

• 1. Bloch Sphere: The Bloch Sphere is a mathematical tool used to represent all possible states of a single qubit. It’s a 3D representation where each qubit state is represented as a point on the surface of the sphere. The north and south poles typically represent the two computational states [12].

• 2. Q-Sphere: The Q-Sphere is a visualization tool used in quantum computing to represent the state of multiple qubits. Unlike the Bloch Sphere, which represents the state of a single qubit, the Q-Sphere can represent transformations between different multi-qubit states. Here, the size of the points is proportional to the probability of the corresponding term in the state and the color represents the relative phase between the basis states.

To try these visualizations, qiskit offers great tools for visualizing qubits. You can use the plot_bloch_vector function for Bloch Sphere visualization and the plot_state_qsphere function for Q-Sphere visualization. These functions provide an interactive and intuitive way to understand the state of your qubits.

References

[1]https://spectrum.ieee.org/transistor-history

[2] Nielsen, Michael A., and Isaac L. Chuang. 2012. “Quantum Computation and Quantum Information.” Cambridge University Press. https://doi.org/10.1017/cbo9780511976667.

[3]https://arxiv.org/pdf/2112.08863.pdf

[4]https://www.intel.com/content/www/us/en/newsroom/news/quantum-computing-chip-to-advance-research.html

[5]https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.106.130506

[6]https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.2.020343

[7]https://ethz.ch/en/news-and-events/eth-news/news/2024/03/a-new-ion-trap-for-larger-quantum-computers.html

[8]https://www.nature.com/articles/s41586-023-06927-3

[9] Couteau, C., Barz, S., Durt, T. et al. Applications of single photons to quantum communication and computing. Nat Rev Phys 5, 326–338 (2023). https://doi.org/10.1038/s42254-023-00583-2

[10] https://arxiv.org/abs/1904.06560

[11] https://www.nature.com/articles/d41586-023-03854-1

[12] https://en.wikipedia.org/wiki/Bloch_sphere

_{Traditional computers use Bits that can be either 0 or 1, but not both at once. Quantum computers, on the other hand, use Qubits which can be in a linear combination of 0 and 1, a phenomenon known as ‘Superposition’. Unlike Bits, Qubits can exist anywhere on a sphere, as shown in the image above.}

A key distinction between a classical bit and a quantum bit (qubit) is the ability of the qubit to exist in superposition states. In other words, a qubit can exist in a linear combination of basis states |0⟩ and |1⟩, which we also describe as computational states. A general qubit state can thus be expressed as

|ψ⟩ = α|0⟩ + β|1⟩,

which is a superposition of ∣0⟩ and ∣1⟩ with complex amplitudes α and β.

When a larger quantum system composed of multiple qubits is in a superposition |ψ >= α₁∣000…0⟩ + α₂∣000…1⟩ + … + α_2^n∣111…1⟩ of its composite basis states, it can perform calculations on all possible combinations of its qubits at once. As a result, quantum computers can process many possibilities in parallel and potentially solve certain types of problems much more efficiently than classical computers. This is evident in Shor’s Algorithm, where the speedup comes from the application of the quantum Fourier transform, which is a key part of the algorithm and exploits the principle of superposition.

Superposition Notation & Measurement

Contrary to classical physics, in quantum mechanics, the outcome of a measurement is governed by probabilities and randomness. This means that for a qubit in a state of superposition |ψ⟩, one can only predict the probabilities of outcomes for repeated measurements on a large number of copies of the same qubit. However, the measurement outcome of one individual measurement is entirely random and can be either |0⟩ or |1⟩. This is also the reason why, in quantum computing, we currently have to carry out several so-called shots, or executions, of the same circuit in order to get a meaningful result. We will explore quantum circuits in more depth later in another blog article.

When measuring a qubit, the superposition collapses into one of the basis states, corresponding to the particular basis we are measuring in, generally the computational basis |0⟩ and |1⟩.

Let’s consider a qubit in the general superposition state from above, represented as |ψ⟩ = α|0⟩ + β|1⟩, where α and β are complex numbers. As mentioned in our article  “From bits to qubits”,  the probabilities of measuring |0⟩ or |1⟩ are given by the squares of the absolute values of α and β, respectively. That is, P(0) = |α|^2 and P(1) = |β|^2.

However, we can choose to measure the qubit with respect to any other basis, e.g. |+⟩ = 1/√2|0⟩ + 1/√2|1⟩ and |-⟩ = 1/√2|0⟩ – 1/√2|1⟩, and the probabilities to obtain either outcome, |+⟩ or |-⟩, as a result of the measurement will change accordingly. For example, in an equal superposition state (|ψ⟩ = 1/√2|0⟩ + 1/√2|1⟩) the probabilities of measuring |0⟩ or |1⟩ are both P(0)=P(1)=50% if we measure in the computational basis; however, the probability of measuring in the |+⟩, |-⟩ basis are P(+) = 100% and P(-) = 0%.

It is important to not confuse a genuine quantum superposition with a probabilistic classical state, such as a tossed coin. The physics of the tossed coin is essentially deterministic and the probability of coming up heads or tails is 50% each due to lack of knowledge of parameters such as the tossing angle, throwing speed, etc. However, these parameters can in principle be known, and if we knew all the initial parameters of the coin at the moment of the toss, we could make a prediction (using the laws of classical physics) at which point during the toss the flipping coin would show heads or tails.

However, for the qubit in a genuine quantum superposition, even if we know every parameter there is to know about the qubit’s state, an individual measurement outcome is completely random. We can only derive the probabilities of outcomes for repeated measurements on a large number of copies of the qubit.

Superposition on the Bloch Sphere

The Bloch sphere visualizes quantum states of a qubit using a unit sphere, where each point corresponds to a unique state— the north pole is usually for |0⟩ and south pole for |1⟩. A superposition state of a qubit is visually represented as a point on the sphere’s surface. The exact coordinates of the point represent the probability amplitudes of measuring each state and the phase difference between them. In the Bloch sphere picture, the qubit state is parametrized by two real numbers, θ and φ:

|ψ⟩ = cos(θ/2)|0⟩ + e^iφ sin(θ/2)|1⟩

For example, if a qubit is in an equal superposition state (|+⟩ = 1/√2|0⟩ + 1/√2|1⟩), it would be represented by a point on the equator of the Bloch sphere where θ and φ take the values θ= and φ=0. If we want to depict the |-⟩ state on the Bloch sphere, the values would be θ=π/2 and φ=π.

References:

[1] Nielsen, Michael A., and Isaac L. Chuang. 2012. “Quantum Computation and Quantum Information.” Cambridge University Press. https://doi.org/10.1017/cbo9780511976667.

FAQ – Frequently Asked Questions

Q1: How does hybrid quantum computing work?

The core idea behind hybrid quantum computing is that quantum computers work as co-processors alongside classical systems, where both carry out parts of the computational workload. Alternative interpretations of the term hybrid, however, may vary based on contextual distinctions, including different system architectures and types of tasks taken over by the involved compute resources. For example, quantum computers always rely on classical computers to control the physical operations of the quantum device or enable remote access. Some architectural approaches also involve high-performance computing (HPC) systems to accelerate helper tasks such as compilation, error mitigation, and data preprocessing for quantum computers.

Q2: What are the benefits of hybrid quantum computing?

Quantum computers will only execute certain parts of a program. Hybrid algorithms facilitate the division of computational tasks between quantum and classical computing resources, capitalizing on the strengths of each. While numerous quantum algorithms require error correction and extensive physical resources, recently developed hybrid quantum algorithms, such as variational quantum algorithms, ensure that certain tasks can be reliably executed with current quantum technology.

Q3: What are the challenges of implementing hybrid quantum-classical algorithms?

Implementing hybrid quantum-classical algorithms poses several challenges regarding system architecture, hardware and software compatibility, resource management, and more. Overcoming these challenges requires innovative approaches from hardware design to software engineering and algorithm development. Collaboration among academia, industry, and government agencies will play a pivotal role in advancing hybrid quantum-classical computing technology.

Why are quantum computers interesting, and what can they do (better)?

First, what is a quantum computer?

Quantum computers promise supreme computational capabilities through their natural ability to process quantum information. The basic unit of quantum information is called a quantum bit (or qubit), the counterpart of a classical bit. Quantum bits are physically encoded in a two-levelled quantum system, such as the polarization of single photons or the spin of electrons. Using quantum systems for information processing allows for exploiting non-classical phenomena such as superposition, entanglement, and interference to represent data and carry out computations.

A multitude of small- to medium-scale realizations of quantum processors with a few hundred up to a thousand qubits and based on a variety of different hardware – ranging from atomic and ionic systems, superconducting qubits, to solid-state and optical platforms – already exist [1, 2]. Furthermore, experimental realizations with superconducting qubits as well as with optical setups have demonstrated superior performance compared to leading supercomputers in specific computational tasks, i.e., boson sampling [3, 4, 5]. Although the physical number of qubits has increased significantly in recent years, today’s quantum hardware still suffers from limited scaling potential, qubit coherence and connectivity, and other device imperfections. These imperfections introduce noise to the computation that ultimately destroys the valuable non-classical properties, thereby posing physical limits to the quantum computations that can be executed.

What can quantum computers do (better)?

Quantum systems offer a new computational paradigm, fundamentally different from classical computation. Theory states that for certain problems, quantum algorithms scale more efficiently with larger problem sizes than the best-known classical algorithms [1]. Therefore, quantum computers are expected to provide a computational advantage over classical computers when solving specific tasks. Well-known examples include search-related tasks, factorization of large numbers, and the simulation of molecular dynamics.

This extends further to a broad range of possible applications, including the simulation of complex engineering processes and chemical systems for the development of new materials and drug discovery, optimization of routing and supply chains in the production and logistics domain, as well as insurance risk assessment [6].

However, large-scale, fault-tolerant quantum computers that could revolutionize industries from automotive, manufacturing, chemical, and pharmaceutical production to insurance and finance remain theoretical for the time being. Today’s quantum computers are still in an early stage of development and have not yet demonstrated an advantage in solving any real-world business or industry problems. Currently, available quantum hardware is error-prone and intermediate in scale, with up to a thousand qubits, coining the term noisy-intermediate-scale-quantum (NISQ) era. One of the challenges that remains in the near future is overcoming some of their current limitations.

Hybrid quantum computing approaches

Introducing quantum for classical systems

Hybrid quantum-classical computing systems present an exciting possibility. The idea is that quantum computers work as co-processors alongside classical systems. Standard classical computers are already required to control the physical operation of quantum gates or enable remote access. In that sense, a quantum processor will always work in tandem with a classical computer, although the reverse is not true. Other architectural visions rely on high-performance computing (HPC) systems to speed up classical helper tasks such as compilation, error mitigation, error correction, preprocessing the input data for the quantum computer, as well as post-processing and analysis [7, 8]. Academia and industry are also focusing on the development of hybrid algorithms that split a computational workload between quantum and classical computing resources, aiming to use each resource for what it does best [9, 10].

Based on recent developments of NISQ devices, it is envisioned that a first quantum advantage for an industrially relevant problem will be achieved through a hybrid quantum-classical combination of traditional HPC systems with a quantum computer as an accelerator [7]. What the eventual architecture and design of such a hybrid system could look like can only be sketched today and is an extensively studied research topic. Supercomputing centers and research groups around the world are investigating this HPC-integrated approach to quantum computing. Prominent examples are Forschungszentrum Jülich, Barcelona Supercomputing Center, CINECA, Oak Ridge National Laboratory, Pawsey Supercomputing Centre, and many more.

Quantum-Classical Synergy in Action

Many proposals for quantum algorithms require error correction and demand more resources than today’s NISQ technology can provide, limiting their usefulness in the near future. However, variational quantum algorithms (VQAs) are a class of hybrid quantum-classical algorithms that show great potential and are particularly suited for use with NISQ devices [9]. They are built on a quantum-classical loop, where the quantum part consists of parameterized quantum gates whose parameters are trained and updated using a classical optimization algorithm. As the algorithm iterates, tasks are assigned to the quantum processor and the classical computer. VQAs have been demonstrated in various proof-of-concept implementations, e.g., with photonic [12] and ionic systems [13]. They also provide a realistic approach to leveraging the full capabilities of near-term hybrid systems, as they are commonly used in conventional HPC applications such as chemistry simulations, machine learning, and optimization problems [9, 11].

Real-World Applications Across Industries

Quantum computing finds applications across industries. Below, you will find some examples of recent collaborations addressing various use cases:

Quantum Mobility Quest by Airbus and BMW Group

To cite a recent announcement, Airbus and BMW Group have partnered to launch the Quantum Mobility Quest, an innovative initiative aimed at revolutionizing the transportation sector through quantum computing. This global challenge focuses on harnessing quantum technologies to address key issues in the aviation and automotive industries. Participants are invited to tackle one of five problem statements. Registration is open from mid-January to April 30, 2024, with the winning team to be announced by the end of the year.

Airbus:

In partnership with the BMW Group and Quantinuum, Airbus is working on simulating the oxygen reduction reaction on the surface of a platinum-based catalyst using quantum computing. This reaction is critical in the process that converts hydrogen and oxygen into water and electricity in a fuel cell. Understanding this process is expected to lead to the development of alternative materials that may improve the performance and reduce the production costs of fuel cells, crucial for sustainable hydrogen-powered alternatives in aviation.

Thales Group:

Thales Group, in collaboration with Terra Quantum, has applied hybrid quantum computing to optimize satellite mission planning. They’ve demonstrated significant potential for improving satellite operational utility using a hybridized quantum-enhanced reinforcement learning approach. According to The Quantum Insider, Terra Quantum and Thales Group estimate their model could generate an estimated EUR 22,500 of value creation potential per satellite per day.

Outlook and further information

Developments in (hybrid) quantum computing are rapidly emerging, driven by academic research and industry advancements. While the potential for transformative applications is immense, it is crucial to approach the technology with a balanced perspective. We believe that access to quantum computing resources is crucial for forming realistic expectations in targeted industries. That’s why QMware is building a cloud platform that supports the heterogeneous use of emulated and physical QPUs from across the technology spectrum, enabling our clients to leverage quantum acceleration for their workflows and achieve production readiness.

We understand that realizing the full promise of quantum computing requires collective effort. That’s why QMware is part of projects such as Quantum Ready and Quantum Connect in Austria, as well as the German SeQuenC initiative led by IONOS and supported by PlanQK, Fraunhofer Fokus, and the University of Stuttgart. The goal is to provide access to classical and quantum computing resources for SMEs and industries alike.

For a more detailed understanding of the current status and future outlook in this field, we recommend the latest edition of the State of Quantum Report 2024, published by Lakestar in collaboration with IQM, Open Ocean, and The Quantum Insider.

Curious about what hybrid quantum computing has in store for your business? Discover how QMware’s solutions can redefine your computing capabilities!

References

[1] Nielsen, Michael A., and Isaac L. Chuang. 2012. “Quantum Computation and Quantum Information.” Cambridge University Press. https://doi.org/10.1017/cbo9780511976667.

[2] Castelvecchi, Davide. 2023. “IBM Releases First-Ever 1,000-Qubit Quantum Chip.” Nature. Springer Science and Business Media LLC. https://doi.org/10.1038/d41586-023-03854-1.

[3] Arute, Frank, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C. Bardin, Rami Barends, Rupak Biswas, et al. 2019. “Quantum Supremacy Using a Programmable Superconducting Processor.” Nature. Springer Science and Business Media LLC. https://doi.org/10.1038/s41586-019-1666-5.

[4] Zhong, Han-Sen, Hui Wang, Yu-Hao Deng, Ming-Cheng Chen, Li-Chao Peng, Yi-Han Luo, Jian Qin, et al. 2020. “Quantum Computational Advantage Using Photons.” Science. American Association for the Advancement of Science (AAAS). https://doi.org/10.1126/science.abe8770.

[5] Madsen, Lars S., Fabian Laudenbach, Mohsen Falamarzi. Askarani, Fabien Rortais, Trevor Vincent, Jacob F. F. Bulmer, Filippo M. Miatto, et al. 2022. “Quantum Computational Advantage with a Programmable Photonic Processor.” Nature. Springer Science and Business Media LLC. https://doi.org/10.1038/s41586-022-04725-x.

[6] Andreas Bayerstadler, Guillaume Becquin, Julia Binder, Thierry Botter, Hans Ehm, Thomas Ehmer, et al. 2021. “Industry Quantum Computing Applications.” EPJ Quantum Technology. Springer Science and Business Media LLC. https://doi.org/10.1140/epjqt/s40507-021-00114-x.

[7] Bertels, K., A. Sarkar, A. Krol, R. Budhrani, J. Samadi, E. Geoffroy, J. Matos, R. Abreu, G. Gielen, and I. Ashraf. 2021. “Quantum Accelerator Stack: A Research Roadmap.” arXiv. https://doi.org/10.48550/ARXIV.2102.02035.

[8] Perelshtein, Michael, Asel Sagingalieva, Karan Pinto, Vishal Shete, Alexey Pakhomchik, Artem Melnikov, Florian Neukart, Georg Gesek, Alexey Melnikov, and Valerii Vinokur. 2022. “Practical Application-Specific Advantage through Hybrid Quantum Computing.” arXiv. https://doi.org/10.48550/ARXIV.2205.04858.

[9] Cerezo, M., Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, et al. 2021. “Variational Quantum Algorithms.” Nature Reviews Physics. Springer Science and Business Media LLC. https://doi.org/10.1038/s42254-021-00348-9.

[10] Weigold, Manuela, Johanna Barzen, Frank Leymann, and Daniel Vietz. 2021. “Patterns for Hybrid Quantum Algorithms.” Service-Oriented Computing. Springer International Publishing. https://doi.org/10.1007/978-3-030-87568-8_2.

[11] Cranganore, Sandeep Suresh, Vincenzo De Maio, Ivona Brandic, Tu Mai Anh Do, and Ewa Deelman. 2022. “Molecular Dynamics Workflow Decomposition for Hybrid Classic/Quantum Systems.” 2022 IEEE 18th International Conference on E-Science (e-Science). IEEE. https://doi.org/10.1109/escience55777.2022.00048.

[12] Peruzzo, Alberto, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J. Love, Alán Aspuru-Guzik, and Jeremy L. O’Brien. 2014. “A Variational Eigenvalue Solver on a Photonic Quantum Processor.” Nature Communications. Springer Science and Business Media LLC. https://doi.org/10.1038/ncomms5213.

[13] Hempel, Cornelius, Christine Maier, Jonathan Romero, Jarrod McClean, Thomas Monz, Heng Shen, Petar Jurcevic, et al. 2018. “Quantum Chemistry Calculations on a Trapped-Ion Quantum Simulator.” Physical Review X. American Physical Society (APS). https://doi.org/10.1103/physrevx.8.031022.

“The collaboration with QMware is a strategic step to address the growing demand for top-tier quantum solutions in the European market. Our combined expertise and resources pave the way for complex computing challenges to be solved with the ideal combination of classical and quantum resources.”

Yuval Boger

Chief Marketing Officer QuEra Computing

“At QuiX Quantum, we are fully committed to making native quantum hardware accessible for early industrial applications. QuiX Quantum’s technologies, like boson sampling, make quantum hardware integration into existing data centers a reality today. We are excited to be working with QMware to make commercial quantum advantages available at scale.”

Dr.-Ing. Stefan Hengesbach

CEO of QuiX Quantum

Quantum training: Latest collaborations.

Technikum Vienna and QMware launch summer school in quantum computing.

More about our quantum training >

QMware technology for Leibniz Supercomputing Centre.

More about our collaboration with LRZ >

McKinsey’s latest Quantum Technology Monitor illustrates the rapid adoption and exploration of quantum technology – also reflected by QMware’s recent collaborations with partners such as NVIDIA. The demonstration of QMware’s new service will enable customers to explore new industrial use cases of hybrid quantum computing, which is a combination of classical high-performance and quantum computing that delivers the highest standard of processing power available today.

The accelerated computing capabilities provided by NVIDIA A100 GPUs, coupled with OCI Compute bare-metal instances and RDMA cluster networking, will provide QMware with a diverse array of options for testing and developing commercially-valuable quantum computing applications that can be used in fields such as AI, quantum machine learning, and quantum-enhanced optimization. CUDA Quantum provides scientists with powerful simulation tools and capabilities to program hybrid CPU, GPU, and QPU systems.

Martin Peck, Vice President of Technology Software Engineering at Oracle, said: “Hybrid quantum computing has the potential to reshape how businesses operate, gain insights from data, and innovate new products and services. QMware is a front-runner in this exciting field, and we are pleased to bring the power and flexibility of Oracle Cloud Infrastructure to support them in building hybrid quantum services for enterprises.”

QMware’s CEO and co-founder Markus Pflitsch said: “We are entering an era where quantum computing is transitioning from experimental to practical. Our collaboration with Oracle symbolizes a huge step in this journey. By combining QMware’s expertise in quantum technology with Oracle’s robust cloud infrastructure, we are scaling up quantum capabilities and simplifying its accessibility for businesses across all sectors.”

QMware’s CTO and co-founder George Gesek emphasized: “We build an ecosystem where developers, researchers, and business leaders collaborate to explore quantum computing’s potential in solving real-world problems. The future of quantum computing lies in its integration into the fabric of daily business operations, and through this collaboration, we are helping to make this future a reality.”

Tim Costa, Director of Quantum Computing and HPC at NVIDIA, said: “High-performance quantum simulation is crucial for researchers to tackle the toughest challenges in quantum computing. Through collaborations with innovators such as Oracle and QMware, NVIDIA helps enable the world’s researchers to achieve breakthroughs toward useful quantum-integrated computing.”

About QMware

QMware stands as a leading provider of hybrid quantum computing cloud services, specializing in B2B Quantum as a Service. The company’s platform blends high-performance computing with advanced quantum resources, designed to augment today’s hyperscaler capabilities. Leveraging sophisticated quantum hypervisor technology, QMware manages data processing tasks, selecting the most appropriate system—classical or quantum—based on each task’s specific requirements. This strategic approach establishes QMware as a key enabler of early quantum computing solutions in the industry, catering to the growing demand for superior computing performance in optimization, simulation, and machine learning. As a member of the SeQuenC initiative, in 2022 QMware was selected to build the first quantum cloud for German industry. The project is funded by the Federal Ministry for Economic Affairs and Climate Protection. Further information can be found at qm-ware.com and LinkedIn.

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Press Contact

Mira Dechant

VP PR & Marketing of QMware

Phone: +41 795105338

E-Mail: mira.dechant@qm-ware.com

Vienna, Berlin, 23. November 2023

Developing machine learning applications that can benefit from quantum computing requires not only ML expertise, but also knowledge of the specific quantum hardware platforms and the combination of infrastructure, quantum mathematics, and machine learning. This combination of skills is difficult for individual companies to achieve, which underlines the need for interdisciplinary collaboration between all stakeholders to enable broader access to quantum machine learning. Quantum Connect brings together experts and industry partners to create a unified platform for knowledge and technology sharing.

The initiative was launched by Gradient Zero, a leading Austrian machine learning company, and funded by PQML. Through the partnership with QMware, the leading European quantum cloud company, and Anaqor, a pioneer in the European quantum ecosystem with its platform PlanQK, Quantum Connect was born.

PlanQK, a community-driven platform and ecosystem for quantum applications, with its established user base, will form the technological cornerstone of Quantum Connect and drive the exploration and development of quantum machine applications. While Quantum Connect leverages PlanQK as a DevOps platform, QMware provides unmatched back-end efficiency by delivering its innovative hybrid quantum computing approach – a combination of classical high-performance and quantum computing resources – to run quantum applications on both simulated and native quantum hardware.

Quantum Connect offers machine learning developers direct and easy access to a fully functional quantum system. Quantum Connect is dedicated to advancing quantum machine learning and building an active community in the field.

“We are excited to launch Quantum Connect with our partners QMware and Anaqor to bring Quantum Machine Learning to Austria and beyond,” says Jona Boeddinghaus, COO at Gradient Zero. “We can’t wait to connect learners of all ages and experience levels and provide tutorials and infrastructure to those who want to dive into the world of Quantum and AI.”

– End –

About Gradient Zero

Gradient Zero is a software and machine learning company focused on secure data sharing, enabling responsible use of AI, and quantum machine-learning. Based in Vienna, Gradient Zero’s team of data scientists and business experts develop software solutions across various industries, including healthcare, research, finance, and agritech.

About Anaqor – PlanQK

Anaqor is a software-focused quantum start-up in Berlin and Stuttgart, founded to help innovators develop and use the quantum solutions of tomorrow – from quantum-based simulations and optimizations to quantum machine learning. At the heart of Anaqor’s approach is PlanQK, the first open, community-based platform for quantum applications. PlanQK was initiated in 2019 and has since been continuously developed together with leading universities and companies as part of a flagship project funded by the German government. PlanQK connects developers, industrial users, researchers and providers of quantum hardware with a platform for the integration, provision, further development and monetization of quantum implementations. With over 30 successfully tested use cases and more than 100 partners, PlanQK is a pioneer in the field of platforms for quantum computing.

About QMware

QMware AG, the leading hybrid quantum cloud company, is pioneering the development of commercial solutions for hybrid quantum computing. The unique cloud platform seamlessly integrates classical high-performance computing capabilities with virtual quantum processors that include classical simulators and native quantum registers. The innovative combination of classical and quantum technology positions QMware at the forefront of making the future benefits of hybrid quantum computing economically viable for both industry and the research sector. In addition, the QMware Cloud ensures the highest data protection and security standards: customers receive access to hybrid quantum computing via GDPR- and GAIA-X-compliant online services. In 2022, QMware was commissioned as a member of the SeQuenC initiative to set up the first quantum cloud for German industry. The project is funded by the Federal Ministry for Economic Affairs and Climate Protection. Further information can be found at qm-ware.com and LinkedIn.

About PQML

PQML GmbH is a subsidiary of 2030 Green BeteiligungsgmbH, Vienna. 2030 Green invests in future technologies and markets in Austria and supports companies with access to Silicon Valley.

Vienna, Austria, March 23, 2023 – QMware, the leading provider of hybrid quantum computing with private cloud and cloud-at-customer solutions, announces its participation in the Austrian research project QuantumReady. The initiative is intended to help SME to develop industrial use cases for quantum computers and to implement them in the first case studies. In doing so, they can gain expertise in realizing higher levels of computing power in the best possible way using different computer architectures. The three-year project focuses primarily on simulations, optimization tasks and time series analyses. QuantumReady is funded by Quantum Austria, an initiative of the Federal Ministry of Education, Science and Research (BMBWF).

Behind QuantumReady is a consortium of technology and industry experts, which also brings together ESS Engineering Software Steyr, the Johannes Kepler University Linz, HAKOM Time Series and QMware under the direction of the Software Competence Center Hagenberg. With the interaction of experts from research and practice, QuantumReady will unlock the immediate benefits of today’s quantum computing applications. Hybrid quantum computing, and thus the combination of classical and quantum-based computing power, is of essential importance here. The hybrid approach pursued by QMware already offers numerous application possibilities in the economy and has clear benefits over pure quantum computing, which is currently often error-prone in the hardware.

With the combination of classical and quantum computing, the QMware Cloud offers the right platform to effectively connect existing IT infrastructure with quantum-based applications. With the results, SME can explore how they can already use quantum computing in hybrid applications and implement corresponding solutions economically.

“With QuantumReady, small and medium-sized companies in Austria have access to quantum computing technology for the first time and can test and develop quantum-based algorithms and applications in practice for their specific purposes,” explains Georg Gesek, CTO at QMware. “As a pioneer in hybrid quantum computing, we are delighted to support QuantumReady with the QMware technology and thus to develop the economic potential of quantum computing in Austria.”

Dr Ricardo Wickert, Head of Research and Development at HAKOM Time Series, says: “Our customers in energy trading and power generation have already expressed interest in researching alternatives beyond classic computing. In the QuantumReady project, we want to find out how our PowerTSM platform can be integrated into future quantum architectures.”

Prof. Dr. Robert Wille, scientific director of the Software Competence Center Hagenberg, explains: “Quantum computers will be used in practice in the next few years. It is important to prepare and build up the knowledge today so that you can benefit when the hardware is available.”

About QMware AG

QMware AG, the leading private quantum cloud company, builds the first commercially valuable applications for hybrid quantum computing. Due to its open quantum platform architecture, QMware integrates technology from hard- and software providers from the industry as well as academia into its own unique quantum computing stack: The platform consists of classical high-performance computers with virtual quantum processors, which in turn can include both classical simulators and native quantum registers. This way, QMware integrates the leading quantum technologies available on the market for high-performance computing. Leveraging the best of the classical and quantum world, the future advantage of hybrid quantum computing is already economically tangible for industry and research partners. Additionally, the setup enables the realization of modern, GDPR- as well as GAIA-X-compliant online services with hybrid quantum computing. In 2022, QMware was selected to be part of a consortium to build the first quantum cloud for the industry in Germany.

Media Contact
Mira Dechant

VP PR & Marketing QMware AG
E-mail: mira.dechant@qm-ware.com
Phone: +41 795105338

Boston, Massachusetts, and St.Gallen, Switzerland, October 25, 2023 – QuEra Computing, a leader in neutral-atom quantum computers, and QMware AG, the leading hybrid quantum computing cloud company, today unveiled their collaboration to power early use cases in hybrid quantum computing. The partnership will integrate QuEra’s leading quantum technology into QMware’s hybrid quantum computing cloud platform, making it accessible to a broad spectrum of European customers across research, industry, and academia.

QMware’s hybrid quantum computing cloud distinguishes itself through its distinctive quantum computing stack, seamlessly combining classical high-performance computers with virtual quantum processors. By integrating QuEra’s 256-qubit quantum computers, QMware is extending its platform’s capabilities and reinforcing its commitment to hybrid quantum computing.

“Integrating QuEra into the QMware Cloud platform expands our product offering from several gate-based to analog quantum processors, further emphasizing our commitment to delivering top-tier computing performance to our customers,” said George Gesek, chief technology officer, QMware.

QuEra’s unparalleled technology, including its 256-qubit Aquila-class machines and FPQA™️ technology, will address a broad number of opportunities for QMware’s customers. QuEra’s integration into QMware’s platform signifies an evolution in quantum computing, offering a blend of system size, coherence, and innovative quantum processing.

“The collaboration with QMware is a strategic step to address the growing demand for top-tier quantum solutions in the European market,” said Yuval Boger, chief marketing officer, QuEra. “Our combined expertise and resources pave the way for complex computing challenges to be solved with the ideal combination of classical and quantum resources.”

Media Contact

Mira Dechant
VP PR & Marketing QMware AG
E-mail: mira.dechant@qm-ware.com
Phone: +41 795105338

Berlin, 10 October 2023 – German research projects such as PlanQK and SeQuenC are driving development in quantum computing to ensure European digital sovereignty in this important area. In the process, political funding aims to give small and medium-sized enterprises in particular access to quantum computing. Last year, the University of Stuttgart, the Fraunhofer Institute for Open Communication Systems FOKUS and the hybrid quantum computing cloud company QMware AG joined forces under the consortium leadership of IONOS: the SeQuenC Cloud is intended to democratize quantum computing. SMEs should be able to test and use quantum computing solutions at an early stage. The new partnership between Anaqor – and thus also the PlanQK platform – and the SeQuenC initiative aims to share knowledge between the two projects and strengthen the European quantum community.

The new cooperation marks a significant milestone in German quantum research and its political funding. Also launched by the BMWK in 2020, PlanQK can now strengthen the further development of the European quantum computing ecosystem after successful implementation. As a new partner of the SeQuenC initiative, PlanQK enriches the project with a professional Quantum DevOps platform that provides companies and business users with a selection of applications. Current users of the PlanQK platform will now be the first to gain access to the SeQuenC Cloud. PlanQK is thus a successful example of political investment at the federal level and is leading the way in promoting high technology.

Michael Falkenthal, Co-Founder and Chief Technology Officer of Anaqor, the
consortium leader of PlanQK, said: “With SeQuenC, we share a mission to make the
use of quantum computing as easy and frictionless as possible. Therefore, it is a
pleasure for us to provide the PlanQK community with future access to Germany’s
first quantum cloud for industrial users and empower them to drive innovation in
quantum computing.”

Rainer Sträter, Senior Vice President Technology Office at IONOS, added: “With
SeQuenC, we are laying the foundation for the first German quantum cloud for
industrial users. The project is a major step for the future viability of German
industry. We are thus positioning Germany as a pioneer in this key technology and
strengthening Europe’s digital sovereignty.”

About SeQuenC:

SeQuenC is part of the “Digital Technologies for the Economy” funding program and plays a crucial role in creating the conditions for German industry and business to develop and operate sector-specific quantum applications. SeQuenC’s innovative cloud offering, which links cloud services, classical high-performance computing, and quantum computing, marks a groundbreaking step for Germany’s digital sovereignty in the field of quantum computing. In 2022, the Federal Ministry for Economic Affairs and Climate has commissioned the consortium with the work. In addition to consortium leader IONOS, Fraunhofer Fokus, the University of Stuttgart and QMware AG are also involved.

About PlanQK:

PlanQK is the first open community-powered platform and ecosystem for quantum applications, connecting developers, industrial users, researchers, and quantum hardware providers with a platform for the integration, deployment, development, and monetization of quantum services. With over 30 successfully tested use cases and more than 100 partners, PlanQK, is a pioneer in the field of quantum platforms. Initiated in 2019 and supported through a research grant by the German Federal Ministry of Economic Affairs and Climate Affairs it has been continuously developed in collaboration with leading universities and companies.

About Anaqor:

Anaqor is a software-focused quantum start up based in Berlin and Stuttgart founded to enable innovators who build and use tomorrow’s quantum solutions. In 2019, Anaqor initiated PlanQK – the first community-powered quantum platform and ecosystem supported by the German government and trusted to date by more than 100 organizations. PlanQK provides the technological foundation to connect all players involved in the quantum value chain to push together the boundaries of what’s possible today and create the biggest impact for society.

About Ionos:

IONOS is the leading European digitalization partner for small and medium-sized enterprises (SMEs). IONOS has more than six million customers and is active in 18 markets in Europe and North America with a globally available platform. With its Web Presence & Productivity offerings, the company acts as a “one-stop-shop” for all digitization needs from domains and web hosting to modern website builders with artificial intelligence and Do-It-Yourself solutions, from e-commerce to online marketing tools. In addition, IONOS offers cloud solutions for companies that want to move to the cloud as their business evolves.

Media Contact
Mira Dechant

VP PR & Marketing QMware AG
E-mail: mira.dechant@qm-ware.com
Phone: +41 795105338