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ChatGPT and its wordsmith capabilities are all over the news, and for good reason. The large language model (LLM) at the heart of the chatbot can create impressive results, with some even claiming the era of human writers is nearing its end.

While the reality is most likely less catastrophic for us human writers, we must admit that the question of whether AI will end up taking our jobs did pass through our minds once or thrice. So, let's check out whether our readers can spot the difference between a human writer and an AI.

Instead of writing an entire essay and then letting ChatGPT come up with its own version of the same topic, we've decided to compare humans and ChatGPT based on short-form answers to various tech-related questions. We took some answers from TechSpot explainer articles and wrote some additional ones that are less "conceptual" to see what GPT 4.0 came up with.

Each question below features two answers: one made by a human and the other provided by ChatGPT. They're listed randomly on the left and on the right, and it's up to you to spot the difference. Can you tell which is which? You can click the poll below each answer and see how you do.

Before we start, a couple of additional disclaimers: first, this piece is not meant to be a formal experiment, just a fun take on the human vs. AI discussion. Second, if you've used ChatGPT before, then you know the bot is known to spew paragraphs and paragraphs of text, even when you ask basic questions. This is why we had to include an 80-word limit in our prompts; otherwise, the answers generated by ChatGPT would be considerably longer, unnecessarily ballooning this piece.

Let's get started.

Manufacturing processes: Which is the human answer?

The human answer is on the left. GPT's answer isn't completely accurate.

Throttling: Which is the human answer?

The human answer is on the left.

OLED and LCD: Which is the human answer?

The human answer is on the right.

PCIe lanes: Which is the human answer?

The human answer is on the right. GPT's answer is only partially correct.

Chip binning: Which is the human answer?

The human answer is on the right.

SATA SSD fit: Which is the human answer?

The human answer is on the right. GPT's answer is unaware of SATA-to-M.2 adapters even though they are a niche solution.

M.2 SSD to SATA: Which is the human answer?

The human answer is on the left.

Cryptography: Which is the human answer?

The human answer is on the right.

Quantum computing: Which is the human answer?

The human answer is on the left.

Path tracing: Which is the human answer?

The human answer is on the left. Also, GPT got it wrong this time.

SSD trimming: Which is the human answer?

The human answer is on the right.

So, how did you do? How many answers did you get right (out of 11)?

Continued here:
We Asked GPT Some Tech Questions, Can You Tell Which Answers ... - TechSpot

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by Ingrid Fadelli , Phys.org

Researchers at University of Oxford have recently created a quantum memory within a trapped-ion quantum network node. Their unique memory design, introduced in a paper in Physical Review Letters, has been found to be extremely robust, meaning that it could store information for long periods of time despite ongoing network activity.

"We are building a network of quantum computers, which use trapped ions to store and process quantum information," Peter Drmota, one of the researchers who carried out the study, told Phys.org. "To connect quantum processing devices, we use single photons emitted from a single atomic ion and utilize quantum entanglement between this ion and the photons."

Trapped ions, charged atomic particles that are confined in space using electromagnetic fields, are a commonly used platform for realizing quantum computations. Photons (i.e., the particles of light), on the other hand, are generally used to transmit quantum information between distant nodes. Drmota and his colleagues have been exploring the possibility of combining trapped ions with photons, to create more powerful quantum technologies.

"Until now, we have implemented a reliable way of interfacing strontium ions and photons, and used this to generate high-quality remote entanglement between two distant network nodes," Drmota said. "On the other hand, high-fidelity quantum logic and long-lasting memories have been developed for calcium ions. In this experiment, we combine these capabilities for the first time, and show that it is possible to create high-quality entanglement between a strontium ion and a photon and thereafter store this entanglement in a nearby calcium ion."

Integrating a quantum memory into a network node is a challenging task, as the criteria that need to be fulfilled for such a system to work are higher than those required for the creation of a standalone quantum processor. Most notably, the developed memory would need to be robust against concurrent network activity.

"This means that the quantum information stored in the memory must not degrade while a network link is established," Drmota explained. "This requires extreme isolation between the memory and the network, but at the same time, there also needs to be a fast and reliable mechanism that couples the memory to the network when needed." View inside the vacuum chamber, where we trap strontium and calcium ions using electric fields and lasers. Credits: David Nadlinger.

To create their quantum memory, Drmota and his colleagues used two different atomic species, namely strontium and calcium, as this allowed them to minimize crosstalk while establishing a network link. The limited crosstalk in this mixed-species architecture also allowed them to detect errors in real-time and to utilize what is known as in-sequence cooling. Mixed-species entangling gates provided the missing connection between the network and the memory.

"One of the technical error sources that we face with trapped-ion qubits is dephasing due to magnetic field noise," Drmota said. "Nevertheless, calcium-43 features transitions that are insensitive to magnetic fields, eliminating this error, hence boosting their coherence time. While strontium-88 is perfectly suited for generating photons for networking, it is sensitive to magnetic field noise."

Although strontium-88 is known to be sensitive to magnetic field noise, the researchers were able to preserve entanglement between their memory ion and a photon for a longer time, by transferring quantum information from the strontium to calcium in the system. Specifically, they could preserve this entanglement for over 10s, which is at least 1000 times longer than they observed between a bare strontium ion and a photon.

"Furthermore, the strontium ion can be reused to generate further entangled photons, and we show that this process does not affect the fidelity of entanglement between the memory and the previous photon, hence achieving robustness to network activity," Drmota said. "Notably, we managed to integrate the complexity associated with multiple challenging techniques, which have been developed in isolation in different setups over many years, in a single experiment."

In initial tests, the quantum memory created by Drmota and his colleagues achieved very promising results, as it was found to be highly robust, preserving entanglement between a trapped ion and photon for at least 10s. The team's demonstration of this quantum memory could be an important milestone on the ongoing quest to realize distributed quantum information processing.

Using their design, individual quantum computational nodes can be loaded with a given number of processing qubits (i.e., calcium), while the network qubit (i.e., strontium) can then be used to create quantum links between distant modules. Ultimately, this promising quantum memory could pave the way towards the creation of scalable quantum computing systems, as using small modules that can process quantum information and interconnecting them with other modules circumvents the need for large and complex ion traps.

"The robust quantum memory could be used in quantum repeaters, for private (blind) quantum computation, and is key for new developments in quantum communications, metrology and time keeping," Drmota added. "For example, for the nascent field of entangled atomic clocks, the long entanglement storage durations achieved in our experiments will lead to an order-of-magnitude improvement in the precision of frequency comparison between distant clocks."

More information: P. Drmota et al, Robust Quantum Memory in a Trapped-Ion Quantum Network Node, Physical Review Letters (2023). DOI: 10.1103/PhysRevLett.130.090803.

Journal information: Physical Review Letters

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A robust quantum memory that stores information in a trapped-ion quantum network - Phys.org

Reps. Randy Feenstra, R-Iowa, and Haley Stevens, D-Mich., have introduced new legislation that aims to apply the power of quantum computing to assist the agriculture industry and streamline fertilizer production.

The Quantum in Practice Act would introduce quantum molecular simulations and modeling to allow experts to study fertilizer chemical elements and reactions with accuracy.

From fertilizer production to materials manufacturing, quantum computing has the untapped potential to lower input costs for our farmers, improve energy storage, and produce more effective medications for patients, Rep. Feenstra said in a press release.

Im proud to introduce the Quantum in Practice Act to ensure that our main streets, farmers, and small businesses can realize the real benefits of quantum computing, not just in theory, but in practice, he added. Thanks to scientific ingenuity, there is boundless opportunity for our rural communities to harness the power of quantum computing to strengthen our agricultural sector, streamline fertilizer production, and enhance our way of life in the 4th District.

Rep. Feenstra originally introduced a version of this bill in 2022. According to the press release, quantum computing can model the nitrogen fixation process utilized by bacteria, which could be used to develop cheaper, next-generation synthetic fertilizers.

In addition to assisting the agriculture industry with streamlining fertilizer production, the members of Congress said potential scientific discoveries could also help produce safer medicines, energy storage, new metals, protective gear, and superconductors.

Original cosponsors of the legislation include: House Science, Space, and Technology Chairman Frank Lucas, R-Okla., and Ranking Member Zoe Lofgren, D-Calif., and Reps. Young Kim, R-Calif., Jake Ellzey, R-Texas, Rick Crawford, R-Ark., Byron Donalds, R-Fla., Nancy Mace, R-S.C., Brian Fitzpatrick, R-Pa., Rudy Yakym, R-Ind., Brandon Williams, R-N.Y., Tom Kean, R-N.J., Joseph Neguse, D-Colo., and Jeff Jackson, D-N.C.

Sens. Todd Young, R-Ind., and Raphael Warnock, D-Ga., have introduced companion legislation in the Senate.

Quantum simulations are able to model interactions at the sub-molecular level and create a cost-effective alternative to the expensive development of new fertilizers, medications, protective equipment, and more, said Sen. Young.

As we secure our competitive advantage in the 21st?century, we must support the cutting-edge research that will revolutionize Indianas agriculture and pharmaceutical industries, he added. The Quantum in Practice Act would help ensure that American researchers and industries can pursue practical applications to advance quantum technologies.

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House Bill Aims to Apply Quantum Tech to Agriculture - MeriTalk

The advent of the computer in the 20th century brought an explosion of innovation, productivity and economic development. But so-called classical computers are limited by their physical properties, and have seen a relative deceleration in technological progress in recent years. However, a new generation of computers leveraging phenomena from quantum physics promises exponentially greater power that, at least in certain areas, can enable a new era of transformative innovation. In this edition of From the Future, three Notre Dame researchers give their perspectives on the powers and applications of quantum computers, describe the cutting-edge research they are conducting and consider the future of quantum computing here on campus.

Zhiding Liang, PhD student, Department of Computer Science and Engineering

Zhiding Liang, PhD student in computer science and engineering, did his undergraduate studies in electrical engineering and is trained in the world of classical devices and circuits.

However, in pursuing graduate education, Liang decided to take a leap into quantum computing, a field he thinks has great potential.

Liang explained that the potential of quantum computing lies in the basic processing unit for these systems: the quantum bit, or the qubit. Classical binary computer transistors can only be in one of two states, zero or one, representing off or on switches for electrical signals. Leveraging quantum properties, qubits can represent zero or one, or any proportion of zero and one at the same time a phenomenon called superposition.

Superposition enables quantum computers to wield immense computational power since the amount of information a system can process grows exponentially with each additional qubit.

However, quantum computers have limitations. In terms of hardware, qubits are incredibly fragile devices. To maintain a state of superposition, qubits require particular environmental conditions (temperature, noise, etc.). If such conditions are not maintained, qubits could experience decoherence, or losing their power of superposition, and, in effect, devolving into classical binary bits.

Liang focuses on solving problems on the software side of things. His research looks at ways to optimize quantum computer architecture to improve the performance of algorithms. The difficulty of maintaining superposition and avoiding decoherence often limits the time in which quantum computation is possible. So, decreasing the latency of these systems (the time it takes to send data) is important for making quantum computers usable.

When he isnt programming quantum computers, Liang works on a different kind of programming: educational lectures about quantum computing for fellow students.

As an electrical engineer, Liang came to quantum computing without background knowledge in physics, which is necessary even for software-focused researchers like himself.

Liang said that his first semester as a PhD student involved extensive studies outside of class to get up to speed on fundamental physics concepts. This was frustrating due to the lack of resources online and even from other universities.

Liang was inspired to create the Quantum Computer System Lecture Series to make the transition to quantum computing easier for students like himself who do not have a physics background.

I think theres not enough open source resources online, Liang said. I hope to offer a platform to let students who are interested in quantum computing have a pathway to get in touch with this area.

The lecture series has featured 33 talks from quantum computing students and researchers from around the world. Topics range from introductions to basic quantum concepts to state-of-the art research outcomes.

Liang hopes that his lecture series will spark interest in quantum computing at Notre Dame. He recognizes that this field can be intimidating due to the high knowledge bar and uncertainty about when this technology will arrive. But he is optimistic about quantum computers and the opportunities for Notre Dame and its students.

I want to contribute to building a quantum computing community at Notre Dame, Liang said. Quantum computing is still a really young area. Theres lots of things you can do theres a lot of opportunities.

Mariya Vyushkova, Quantum Computing Research Specialist at the Notre Dame Center for Research Computing, Visiting Scientist at IBM Research

While the quantum community in South Bend is still in its infancy, members of the Notre Dame community are forming connections with some of the worlds premier hubs for quantum computing research.

Dr. Mariya Vyushkova is a quantum computing research specialist with the Notre Dame Center for Research Computing, but she is currently a visiting scientist at IBM Research in San Jose, California studying applications for quantum computing.

Vyushkovas research focuses on the possibilities of using quantum computers for simulations in spin chemistry, a field at the intersection of chemistry and physics that deals with magnetic and spin effects in chemical reactions.

Spin chemistry is closely connected to the development of quantum computers. One type of qubit (called the spin qubit) uses spin chemistry phenomena to create quantum properties like superposition and enable powerful computation.

However, spin chemistry simulations have other practical applications, like studying the manipulation of reactions in biological systems, solar energy technology or organic semiconductors. Overall, Vyushkova said many experts believe that chemistry is the field where quantum computing has the most promising applications and will make the most immediate impact.

In discussing future applications for quantum computing, Vyushkova made a point to clarify a common misconception that quantum computers will replace classical computers.

This is not the case. Even when quantum computers reach their full potential, they will only have an advantage over classical computers for certain applications. And at the moment, for reasons of unreliability and fragility discussed above, quantum computers are no match for classical computers.

However, Vyushkova said that we still need to be quantum ready: i.e., prepared for the day when quantum computers become reliable enough to be used for their unique advantages.

We cannot just sit here and wait for 10 years for the engineers to build an ideal quantum computer, Vyushkova said. We need to learn to use those devices right now.

Vyushkova compared the current state of quantum computers to that of classical computers in the mid-20th century. At that time, computers were huge, slow, noisy, expensive and generally impractical machines. However, early computer scientists were still able to develop techniques and applications so that when the technology became cheaper, faster and more viable, we as a society could leverage computers to the fullest.

Its possible quantum computers would provide a similar advantage [as classical computers have], Vyushkova said. They will never replace classical computers, but in certain fields they are capable of potentially solving problems which are exponentially harder, just unimaginable.

Vyushkova noted that countries like China and Russia are investing significant resources into quantum computing with the belief that this technology will give them a strategic advantage in the future. In this sense, the relatively lower investment into quantum computing in the United States could have serious consequences.

We are at the very beginning; this field is still in its infancy, Vyushkova said. If you miss this opportunity, then you wont be competitive in the future.

Laszlo Forro, Aurora and Thomas Marquez Professor of Physics of Complex Quantum Matter, Director of Stavropoulos Center for Complex Quantum Matter

Back on Notre Dames campus, a new research center aims to provide the physical hardware needed to facilitate the opportunities and applications discussed above.

Located on the fourth floor of the Nieuwland Hall of Science, the Stavropoulos Center for Complex Quantum Matter was established in 2019 as a home for research on new materials for next-generation technologies like quantum computing.

Dr. Laszlo Forro came to Notre Dame from Switzerland in July 2021 to serve as a professor of physics and director of the Stavropoulos Center, bringing a mindset of doing research that leads to practical applications at least eventually.

In this branch of condensed matter physics, the goal is always to do something useful, Forro said. But I believe that every serious research, sooner or later, will be applied. Its just a question of different timescales could be applied in two years, five years or 50.

Quantum computing is one key area of research for the Stavropoulos Center. According to Forro, an important unaddressed issue with quantum computers is longevity: being able to sustain quantum states (i.e., avoid decoherence) long enough to operate the computer and process information.

New quantum materials can help solve this issue. Researchers at the Stavropoulos Center are experimenting with different atomic structures and creating pure materials that can extend the lifetime of quantum states, and therefore improve the usability of quantum. computers.

As noted, quantum computers cant do everything better or faster than a classical computer. However, once quantum materials are improved, Forro sees a multitude of applications for quantum technology. Potential uses include cryptography in banking or data transferring, drug discovery and AI or machine learning research.

Forro also thinks that quantum computing could be a commercially available technology. He suggested that, in the future, we could have USB stick devices that plug into classical computers and enable quantum computation.

However, Forro said that these applications are far off, and there is significant uncertainty about timelines for viable quantum computers. For now, Forrs immediate goals are to expand his team, grow the Stavropoulos center and produce research.

We have hired high-level scientists, and I hope that, based on our performance, we can ask the school to give us a few more positions to extend our research profile and to be more productive, Forro said.

Forro believes that Notre Dame has his back in this effort to build the Stavropoulos Center into a world-class quantum research facility.

If it runs well, I have no doubt that the school will support us to extend the number of [project leaders], Forro said. I have a strategic plan and vision for the center, which is supported by the college of science Dean [Santiago Schnell] and also by the Provost [John McGreevy] today. This is a very nice feeling for me as a director that I will have the full support of the school.

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From the Future: Quantum Computing // The Observer - Observer Online

If you thought all generative AI could do was produce text and images, think again. It can do far more about way more serious and complex problems all it needs is a quantum boost. This new field of learning called quantum generative AI is a perfect marriage between the very buzzy area of generative AI and quantum computing and brings together the benefits of both.

In an exclusive chat with Prateek Jain, Lead Researcher and Architect of Quantum Computing at Fractal, we discuss the applications of quantum generative AI in critical sectors like healthcare, the associated challenges and how to get around them.

Fractal: Quantum generative AI (QGAI) is a subfield of quantum computing that focuses on developing algorithms and models that can generate new data with the help of quantum computers.

The main difference between QGAI and classical generative AI is the underlying computational platform. While classical generative AI relies on classical computers, QGAI is a novel approach which relies on quantum computers to perform operations on quantum bits (qubits) to generate new data. QGAI algorithms are designed to exploit the quantum mechanical properties of qubits to generate data with unique and potentially useful features.

Some examples of QGAI applications include:

Fractal: Quantum generative AI can be used to optimize drug and material properties by generating new compounds with specific characteristics that are required for various applications. Here are some ways in which QGAI can be used to optimize drug and material properties:

Drug Optimization: QGAI can be used to optimize the properties of existing drugs to enhance their efficacy and reduce side effects. By simulating the behaviour of molecules on quantum computers, QGAI algorithms can predict the binding affinity of a molecule to a target protein, which is a key factor in determining drug efficacy. QGAI can also be used to optimize drug properties, such as solubility and bioavailability, to improve their pharmacokinetics.

You can find some of our research at Fractal.ai here https://arxiv.org/abs/2212.07826

Material Optimization: QGAI can be used to optimize the properties of materials for specific applications, such as energy storage or catalysis. By simulating the behaviour of atoms and molecules on quantum computers, QGAI algorithms can predict the properties of materials, such as their conductivity or reactivity, malleability, radioactivity etc.

Chemical Reaction Optimization: QGAI can also be used to optimize chemical reactions by predicting the optimal conditions and reactants required for a specific reaction. This can lead to more efficient and sustainable chemical processes, such as the production of pharmaceuticals or materials.

Fractal: Quantum generative AI has the potential to revolutionize drug and material design by helping researchers to generate new and innovative compounds that could not be discovered using traditional methods. QGAI algorithms use quantum computers to simulate the behaviour of molecules and atoms and help researchers to predict their properties and generate new structures that meet specific criteria.

Here are some examples of how QGAI can be applied to drug and material design:

Drug Discovery: QGAI can be used to generate new compounds that are more effective at targeting specific diseases than existing drugs.

Material Design: QGAI can be used to design new materials with unique properties, such as superconductors or catalysts. By simulating the behaviour of atoms and molecules on quantum computers, QGAI algorithms can predict the properties of new materials, such as their conductivity or reactivity. This can help researchers design materials with specific properties that are required for various applications, such as energy storage or catalysis.

The potential benefits of using QGAI for drug and material design are significant. QGAI can accelerate the drug discovery and material design process by reducing the time and cost required to synthesize and test new compounds. QGAI can also enable researchers to discover compounds that would be difficult or impossible to discover using classical methods, leading to new treatments for diseases and innovative materials for a variety of applications. Additionally, QGAI can enable researchers to design compounds with specific properties that are required for various applications and pave the way for more effective drugs and materials.

There have been several research projects that have already applied quantum generative AI (QGAI) to drug and material design.

For instance, in 2019, researchers from IBM used QGAI to design a new molecule that could potentially be used to create more efficient potential drug molecules. The designed molecule was then synthesized and tested and was found to have high drug-like properties.

These examples demonstrate the potential of QGAI to accelerate the drug and material design process by generating new compounds that have desirable properties. While these projects are still in the early stages of development, they provide a glimpse into the exciting possibilities of QGAI in the field of drug and material design.

Fractal: Even though the emerging fields of quantum machine learning & newer quantum generative modelling are in their infancy, fast-moving research will push these areas to the fore. For starters, organizations can ensure that their quantum generative AI models are accurate and reliable by following some established approaches:

Data quality control: High-quality data is crucial for models to produce accurate results, especially in the healthcare sector. Organizations can ensure data quality by validating and cleaning up the data before using it to train QGAI models. They can also use statistical methods to identify and remove any outliers or irrelevant data points.

Model validation and testing: Organizations should validate their models by testing them on independent data sets. This can help identify any errors or biases in the models and provide insights into how they can be improved.

Explainability and transparency: Organizations should ensure that their models are transparent and explainable, which means that the models should be able to provide a clear explanation of their decision-making process. This can help identify any potential biases ahead and reduce them before they pop up in the predictions.

Regular updates and maintenance: Models should be regularly updated and maintained to ensure their accuracy and reliability.

In addition to these measures, organizations can also take the following steps to mitigate any potential errors or biases in their models:

Diversity and inclusivity: Organizations should ensure that their QGAI models are trained on diverse and inclusive data sets to avoid any biases that may arise from underrepresented groups.

Robustness testing: Organizations can test the robustness of their models by intentionally introducing errors or biases into the data and observing how the models respond.

Ethical considerations: Organizations should consider the ethical implications of their QGAI models and ensure that they do not cause harm or discrimination to individuals or groups.

Fractal: Using quantum generative AI brings along several challenges:

Scalability: As the size of the molecule or material being designed increases, so does the complexity of the calculations required. This can limit the scalability of QGAI for designing larger and more complex materials and drugs especially when the quantum processors are very small in the NISQ era.

Noise and decoherence: The inherent noise and decoherence in quantum computing is a major problem & can affect the accuracy of the models by a wide margin. This can lead to several errors in the predictions made.

Data quality and quantity: QGAI models require high-quality and diverse data to accurately predict properties and generate new compounds. However, acquiring such data can be challenging and costly, especially for rare or newly discovered compounds.

Interpreting results: QGAI models may generate novel compounds that exhibit desired properties, but it can be challenging to interpret why these compounds have these properties, making it difficult to optimize their performance further.

To address these challenges, researchers are exploring various approaches, such as:

Developing more efficient algorithms: Researchers are developing new algorithms and techniques to improve the efficiency of QGAI calculations and reduce the computing resources required for large-scale designs.

Quantum error correction: Researchers are developing quantum error correction techniques to mitigate the impact of noise and decoherence in quantum computing.

Integration with classical computing: Hybrid quantum-classical computing approaches are being developed to address the current limitations by utilizing the strengths of classical computing to supplement quantum computing.

Enhanced data collection and processing: Researchers are exploring ways to improve data quality and quantity by leveraging advances in data collection and processing technologies, such as machine learning and high-throughput screening techniques.

By addressing these challenges, researchers can further advance the use of QGAI for drug and material design and unlock its full potential for revolutionizing these industries.

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Reaping the Synergies Between Quantum Computing and ... - Analytics India Magazine