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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

Scientists achieve groundbreaking room-temperature quantum coherence for 100 nanoseconds, propelling molecular qubits closer to practical quantum computing.

Scientists have recently managed to maintain quantum coherence in a molecular qubit for over one hundred nanoseconds at room temperature, hinting at potential breakthroughs in quantum computing.

Quantum computers could revolutionize information technology by changing the paradigm of computing. This is attributed to their basic units, called qubits, which can exist inany combination of states, unlike classical bits constrained to a definite value of 1 or 0. Due to this infinite variety of qubit states, a quantum computer should be able to easily handle computational problems that would take a conventional computer trillions of years to solve.

Scientists have successfully created qubits from particles such as photons, atoms, individual electrons, or even a superconducting loop. However, creating a qubit is one thing, building a working quantum computer out of thousands or even millions of qubits is an entirely different challenge, and attempts thus far have been fraught with substantial difficulties.

For a quantum computer to work, it is necessary to establish and manipulate subtle quantum interactions among multiple qubits a state known as entanglement. However, for this to work, the qubits themselves need to remain stable or coherent, which means keeping it in a well-defined quantum state. The problem is, coherence is difficult to maintain as it easily crumbles when qubits interact with their surroundings even radiation from space can throw them.

To solve this, a team of Japanese researchers led by Nobuhiro Yanai, associate professor at Kyushu University, has engineered a stable qubit using a special structure called a metal-organic framework. This structure involves combining pentacene molecules (made up of five connected benzene rings) with zirconium ions and organic dicarboxylate ligands. The pentacene molecules act like bridges, linking the ligands and ions together into a framework made up of both organic molecules and metal ionshence the name.

The role of the qubit was played by a pair of neighboring pentacene molecules, which were coupled and exist within five different quantum states achieved by irradiating the metal-organic framework with various wavelengths of microwave radiation.

The metal-organic frameworks nanoscale voids offer the pentacene molecules a degree of freedom, but ultimately restricts their full movement under the radiations influence, ensuring they formed a desired quantum state and remained trapped in it for a significant amount of time.

The metal-organic framework in this work is a unique system that can densely accumulate [pentacene molecules], said Yanai in a press release. Additionally, the nanopores inside the crystal enable [them] to rotate, but at a very restrained angle.

The most important result of the study was that the team could maintain coherence for more than a hundred nanoseconds at room temperature, whereas previously this could only be achieved in similar systems at incredibly cold temperatures of about -200 degrees Celsius. At such temperatures, it was possible to maintain coherence only in photonic qubits, but in addition to needing such extreme conditions to operate, quantum computers using these photon qubits suffer from photon leakage.

Maintaining cryogenic temperatures is not only expensive but complicates the entire computing setup. Thus, creating a stable qubit that operates at room temperature is an impressive and practical achievement.

Looking ahead, the scientists are optimistic about extending coherence for even longer periods. They believe that by designing improved metal-organic frameworks and identifying more suitable molecules for qubits, they can push the boundaries further.

It will be possible to generate quintet [] state qubits more efficiently in the future by searching for guest molecules that can induce more such suppressed motions and by developing suitable metal-organic framework structures, concluded Yanai. This can open doors to room-temperature molecular quantum computing.

Reference: Akio Yamauchi et al, Room-temperature quantum coherence of entangled multiexcitons in a metal-organic framework, Science Advances (2024), DOI: 10.1126/sciadv.adi3147

Feature image credit: geralt on Pixabay

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Breakthrough in quantum computing with stable room temperature qubits - Advanced Science News

This article looks at several undervalued quantum computing stocks for investors to consider. Quantum computing is an innovative technology that utilizes the principles of quantum mechanics to tackle highly intricate problems beyond the capabilities of classical computers. With the availability of real quantum hardware, a concept envisioned only 30 years ago, hundreds of thousands of developers now have access.

Engineers consistently release increasingly potent superconducting quantum processors, accompanied by pivotal advancements in software. This collective effort aims to achieve the speed and capacity required to revolutionize various industries.

In laymans terms, quantum machines differ significantly from classical computers that have existed for over half a century, marking a transformative era in computational capabilities.

Supercomputers, comprising thousands of classical CPU and GPU cores, are the go-to for scientists and engineers facing complex challenges. However, their reliance on binary code and 20th-century transistor technology limits their effectiveness, especially for highly intricate problems involving numerous interacting variables.

Classical computers often falter when dealing with complexity, such as modeling atomic interactions or detecting subtle fraud patterns. Quantum computers, leveraging quantum physics principles, offer a promising alternative.

Operating with quantum bits (qubits) that exist in multiple states simultaneously, they present a potential solution to problems deemed unsolvable by classical computers. As the real world operates on quantum physics, quantum computing emerges as a revolutionary tool for tackling previously insurmountable tasks.

Quantum computers need to operate in an extremely cold operating environment, as low as -272C, to prevent interference from thermal noise.

Lets dive into the three most undervalued quantum computing stocks in January.

IonQ (NYSE:IONQ) is a leading player in quantum computing, offering cutting-edge solutions. Utilizing trapped ions as qubits, IonQ stands out for its advanced quantum hardware. The company aims to deliver practical quantum computing power for various applications, ranging from optimization problems to complex simulations.

Last September, IonQ reported third-quarter results with $6.1 million in revenue, surpassing the upper end of its previously-communicated range. The outlook for 2023 full-year revenue and bookings has been raised once again.

The third quarter saw bookings of $26.3 million, bringing the year-to-date bookings to $58.4 million as of Q3. The company demonstrated robust growth in its commercial pipeline. It achieved a significant milestone with $100 million in cumulative bookings within the initial three years of its commercialization efforts, showcasing the strong demand for IonQs quantum computing solutions.

Shares are down about 24% over the last three months. IONQ has a market cap of $2.33 billion.

FormFactor (NASDAQ:FORM) is one of the three undervalued quantum computing stocks, is a prominent semiconductor testing and measurement solutions provider. Specializing in advanced wafer probe cards, FORM facilitates the evaluation and testing of semiconductor devices during manufacturing. The companys cutting-edge technologies contribute to developing high-performance electronic devices, including quantum computing products, across various industries.

Approximately 25% of FormFactors revenue falls under the systems category, encompassing machines utilizing probe cards. CEO Mike Slessor highlighted on an earnings call that these systems collaborate with fab customers, contributing to R&D efforts for advancing wafer and chip manufacturing techniques, particularly for materials like silicon carbide (SiC) and gallium nitride (GaN).

Notably, FormFactors quantum cryogenics systems, included in this unit, cater to the unique requirements of quantum computers, which operate in a closely monitored environment. FormFactor is vital in supporting companies developing quantum computers and chipmakers testing advanced chips and materials for extreme conditions.

For the third quarter of 2023, the company reported record systems segment revenue. Shares are up 16% over the past three months, with a market cap now at just over $3 billion.

Source: Laborant / Shutterstock.com

IBM (NASDAQ:IBM) said recently that it has developed hardware and software solutions reaching a groundbreaking point. This enables the execution of quantum circuits with 100 qubits and 3,000 gates, devoid of known answers. Accordingly, this marks a pivotal moment where quantum becomes a practical computational tool.

I like to say users are using quantum computing to do quantum computing, and we are adding capabilities that open up quantum to an extended set of users that includes what we refer to as quantum computational scientists. We think this is proof enough that weve entered a new era, the company said in a blog post.

IBM recently unveiled IBM Condor, a remarkable leap in quantum processing with a 1,121 superconducting qubit quantum processor. Built on cross-resonance gate technology, Condor achieves a 50% increase in qubit density, pushing the boundaries of chip design scalability and yield. Despite its significantly expanded scale, Condors performance remains comparable to its predecessor, the 433-qubit Osprey.

IBM stock is up about 20% over the past three months. However, its multi-year performance still lags other Big Tech stocks, leaving room for shares to re-rate higher on the companys increasing exposure to next-gen technologies like quantum computing, AI, ML, etc.

On the date of publication, Shane Neagle did not hold (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.

Shane Neagle is fascinated by the ways in which technology is poised to disrupt investing. He specializes in fundamental analysis and growth investing.

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The 3 Most Undervalued Quantum Computing Stocks to Buy in January - InvestorPlace

January 23, 2024

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The era of quantum computing is on its way as governments and private sectors have been taking steps to standardize quantum cryptography. With the advent of the new era, we are faced with new opportunities and challenges. This article will outline the potential impact of quantum computing and discuss strategies for preparing ourselves amid these anticipated changes.

In 1980, Paul Benioff first introduced Quantum Computing (QC) by describing the quantum model of computing. In classical computing, data is processed using binary bits, which can be either 0 or 1, whereas quantum computing uses quantum particles called qubits. Qubits can be in multiple states beyond 0 or 1, making them much faster and more powerful to perform calculations than a normal bit. To be more specific, with a quantum computer, we can finish a series of operations that would take a classical computer thousands of years in just hundreds of seconds. In fact, IBM just launched the first quantum computer with more than 1,000 qubits in 2023.

Nevertheless, the speed boost of quantum computing can have double-edged consequences. Modern cryptographers have been concerned about the potential impacts on the security of public-key crypto algorithms. Those regarded as unbreakable are now at risk, as a cryptographically relevant quantum computer (CRQC) can do short work of decryption. For instance, the most popular public-key cryptosystem, Rivest-Shamir-Adleman (RSA), was previously considered very challenging with its complex inverse computation. However, in Shors algorithm where quantum speedup is particularly evident, the once reliable computation time becomes CRQC-vulnerable. As such, the US National Institute of Standards and Technology (NIST) has been promoting the standardization of post-quantum cryptography (PQC). In addition, the National Security Memorandum (NSM-10) was issued in 2022 in response to the threat brought by cryptographically relevant quantum computers (CRQC).

In fact, when it comes to quantum computing, there are still many issues that researchers cannot agree on. In the current noisy intermediate scale quantum (NISQ) era, it is still unclear what the ideal architecture of a quantum computer is, when we can expect the first CRQC, and how many qubits we will need for a quantum computer. Take the minimum number of qubits would qualify a quantum computer as an example. Google estimated that it may be 20 million qubits. But with a different quantum algorithm, Chinese researchers in 2022 proposed their own integer factoring algorithm, claiming that only 372 qubits are needed to break a 2048-bit RSA key.

Despite the various quantum computing issues, researchers have a consensus on the necessity and urgency of the PQC transition. Based on the guidelines proposed by both public and private sectors, we have concluded the following key points for a smooth PQC transition:

The above suggestions are, in fact, not dependent on the PQC standards, and the preparations can start now. It is important to keep in mind that overall system security remains the top priority in both classical computing and the PQC era. The scope of the transition will not really affect all the classical cryptographic algorithms we are familiar with. That is, the current NIST-recommended AES-256 cipher and SHA-384 hash algorithms are still acceptable (yet not satisfying) in the post-quantum world.

The full transition to PQC may span many years, giving us more time to examine PQC readiness and stay crypto-agile. According to the National Security Memorandum (NSM-10), the winners of the final round of NISTs PQC Standardization are expected to be announced in 2024, so organizations are suggested to start the timer then. Table 1 compares those algorithms that have already been selected for NIST standards with their classical counterparts in terms of public key and ciphertext/signature size (in bytes). More importantly, any systems built today should maintain the ability to stay flexible enough to account for possible future modifications, understanding that what may appear quantum-safe today may not be so soon.

Table1: Candidates of NISTs PQC Standardization

Security concerns and levels will continue to evolve as quantum computing advances. This makes a more robust safety storage system, such as NeoPUF, necessary. When all is said and done, security is all about trust. Without the foundation of trust, the classical RSA public-key algorithm or a lattice-based PQC algorithm becomes ineffective. Since important system keys should be highly random and unable to be guessed, the secure methods for creating trust in a system will become increasingly important in the post-quantum world.An even stronger base of trust, a hardware root of trust (HRoT), must be implemented in the hardware, as the software root of trust alone is no longer considered sufficient. The most robust form of such internal provisioning is PUF-based. Having delivered trust on multiple foundry platforms, eMemory and its subsidiary PUFsecurity are highly credible. Experienced solution providers such as eMemory and PUFsecurity will still be the best choice now and moving into the post-quantum world.

To learn more about Post-Quantum Cryptography, please read the full article on PUFsecurity Website.

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Preparing for Post-Quantum Cryptography: Trust is the Key - Embedded Computing Design