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PARIS, Jan. 23, 2024 Alice & Bob, a leading hardware developer in the race to fault tolerant quantum computers, in collaboration with the research institute Inria, today announced a new quantum error correction architecture low-density parity-check (LDPC) codes on cat qubits to reduce hardware requirements for useful quantum computers.

The theoretical work, available on arXiv, advances previous research on LDPC codes by enabling the implementation of gates as well as the use of short-range connectivity on quantum chips. The resulting reduction in overhead required for quantum error correction will allow the operation of 100 high-fidelity logical qubits (with an error rate of 10-8) with as little as 1,500 physical cat qubits.

Over 90% of quantum computing value depends on strong error correction, which is currently many years away from meaningful computations, said Jean-Franois Bobier, Partner and Director at the Boston Consulting Group. By improving correction by an order of magnitude, Alice & Bobs combined innovations could deliver industry-relevant logical qubits on hardware technology that is mature today.

This new architecture using LDPC codes and cat qubits could run Shors algorithm with less than 100,000 physical qubits, a 200-fold improvement over competing approaches 20 million qubit requirement. said Thau Peronnin, CEO of Alice & Bob. Our approach makes quantum computers more realistic in terms of time, cost and energy consumption, demonstrating our continued commitment to advancing the path to impactful quantum computing with error corrected, logical qubits.

Cat qubits alone already enable logical qubit designs that require significantly fewer qubits, thanks to their inherent protection from bit flip errors. In a previous paper by Alice & Bob and CEA, researchers demonstrated how it would be possible to run Shors algorithm with 350,000 cat qubits, a 60-fold improvement over the state-of-the art.

LDPC codes are a class of efficient error correction codes that reduce hardware requirements to correct errors occurring in information transfer and storing. By using LDPC codes on a cat-qubit architecture, this latest work not only shows how the qubit footprint of a fault tolerant quantum computer could be further reduced but overcomes two key challenges for the implementation of quantum LDPC (qLDPC) codes.

Alice & Bob recently announced the tape out of a chip that would encode their first logical qubit prototype, known as Helium 1. When logical qubits with a sufficiently low error rate are implemented and using the cat qubit LDPC code technique, Alice & Bob would be capable of harnessing the computing power of 100 logical qubits with as little as 1,500 physical qubits, to run fault-tolerant algorithms.

As leading superconducting quantum computing manufacturers like IBM offer up to 1,121 physical qubits, outperforming classical computers in the simulation of quantum systems (quantum supremacy) is a milestone that would become attainable within current hardware capabilities using Alice & Bob new architecture.

In previously proposed qLDPC codes implementation, most notably IBMs last years paper, long-range qubit connectivity and high-weight stabilizers were required, which represent a daunting technical challenge. In contrast, Alice & Bobs combined approach of cat qubits with classical LDPC codes allows the use of short-range, local qubit interactions and low-weight stabilizers.

This simpler architecture enables for the first time the implementation of a fault-tolerant set of parallelizable logical gates without additional hardware complexity. Allowing for logical gates is a necessary step for the implementation of quantum algorithms and practical quantum computing altogether.

About Alice & Bob

Alice & Bob is a start-up based in Paris and Boston whose goal is to realize the first universal, fault-tolerant quantum computer. Founded in 2020, Alice & Bob has already raised 30M in funding, hired over 80 employees, and demonstrated experimental results surpassing those of technological giants like Google or IBM. Alice & Bob specializes in cat qubits, a technology pioneered by the companys founders and later adopted by Amazon. Demonstrating the power of its cat architecture, Alice & Bob recently showed it could reduce hardware requirements to build a large-scale useful quantum computer by up to 200 times compared to competing approaches.

Source: Alice & Bob

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Alice & Bob Advance Quantum Computing with Fewer Qubits Needed for Error Correction - HPCwire

While Wall Street seemingly cant get enough of artificial intelligence, a paradigm shift in the computing ecosystem could draw more eyeballs to quantum computing stocks. According to Investopedia, the underlying arena leverages the principle of quantum theory, which explains the behavior of energy and material on the atomic and subatomic levels.

Now, we dont need to turn this article into a science journal. However, when one of the fascinating components of quantum mechanics is the ability for particles to exist in more than one state at the same time. As it relates to the digitalization protocol, quantum computers could theoretically solve and run multiple problems and processes simultaneously. Its a massive accelerant compared to the classical approach, making quantum computing stocks a must-watch arena.

To be sure, its a young and burgeoning field. What Im trying to say is that myriad risks exist. Nevertheless, this innovation can swing open doors regarding productivity acceleration and even the strengthening of national security.

If you can handle the risk, below are compelling quantum computing stocks to buy and hold.

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In recent years, Ive shied away from running my mouth against the collective wisdom of Wall Street analysts. They get paid tons of money so they should know what theyre doing (I guess). But when it comes to IBM (NYSE:IBM), Im going to make an exception. Its easily one of the quantum computing stocks to buy and hold.

You can make the argument that if theres any enterprise that truly meets that definition for instance, if you theoretically had to make a claim under oath IBM stands alone. Yes, its a consensus hold and yes, the average price target of $163.85 implies significant downside risk. Im undeterred. First, were talking about a company that was around when William Howard Taft was the President of the United States. Its staying for the long haul.

More importantly, IBM delivered profound innovations in artificial intelligence, machine learning and enterprise cybersecurity. It continues to push the envelope with its various quantum computing technologies. While youre waiting for the narrative to pan out, Big Blue offers a forward dividend yield of 3.84%. Combined with an undervalued forward earnings multiple of 17.49X, IBM makes a great case for quantum computing stocks.

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Ive also been avoiding talking about this next company but for basically opposite reasons to IBM. Seemingly, no one can get enough of Nvidia (NASDAQ:NVDA). With its advanced graphics processing units (GPUs) undergirding AI applications, NVDA managed to skyrocket last year. Even more impressive, it has taken the momentum into this year. Since the beginning of January, NVDA popped up nearly 24%.

To be sure, analysts peg NVDA as a consensus strong buy and thats not a cheap assessment either. It breaks down as 33 buys and four holds. No ones going to be reckless enough to issue a sell rating. However, the concern many have is that NVDA lacks the legs to complete another marathon. Still, the experts disagree, projecting a price target of $674.68 or over 13% up.

While Nvidia generates headlines for its AI-related products, its also investing in quantum computing. Last year, the enterprise announced a new system to help build new applications that combine quantum with the most advanced classic computing hardware. With so many relevancies in key areas, NVDA legitimately makes a case for quantum computing stocks to buy and hold.

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Moving into the pure-play arena of quantum computing stocks, D-Wave Quantum (NYSE:QBTS) claims to be the worlds first company to sell computers that exploit quantum effects in their operation. While that may sound like an outrageous statement, D-Wave backs up the hype with substance. Specifically, it has partnered with several high-profile institutions in the past, including Lockheed Martin (NYSE:LMT) and the University of Southern California.

Looking ahead, D-Wave Quantum may offer the best upside among quantum computing stocks. Currently, QBTS enjoys a unanimous strong buy rating among three analysts within the past three months. If you extend out to the trialing year, that comes out to four buys. Moreover, the average price target clocks in at $2.17, implying over 177% upside potential.

Now, while D-Wave purports to offer quantum applications to address myriad problems from portfolio optimization to traffic congestion, the harsh reality is that QBTS is a literal penny stock. That means it could be a top performer or it could become one of the worst. Still, if you have some pocket change lying around, why waste it? Instead, QBTS seems a solid speculative opportunity.

On Penny Stocks and Low-Volume Stocks:With only the rarest exceptions, InvestorPlace does not publish commentary about companies that have a market cap of less than $100 million or trade less than 100,000 shares each day. Thats because these penny stocks are frequently the playground for scam artists and market manipulators. If we ever do publish commentary on a low-volume stock that may be affected by our commentary, we demand thatInvestorPlace.coms writers disclose this fact and warn readers of the risks.

Read More:Penny Stocks How to Profit Without Getting Scammed

On the date of publication, Josh Enomoto did not have (either directly or indirectly) any positions in the securities mentioned in this article.The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

A former senior business analyst for Sony Electronics, Josh Enomoto has helped broker major contracts with Fortune Global 500 companies. Over the past several years, he has delivered unique, critical insights for the investment markets, as well as various other industries including legal, construction management, and healthcare. Tweet him at @EnomotoMedia.

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3 Quantum Computing Stocks to Buy and Hold Forever: January 2024 - InvestorPlace

A Taiwanese research institute has connected to the internet what is being reported as the nation's first home-grown quantum computer.

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The announcement of the successful connection occurred on January 19 and details Taiwan's first domestically built quantum computer that will be used as a test bed for researchers both on-site and elsewhere around the world. Collaborators on the project include the University of California, Santa Barbara, the University of Wisconsin-Madison, the Industrial Technology Research Institute, the National Changhua Normal University, the National Central University, and the National Chung Hsin University.

Academia Sinica's website states the quantum computer was completed back in October last year, but it was only connected to the internet in January this year, marking a milestone in Taiwan's exponential development into quantum computing. The intent of this step forward in the quantum computing industry is to demonstrate the capabilities of the new technology by solving basic problems. After that has been successfully demonstrated, researchers can move on to the next breakthrough application.

"The success of this project at this stage should prove the characteristics of technological research and development," said James Liao, president of Academia Sinica. "Only after a period of patiently solving basic problems can the next application breakthrough be achieved."

"It is hoped that Academia Sinica's small step will drive the development of quantum research and related industries in Taiwan, attract more domestic and foreign talents to participate in the event, and seize opportunities for Taiwan in the field of quantum technology," added Liao

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Taiwan's first home-grown quantum computer is now connected to the internet - TweakTown

Quantum Reinforcement Learnings Impact on AI Evolution

The quickly developing field of quantum computing has great promise for enhancing machine learning on conventional computers. Quantum computers are more effective than conventional computers because they can manage complex connections between inputs. These quantum computers provide ten times greater data processing and storage capacity than modern supercomputers.

Quantum computing is an area of study that integrates computer science, physics, and mathematics to tackle complicated problems more quickly than ordinary computers. To solve specific problems, it employs quantum mechanical processes such as coherence and quantum interference. Machine learning, efficiency, and modeling physical systems, as well as portfolio optimization and chemical simulation, will be future uses.

Qubits store data using what is known as the superposition principle in quantum computing. This enables qubits to be in many states at the same time. Quantum machine learning (QML) augments regular machine learning software with quantum devices. Quantum computers offer substantially more storage and processing power than ordinary computers, enabling them to analyze massive volumes of data that older technologies would take much longer to handle. With this extraordinary processing power, QML can speed and improve the development of machine learning models, neural networks, and other kinds of quantum artificial intelligence (AI).

Four major types of data, based on quantum (Q) or classical type and previous computation on Q or C computers, are derived from the blend of quantum and machine learning.

1. CC: Classical Dataset analyzed in Classical Computers Classical Machine Learning (ML) is a method that is unlikely to have a direct quantum base but draws principles from quantum machine learning theory.

2. QC: Quantum Dataset in Classical Computers learns from quantum states of consciousness using classical machine learning challenges. This technique would address the problem of classifying quantum states produced by physical experiments.

3. CQ: Quantum Computers Handle Classical Datasets In quantum computers, traditional datasets are processed. In a nutshell, quantum computers are employed to find faster solutions to problems that have previously been solved using ML. Traditional algorithms, like picture categorization, are fed into quantum machines to discover the best algorithm parameters.

4. QQ: Using quantum computers that work solely on quantum states would be the purest way. The outcome of a quantum simulation is fed into a machine learning system.

1. Positive and negative interference are used in quantum neural network training.

2. Multi-state exploration and convergence are accelerated by quantum reinforcement learning.

3. Run-time optimization: providing speedier outcomes; Enhancements to learning capacity: enhancing the capacity of connection or content-addressable memory.

4. Advances in learning efficiency: Depending on the degree of training knowledge required, the same data may be used to learn more complicated relations or simpler models.

1. Limited quantum hardware: In the current environment, Noise Intermediate-Scale Quantum (NISQ) systems must limit qubit availability for modeling reasons. Millions of qubits are expected to be required for practical usefulness.

2. Creating data that is quantum-ready: It is difficult to encode standard data using quantum state representations. Today, the bulk of data lacks underlying quantum structure.

3. Algorithm design: To reap the benefits of QML, new quantum-optimized machine learning frameworks and approaches, such as deep learning, are required.

4. Software infrastructure: Because quantum development frameworks are presently in their infancy, integrating them with regular Machine Learning technologies and workflows is difficult.

5. Training Datasets are Limited: There is insufficient labeled quantum data available. Although artificial dataset generation is advantageous, it has limitations.

6. Inadequate skills: Only a handful of academics are currently working on QML at the intersection of quantum research and AI.

Quantum Machine Learning (QML) is a new field of AI and quantum computing that has the potential for spectacular outcomes due to developments in quantum equipment, algorithms, and academic-engineer collaboration. Take classes, join clubs, or experiment with cloud-based technologies to participate in this exciting future.

People Also read The Role of Reinforcement Learning in NLP

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Quantum Reinforcement Learnings Impact on AI Evolution | by The Tech Robot | Jan, 2024 - Medium

Thales announced the launch of its PQC Starter Kit in collaboration with Quantinuum. This offering helps enterprises prepare for Post-Quantum Cryptography (PQC).

The kit provides a trusted environment for businesses to test quantum-hardened PQC-ready encryption keys and understand the implications that quantum computing will have on the security of their infrastructure.

While 73% of organizations recognize quantum computing poses a threat to traditional cryptography, 61% have yet to define a strategy for a post-quantum world. Post Quantum Cryptography helps mitigate this threat. As a result, organizations around the world must test their ecosystem applications, data, and devices currently relying upon traditional cryptography to ensure minimal disruption when quantum-safe protocols become mandatory.

Thales is excited to offer a new solution to its customers to help them prepare for the implementation of Post-Quantum Cryptography. We understand the enormous challenges and complexities behind this upcoming disruption in cryptography and want to support customers as they transition to these new algorithms.. For organizations unsure of navigating this transition, we highly recommend testing current applications, data, and devices that use cryptographic protection as soon as possible to ensure a smooth shift to PQC. Although quantum computing may seem like a future-looking risk, with hackers using Harvest Now, Decrypt Later tactics, post-quantum resilience should be on every organizations radar today, said Todd Moore, Global Head of Data Security Products at Thales.

Hardening encryption keys is critical for the post-quantum era, and Quantum Origin is a unique technology that provides verifiable quantum randomness to maximize encryption key strength. The combination of Quantum Origin and the Thales HSM is a strong solution for IT teams to help them with their PQC transition. We look forward to working with Thales to help smooth the shift to PQC, added Duncan Jones, Head of Cybersecurity at Quantinuum.

The PQC Starter Kit will allow organizations to test within a trusted lab environment. Using the current NIST proposed algorithms that are built into the system, customers can test various security use cases including PKI, code-signing, TLS, and IoT, and observe the impact of implementing PQC technology in these simulated test-lab scenarios, all without impacting operational processes in real-world production environments.

Organizations will also be able to identify potential weaknesses in their encryption deployment and apply changes to their IT infrastructure to protect themselves.

The first available PQC Starter Kit option incorporates Luna HSMs and Quantinuums quantum random number generation (QRNG) technology through which customers can ensure their keys are securely generated and stored while testing the PQC algorithms. The kit offers a choice of Luna HSMs (i.e. appliance or PCIe card) and Quantinuums Quantum Origin the worlds only source of verified quantum entropy. A PQC Starter Kit for network encryption using Thales High Speed Encryptors (HSE) will be available next.

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Thales and Quantinuum strengthen protection against quantum computing attacks - Help Net Security