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With advanced digitalization technologies possibly standing poised to catalyze a seismic paradigm shift, investors should consider quantum computing stocks for millionaires (or at least those who aspire to be millionaires).As the AWS website under Amazon (NASDAQ:AMZN) states, quantum computing is a multidisciplinary field comprising aspects of computer science, physics, and mathematics that utilizes quantum mechanics to solve complex problems faster than on classical computers. Stated differently, the computer as we know is about to get a makeover, bolstering relevance for top quantum computing stocks.

According to Precedence Research, the global quantum computing market reached a valuation of $10.13 billion in 2022. However, experts project that by 2030, the sector could command a valuation of $125 billion. For speculators, its well worth considering millionaire quantum computing stock picks.To be sure, because of the novel market, investors should be prepared for wild unpredictability. Nevertheless, investing in quantum computing stocks could lead to life-changing returns.

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Initially, the inclusion of IBM (NYSE:IBM) as one of the quantum computing stocks for millionaires might not make sense. After all, this segment is supposed to be about high-risk, high-reward speculation. However, if youre interested in taking the more surefire, steady approach to being a millionaire, Big Blue makes plenty of sense.

Fundamentally, IBM belongs in the discussion for top quantum computing stocks thanks to its myriad innovations in the sector. For example, the company produced Qiskit Runtime, which is IBMs quantum computing service and programming model.

Financially, IBM carries a decent profile: nothing too positively remarkable but nothing too horrible either. Notably, IBM trades at a forward multiple of 13.44. As a discount to projected earnings, Big Blue ranks better than 79.51% of the competition. Also, its dividend yield comes out to 5.46%, which is quite generous.

Finally, Wall Street analysts peg IBM as a moderate buy. Their average price target lands at $147.38, implying nearly 16% upside potential.

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Based in Berkeley, California, Rigetti Computing (NASDAQ:RGTI) develops quantum integrated circuits used for quantum computers. Per its website, Rigetti specializes in fusing artificial intelligence and machine learning, thereby allowing the company to address the worlds most important and pressing problems. A wildly risky investment, RGTI only gained a bit more than half a percent in the year so far.

Most of that stems from shares popping up nearly 45% on the May 22 session, rising in sympathy with quantum computing rival D-Wave Quantum (NYSE:QBTS). If you follow the sector closely, youll know that QBTS skyrocketed almost 111% on Monday. However, plenty of traders dont want to overexpose themselves to extreme strength, making RGTI a potentially intriguing alternative for quantum computing stocks for millionaires.

To be sure, its financially a high-risk proposition. According to Zacks Equity Research, Rigetti came out with a quarterly loss of 19 cents for its first-quarter earnings report, missing the consensus estimate of a loss of 16 cents. On the positive side, Rigetti carries a relatively strong cash-to-debt ratio of 3.27.Still, despite some flaws, analysts peg RGTI as a consensus moderate buy. Their average price target clocks in at $1.25, implying over 70% upside potential.

Source: Bartlomiej K. Wroblewski / Shutterstock.com

For those that want to take their quantum computing stocks for millionaires to the extreme, Quantum Computing (NASDAQ:QUBT) may be what youre looking for. A full-stack quantum software and hardware company, Quantum seeks to accelerate the value of quantum computing for real-world business solutions, per its website. To help bring about this goal, the company bought out QPhoton, which specializes in quantum photonic systems (QPS).

Although fundamentally exciting, prospective investors must recognize that QUBT represents a high-risk, high-reward venture. In the trailing one-year period, for example, QUBT stumbled by more than 17%. Over the past five years, shares hemorrhaged 79% of equity value.

Financially, circumstances arent exactly confidence-building. In the first quarter of 2023, Quantum posted revenue of only $120,000. On the bottom line, it incurred a net loss of $8.51 million. Overall, the company features only middling fiscal stability, making it one of the riskier quantum computing stocks to buy.That said, Ascendiant analyst Edward Woo pegs QUBT as abuy. The expert forecasts a price target of $9.25, implying almost 612% upside potential.

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.

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3 Quantum Computing Stocks to Make You the Millionaire Next Door - InvestorPlace

Call of Duty specs will be high

IBM wants to build a 100,000-qubit quantum computing machine within the next 10 years, according to MIT Technology Review.

Biggish Blue has managed to build a 433-quantum bit, or qubits machine so far, making it the world leader. IBM announced the move at the G7 summit in Hiroshima, Japan. The company will partner with the University of Tokyo and the University of Chicago in a 100-million-dollar initiative to push quantum computing into full-scale operation, where the technology could tackle pressing problems that no standard supercomputer can solve.

The 100,000 qubits machine will work alongside supercomputers to achieve breakthroughs in drug discovery, fertiliser production, battery performance, and other applications. It is unclear were potentially dead or alive cats will be involved.

IBM's VP of quantum, Jay Gambetta, told MIT Technology Review that IBM has already done proof-of-principle experiments (PDF) showing that integrated circuits based on "complementary metal oxide semiconductor" (CMOS) technology can be installed next to the cold qubits to control them with just tens of milliwatts.

However, beyond that, he admits, the technology required for quantum-centric supercomputing does not yet exist: that is why academic research is a vital part of the project.

The qubits will exist on a modular chip that is only beginning to take shape in IBM labs.

Modularity, essential when it will be impossible to put enough qubits on a single chip, requires interconnects that transfer quantum information between modules.

IBM's "Kookaburra," a 1,386-qubit multichip processor with a quantum communication link, is under development and slated for release in 2025. Gambetta says that boffins at Tokyo and Chicago have already made significant strides in components and communication innovations that could be vital parts of the final product.

Gambetta thinks there will likely be many more industry-academic partnerships over the next decade.

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IBM wants to build a 100,000-qubit quantum computer in 10 years - Fudzilla

Muhammad Usman, CSIRO

Artificial intelligence algorithms are quickly becoming a part of everyday life. Many systems that require strong security are either already underpinned by machine learning or soon will be. These systems include facial recognition, banking, military targeting applications, and robots and autonomous vehicles, to name a few.

This raises an important question: how secure are these machine learning algorithms against malicious attacks?

In an article published in Nature Machine Intelligence, my colleagues at the University of Melbourne and I discuss a potential solution to the vulnerability of machine learning models.

We propose that the integration of quantum computing in these models could yield new algorithms with strong resilience against adversarial attacks.

Machine learning algorithms can be remarkably accurate and efficient for many tasks. They are particularly useful for classifying and identifying image features. However, theyre also highly vulnerable to data manipulation attacks, which can pose serious security risks.

Data manipulation attacks which involve the very subtle manipulation of image data can be launched in several ways. An attack may be launched by mixing corrupt data into a training dataset used to train an algorithm, leading it to learn things it shouldnt.

Manipulated data can also be injected during the testing phase (after training is complete), in cases where the AI system continues to train the underlying algorithms while in use.

People can even carry out such attacks from the physical world. Someone could put a sticker on a stop sign to fool a self-driving cars AI into identifying it as a speed-limit sign. Or, on the front lines, troops might wear uniforms that can fool AI-based drones into identifying them as landscape features.

Either way, the consequences of data manipulation attacks can be severe. For example, if a self-driving car uses a machine learning algorithm that has been compromised, it may incorrectly predict there are no humans on the road when there are.

In our article, we describe how integrating quantum computing with machine learning could give rise to secure algorithms called quantum machine learning models.

These algorithms are carefully designed to exploit special quantum properties that would allow them to find specific patterns in image data that arent easily manipulated. The result would be resilient algorithms that are safe against even powerful attacks. They also wouldnt require the expensive adversarial training currently used to teach algorithms how to resist such attacks.

Beyond this, quantum machine learning could allow for faster algorithmic training and more accuracy in learning features.

Todays classical computers work by storing and processing information as bits, or binary digits, the smallest unit of data a computer can process. In classical computers, which follow the laws of classical physics, bits are represented as binary numbers specifically 0s and 1s.

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Quantum computing, on the other hand, follows principles used in quantum physics. Information in quantum computers is stored and processed as qubits (quantum bits) which can exist as 0, 1, or a combination of both at once. A quantum system that exists in multiple states at once is said to be in a superposition state. Quantum computers can be used to design clever algorithms that exploit this property.

However, while there are significant potential benefits in using quantum computing to secure machine learning models, it could also be a double-edged sword.

On one hand, quantum machine learning models will provide critical security for many sensitive applications. On the other, quantum computers could be used to generate powerful adversarial attacks, capable of easily deceiving even state-of-the-art conventional machine learning models.

Moving forward, well need to seriously consider the best ways to protect our systems; an adversary with access to early quantum computers would pose a significant security threat.

The current evidence suggests were still some years away from quantum machine learning becoming a reality, due to limitations in the current generation of quantum processors.

Todays quantum computers are relatively small (with fewer than 500 qubits) and their error rates are high. Errors may arise for several reasons, including imperfect fabrication of qubits, errors in the control circuitry, or loss of information (called quantum decoherence) through interaction with the environment.

Still, weve seen enormous progress in quantum hardware and software over the past few years. According to recent quantum hardware roadmaps, its anticipated quantum devices made in coming years will have hundreds to thousands of qubits.

These devices should be able to run powerful quantum machine learning models to help protect a large range of industries that rely on machine learning and AI tools.

Worldwide, governments and private sectors alike are increasing their investment in quantum technologies.

This month the Australian government launched the National Quantum Strategy, aimed at growing the nations quantum industry and commercialising quantum technologies. According to the CSIRO, Australias quantum industry could be worth about A$2.2 billion by 2030.

Muhammad Usman, Principal Research Scientist and Team Leader, CSIRO

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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From self-driving cars to the military: quantum computing can help ... - Cosmos

Quantum algorithms are becoming important because of their accelerated processing speed over classical algorithms for solving complex problems1,2,3,4,5. However, using quantum algorithms to solve practical problems is difficult because quantum states are very susceptible to noise, which can cause critical errors in the execution of quantum algorithms. In other words, quantum errors caused by noise pose a major obstacle to the realization of quantum algorithms.

The quantum circuit model is a well-known model for quantum computation. In this model, quantum algorithms are represented by quantum circuits composed of qubits and gates. Since noise arises from the evolution of quantum states, gate operations are the major cause of noise. Therefore, quantum circuits should be designed with a minimal number of gates, especially in the noisy intermediate-scale quantum (NISQ) arena6,7.

Within the realm of quantum logic synthesis, quantum circuits are broken down into gates derived from a universal gate library. The basic gate library consists of CNOT and single-qubit gates8,9. Since CNOT gates are considered the main generators of quantum errors and have a longer execution time compared to single-qubit gates10, CNOT gates are expected to dominate the cost of quantum circuits when using the basic gate library.

When considering the cost of a quantum circuit, connectivity between qubits should also be taken into account. This is because physical limitations in quantum hardware may enforce quantum circuits to adopt the nearest-neighbor (NN) architecture10,11. The NN architecture means that a qubit in the circuit only interacts with adjacent qubits.

The quantum Fourier transform (QFT) is an essential tool for many quantum algorithms, such as quantum addition12, quantum phase estimation (QPE)13, quantum amplitude estimation (QAE)3, the algorithm for solving linear systems of equations4, and Shors factoring algorithm1, to name a few. Therefore, the cost optimization of QFT would result in the efficiency improvement of these quantum algorithms.

There have been studies aimed at reducing circuit costs of QFT8,14,15,16,17,18,19,20,21,22. Among them are studies related to the number of CNOT gates in QFT, including the following:

When constructing an (n)-qubit QFT circuit using the basic gate library, (n(n-1)) CNOT gates are required, provided that qubit reordering is allowed8. Qubit reordering implies that the sequence of qubits can be altered before and after the execution of the circuit.

In Ref.14, the authors incorporated (n(n-1)/2) extra SWAP gates to develop an (n)-qubit linear nearest-neighbor (LNN) QFT circuit, which accommodates qubit reordering.

To synthesize a single SWAP gate using the basic gate library, three CNOT gates are required8.

Consequently, the total number of CNOT gates required for the (n)-qubit LNN QFT circuit presented in Ref.14 is (5n(n-1)/2).

By employing SWAP gates in the construction of LNN QFT circuits, the primary term representing the quantity of CNOT gates increases by a factor of 2.5.

Previous research efforts, as documented in case studies, have investigated techniques to minimize the amount of SWAP gates required in the LNN architecture when assembling (n)-qubit LNN QFT circuits15,16,17,18. These studies aimed to optimize the circuit design and improve overall efficiency.

In this paper, we propose a new n-qubit LNN QFT circuit design that directly utilizes CNOT gates, unlike previous studies14,15,16,17,18 that utilized SWAP gates. Our approach offers a significant advantage by synthesizing a more compact QFT circuit using CNOT gates instead of SWAP gates, as the implementation of each SWAP gate requires three CNOT gates. Upon qubit reordering, our (n)-qubit LNN QFT circuit requires ({n}^{2}+n-4) CNOT gates, which are 40% of those in Ref.14 asymptotically. Furthermore, we demonstrate that our circuit design significantly reduces the number of CNOT gates compared to the best-known results for 5- to 10-qubit LNN QFT circuits17,18.

In the following analysis, we compare our QFT circuit with the conventional QFT circuit8 when used as inputs for the Qiskit transpiler23, which is required for implementation on IBM quantum computers that necessitate NN architecture10. Our findings confirm that using our QFT circuit as input requires fewer CNOT gates in comparison to the conventional QFT circuits. This evidence indicates that our QFT circuit design could serve as a foundation for synthesizing QFT circuits that are compatible with NN architecture, potentially leading to more efficient implementations.

Furthermore, we present experimental results from implementing the QPE using 3-qubit QFTs on actual quantum hardware, specifically the IBM_Nairobi10 and Rigetti Aspen-1111 systems. We also illustrate the decomposition of controlled-({R}_{y}) gates that share a target qubit using our proposed method. This particular circuit is often found in QAE, which is anticipated to supplant classical Monte Carlo integration methods24,25. By providing these results, we aim to highlight the practicality and effectiveness of our approach in real-world quantum computing applications.

The remainder of this paper is organized as follows: in the Background section, we provide a brief overview of quantum circuits, QFT, QPE, and QAE. The proposed approach section outlines our method for constructing LNN QFT circuits. In the resultsand discussion section, we present the outcomes of transpilation on IBM quantum computers, display the experimental results of QPE executions on quantum hardware, and illustrate how to convert a circuit of controlled-({R}_{y}) gates sharing the target qubit into an LNN circuit using our proposed method. We also address the limitations of our study and suggest potential future research directions. Finally, we conclude the paper with a summary of our findings and their implications for the field of quantum computing.

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Reducing CNOT count in quantum Fourier transform for the linear ... - Nature.com

A recently signed pact with the United States is expected to deliver benefits beyond clean energy or new mines by also boosting national security.

Company bosses and policymakers are still coming to grips with what the deal signed last weekend will mean.

But it's clear the Australia-US Climate, Critical Minerals, and Clean Energy Transformation Compact will pull the two industrial bases together against China's might.

"Nothing but goodness can come from that agreement," cyber security expert Tony Burnside told AAP.

"It makes us more secure."

The pact signalled an intent to deepen collaboration on the materials and know-how that are vital to defence supply chains and clean energy.

Mr Burnside, vice-president at Netskope, said the US was likely to experience certain attacks before anyone else, and information sharing would be crucial.

"In our industry, we collaborate with vendors for the same reason - they may see something, an attack on a computer before we do, or vice versa," hesaid.

Netskope is a global leader in cloud, data and network security and in managing the safe use of artificial intelligence and robotics.

"Many of us have been in the space for years," Mr Burnside said.

But with the spread of chatbot ChatGPT and Google's conversational version called Bard, change is accelerating and Australia must get on board.

"We've got to really keep up with the bad guys out there and, from a competitive standpoint, the other countries that are leveraging it," Mr Burnside said.

He said quantum computing will be key. It's so powerful that it will outmatch existing encryption standards now protecting systems and data.

"We can't rule out some states using quantum computing for cyber warfare in the upcoming years," he said.

"And it won't be too long before hackers also get access to quantum computing."

Quantum-resistant encryption standards are in the works, and the US government has already asked all of its agencies and their suppliers to use quantum-resistant encryption by 2035.

Story continues

President Joe Biden will ask the US Congress to add Australia as a "domestic source" under the US Defense Production Act.

The designation means Australia will be the second country after Canada to be considered for the special status.

It will enable US industry to increase production and investment in Australia, including critical minerals and defence technologies, and allow local firmsto apply for US funding.

"It's important to note, this would not give the US government any authority to direct Australian industry," a spokesperson for the Department of Prime Minister and Cabinet told AAP.

Minerals Council chief executive Tania Constable also wants Australia to makenational security part of the critical minerals boom.

Competing with trillion-dollar tax incentives and funding in North America and Europe, Australia's $15 billion National Reconstruction Fund includes $1 billion for critical technologies and $1 billion for advanced manufacturing.

Australia's new critical technologies list is already out and has the stamp of approval from Netskope and other industry leaders for backing quantum computing and AI.

"Bringing in defence procurement is really important and the links that are made with other countries like the United States on those," Ms Constable told AAP.

"We need to think more broadly than just direct funding," she said.

"But there will be more federal budgets to come and hopefully the critical minerals strategy (due out soon) will set out a pathway and framework for those additional incentives."

Meanwhile, the latest critical technologies list has the stamp of approval from Netskope and other industry leaders in space and defence, advanced manufacturing and energy.

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Australia-US compact to help keep up with the bad guys - Yahoo News Australia