In 1981, physicist Richard Feynman first theorized the creation of quantum computers that harnessed the principles of quantum physics to process calculations that would take standard computers millennia or longer to compute. Over the next four decades, however, research failed to advance significantly enough for the machines to have much impact on society.

But breakthroughs in 2023 signaled that quantum computers have embarked on a new era, one that may unleash a technological revolution full of possibilitiessome good and some bad. On the positive side, quantum computers could lead to the development of new drugs to combat cancer. On the negative side, however, they can break the encryption we use multiple times per day for everything from sending texts to financial transactions.

But this isnt the first quantum race in history that pitted the U.S. against its adversariesand the past provides a guide for how the U.S. can win the coming computing revolution. In the 1940s, a quantum race produced the creation of nuclear weapons and unleashed a technology explosion. Crucially, the U.S. won the competition to harness the new technology. Not only did American scientists create the first nuclear weapons, but advancements in lasers and in chips for computers made the U.S. the home for global innovation.

That only happened, however, because policymakers supplied the funding and support necessary to ensure superiority. In 2024, by contrast, a key quantum funding bill has stalled while allies and adversaries are sinking billions into quantum research and development. Without action, history shows that the U.S. risks falling behind especially in leadership for the revolutionary power of quantum technologies.

Quantum physics developed in Europe in the 1920s and 1930s. As World War II erupted in the 1930s and 1940s, German, Hungarian, and Italian physicists escaped to the U.S. Many of them joined J. Robert Oppenheimer and his American colleagues in the Manhattan Projectwhich birthed the atomic bomb and simultaneously elevated the U.S. as the home for quantum science.

In the ensuing decades, Feynman and other scientists who cut their teeth on the Manhattan Project inspired profound innovation from quantum physics that became woven into the fabric of American life. The first quantum revolution created nuclear weapons and energy, global positioning systems, lasers, magnetic resonance imaging, and the chips that would power the rise of the personal computer.

Read More: Quantum Computers Could Solve Countless ProblemsAnd Create a Lot of New Ones

Although many countries like the Soviet Union built nuclear weapons, none rivaled the U.S. in pioneering innovation. The Soviet launch of Sputnik in 1957 and the space race produced an explosion of federal funding for science and education that was at the root of American success. Further, the Department of Defense provided crucial sponsorship for visionary, but risky, research that developed the internet, stealth capabilities, and voice assistants like Siri. This combination propelled the U.S. to unparalleled innovation heights in the decades after World War II.

The technologies born from the first quantum revolution were at the core of American national defense, and also reshaped civilian life in the U.S., most especially through the development of personal computers and the Information Revolution.

But even as personal computers were beginning to revolutionize American life in 1981, Feynman insisted in a pivotal lecture that something more was possible. He argued that a quantum computer with processing power magnitudes greater than even the highest performing computer then in existence offered the only way to unlock the true knowledge of the world. Feynman admitted, however, that building such a machine required staggering complexity.

The ensuing four decades have proved him correct on the obstacles involved. Designing a quantum computer required tremendous advances in theory as well as materials and components. Since the 1980s, progress has crept along, and many joked that quantum computers would always be 10 to 20 years away.

In 1994, mathematician Peter Shor discovered an algorithm that created a method for a quantum computer to calculate the large prime numbers used in encryption. Despite this breakthrough, the pace of developments since Shors discovery has remained glacial. Persistent funding from the National Security Agency and the Department of Defense especially the former has sustained innovation, but the results have been uneven, because scientists have been unable to build a quantum computer that wasnt plagued by errors.

In the past 10 years, private technology companies such as IBM, Google, and Microsoft have made significant investments in quantum computing, which have pushed the field to new heights of maturity and accelerated a global race for quantum dominance one with major national security and cybersecurity implications.

Even so, todays quantum computers still have yet to outperform standard computers due to regular errors caused by radiation, heat, or improper materials. These errors make quantum computers useless for things like, for example, designing new drugs, because scientists cant replicate an experiment accurately. But all of that is changing quickly.

Advances by IBM and a Harvard team in 2023 demonstrated that error correction is on the horizon and the era of quantum utility has arrived. In July 2023, IBM announced peer reviewed evidence from experiments that indicated the company had made strides in mitigating the errors that have long plagued quantum computing. A few months later in December, a Harvard team and the company QuEra published encouraging results from experiments that showed they too had developed a quantum process with enhanced error-correction.

Read More: How the AI Revolution Will Reshape the World

But its not only American companies and universities trying to figure out how to mitigate the errors that have limited the possibilities of quantum computers. Over the last 15 years, Chinese physicists have undertaken an ambitious program aimed at making their country the world leader in quantum technologies. One estimate pegs China which has invested over $15 billion in the project as a leader or near equal to the U.S. in this new realm of science. In 2023, results from experiments suggested that Chinese physicists were notching impressive achievements that may enable them to construct a quantum computer that could outpace those developed in the U.S.

The consequences of Chinese superiority in this realm would be seismic. The U.S.s foremost adversary would then be able to crack the encryption Americans use every day for secure internet traffic and messaging, and which the U.S. government and its allies use to protect secret communications. One organization projects that the world has a mere six years before this capacity exists. Other estimates insist that date is as far as 10 years away. But it is coming fast.

That means the U.S. has to get out ahead of this impending technology to forestall disastrous consequences in every realm of American life. In May 2022 the White House announced plans to prepare the nation for post-quantum encryption alongside efforts being undertaken by private companies like Apple and Google. But Congress failed to renew a landmark federal funding bill for quantum research and development in 2023. Meanwhile, China and European countries are not flinching at devoting billions to quantum.

Quantum computing breakthroughs in 2023 herald a bright future that will transform life and economics. Technology sits on the cusp of fulfilling Feynmans vision and understanding the world and universe unlike ever before. An error-correcting quantum computer would launch the second quantum revolution, and a race is on to preserve the U.S.s leadership in science for one of the 21st centurys most prized technologies. To win that race, the federal government needs to make a concerted push to sustain American preeminence in quantum computing and other quantum technologies like sensing. Thats how the U.S. won the first quantum revolution and the stakes are too high not to learn from this past triumph.

The opinions are those of the author and do not necessarily represent the opinions of LLNL, LLNS, DOE, NNSA, or the U.S. government.

Brandon Kirk Williams is a senior fellow at the Center for Global Security Research at Lawrence Livermore National Laboratory.

Made by History takes readers beyond the headlines with articles written and edited by professional historians. Learn more about Made by History at TIME here. Opinions expressed do not necessarily reflect the views of TIME editors.

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History Shows How to Win the Quantum Computing Race - TIME

Very soon, IonQ stock could take a post-earnings quantum leap forward

Source: Amin Van / Shutterstock.com

Admit it. You want to find the next Nvidia (NASDAQ:NVDA) stock before it takes a rocket ride. We all do! Right now, theres a needle-in-a-haystack opportunity withIonQ(NYSE:IONQ) stock as IonQ gears up to release its quarterly results.

Just to recap, IonQopenedthe first dedicated quantum-computing manufacturing facility in the U.S. This company is a quantum-computing innovator, but it remains unrecognized among investors. Thats actually a good thing, though as IonQ could easily be the next Nvidia and should be top-of-mind on Wall Street soon enough.

Why did Nvidia stock soar in 2023 and 2024? This happened because the demand for artificial intelligence processors exploded, and Nvidia specializes in AI-enabled processors.

Similarly, IonQ specializes in quantum-computing technology, so IonQ stock offers a pure play in this emerging field. Granted, you might not be aware of the various use cases for quantum computing. For one thing, it can help to provide the vast computing power thats required for AI applications.

There are other use cases, as well. For example, IonQ is teaming up with German-based science research center Deutsches Elektronen-Synchrotron. Together, they will advance quantum-computing technology in the field of flight-gate optimization.

Furthermore, IonQ is collaborating with Oak Ridge National Laboratory (ORNL) to explore how quantum technology can be used to modernize the power grid. This crucial research project is funded by none other than the U.S. Department of Energy.

As you can see, IonQ is a highly active specialist in an exciting technology field. Is IonQ able to generate substantial revenue, though? The answer, as the data will show, is definitely yes.

Heres the evidence. In 2023s fourth quarter, IonQ generated $6.106 million in revenue. Thats up 60% year over year compared to the $3.807 million in revenue that IonQ reported in the year-earlier quarter. Plus, this result beat Wall Streets consensus call for $5.8 million in revenue.

Due to the companys expenses, IonQ recorded a Q4-2023 net earnings loss of 20 cents per share in 2023s fourth quarter. Interestingly, the analysts consensus estimate calls for IonQ to generate $6.6 million in first-quarter 2024 revenue.

IonQs guidance predicts that the company will generate Q1-2024 revenue of $6.5 million to $7.5 million. The midpoint of that guidance range ($7 million) is higher than what Wall Street expects ($6.6 million). Is it possible that the company knows something that the analysts dont?

Additionally, the analysts consensus estimate calls for IonQ to lose 25 cents per share in Q1 of 2024. Thats a low bar to clear, since IonQ only lost 20 cents per share in 2023s third quarter and 14 cents per share in the year-earlier quarter.

So, mark your calendar for May 8. Thats when IonQ will publish its first-quarter 2024 results and, potentially, deliver not-as-bad-as expected results i.e., a positive surprise.

As weve discovered, IonQ is discovering new and important use cases for quantum-computing technology. Hence, for a pure play on the quantum-computing revolution, forward-thinking investors should take a share position in IonQ.

Moreover, the time to take action is now. IonQ has a prime opportunity to beat the Streets muted expectations on May 8. With that in mind, I encourage you to get a head start in May and buy IonQ stock today.

On the date of publication, David Moadeldid 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.

David Moadel has provided compelling content and crossed the occasional line on behalf of Motley Fool, Crush the Street, Market Realist, TalkMarkets, TipRanks, Benzinga, and (of course) InvestorPlace.com. He also serves as the chief analyst and market researcher for Portfolio Wealth Global and hosts the popular financial YouTube channel Looking at the Markets.

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Seize the Quantum Opportunity: Why You Should Buy IONQ Stock Before May 8 - InvestorPlace

This post was contributed by Martin Schuetz, Ruben Andrist, Grant Salton, and Helmut Katzgraber from the Amazon Quantum Solutions Lab, and Pierre Minssen, Romina Yalovetzky, Shouvanik Chakrabarti, Dylan Herman, Niraj Kumar, Ruslan Shaydulin, Yue Sun, and Marco Pistoia from JPMorgan Chase

Many companies face combinatorial optimization problems and across both science and industry there are prominent examples in areas like transportation and logistics, telecommunications, manufacturing, and finance.Analog neutral-atom quantum machines can provide a novel platform to design and implement quantum optimization algorithms, with scientists in both industry and academia searching for the most promising types of problems for which an early quantum advantage could be demonstrated.

Over the past year, the Amazon Quantum Solutions Lab (QSL) has worked together with the Global Technology Applied Research team at JPMorgan Chase to conduct a systematic study of the hardness of certain optimization problems, inspired by real-world use cases in finance.

In this post, well describe our project and summarize the key results. Motivated by recent experiments reporting a potential super-linear quantum speedup [2], we studied the problem native to Rydberg atom arrays (the maximum independent set problem on unit-disk graphs). We identified a class of problem instances with controllable hardness and minimal overhead for neutral atom quantum hardware.

We think this work sets the stage for potentially impactful future experiments towards quantum advantage. For further technical details, feel free to check out our original work published in Physical Review Research [1].

Given its potentially far-reaching impact, the potential demonstration of quantum speedups for practically relevant, computationally hard problems has emerged as one of the greatest milestones in quantum information science. Over the last few years,Rydberg atom arrays have established themselves among the leading contenders for the demonstration of such quantum speedups; see this blog post for more details. In particular, in Ref. [2] a potential super-linear quantum speedup over classical simulated annealing has been reported for the maximum independent set problem (MIS) on unit-disk graphs (MIS-UD), based on variational quantum algorithms run on Rydberg atom arrays with up to 289 qubits arranged in two spatial dimensions.

This work focused on benchmarking quantum variational algorithms against a specific implementation of simulated annealing (representing a classical analogue of the adiabatic algorithm), yet it left open the question of benchmarking against other state-of-the-art classical solvers. In our work, we study the MIS-UD problem in detail (see Figure 1 for a schematic illustration), with a broader range of classical solvers beyond the scope of the original paper. Our main goal is to empirically assess the hardness of the MIS-UD problem, to help zero in on problem instances and system sizes where quantum algorithms could ultimately be useful, and thus identify the most promising directions for future experiments with Rydberg atoms.

Figure 1. Schematic illustration of the problem. (a) We consider unit-disk graphs with nodes arranged on a two-dimensional square lattice with side length L and ~80% of all lattice sites filled, and edges connecting all pairs of nodes within a unit distance (illustrated by the circle). (b) Our goal is to solve the MIS problem on this family of Union-Jack-like instances (as depicted here with nodes colored in red in the right panel) and assess the hardness thereof using both exact and heuristic algorithms.

The MIS problem is an important combinatorial optimization problem with practical applications in network design, vehicle routing, and finance, among others, and is closely related to the maximum clique, minimum vertex cover, and set packing problems [3]. Here we provide two complementary problem formulations, one based on an integer linear program and one based on an Ising-type Hamiltonian compatible with Rydberg atom arrays. We discuss important figures of merit to assess problem hardness.

Consider a graph G = (V, E) with vertex set V and edge set E, with an independent set defined as a subset of vertices that are not connected with each other. The MIS problem is then the task to find the largest independent set. Introducing a binary variable xi for every node (with xi = 1 if node i is in the independent set, and xi = 0 otherwise), the MIS problem can formally be expressed as a compact integer linear program of the form:

with the objective to maximize the marked vertices while adhering to the independence constraint.

Alternatively, a problem formulation commonly used in physics literature expresses this program in terms of a Hamiltonian thatincludes a soft penalty to non-independent configurations (that is when two vertices in the set are connected by an edge) to model the hard independence constraint. This Hamiltonian is given by

with a negative sign in front of the first term because the largest independent set is searched for within an energy minimization problem, and where the penalty parameter V > 1 enforces the constraints.

Energetically, this Hamiltonian favors having each variable in the state xi = 1 unless a pair of vertices are connected by an edge. This second unconstrained formulation provides a straightforward connection to Rydberg atom arrays. Specifically, by mappingthe binary variables xi to two-level Rydberg atoms, MIS-UD problems can be encoded efficiently with Rydberg atoms placed at the vertices of the target problem graph. Strong Rydberg interactions between atoms (as described by the second term in the Hamiltonian) then prevent two neighboring atoms from being simultaneously in the excited Rydberg state.Using a coherent drive with Rabi frequency and detuning , one can then search for the ground state of the Hamiltonian H (encoding the MIS) via, for example, quantum-annealing-type approaches. Compare this blog post for more details on annealing-type quantum optimization algorithms running on Rydberg hardware.

To assess the practical hardness of the MIS-UD problem and compare the performance of various algorithms, we consider the following key figures of merit:

In our work, we studied the MIS-UD problem described earlier using both exact and heuristic algorithms. Here we provide a brief overview of our tool suite for more technical details we refer to Ref. [1].

We now turn to our numerical results. Here we will highlight just a few selected results for more details we refer to Ref. [1].

TTS as function of system size. First, we study TTS as a function of system size (given by the number of nodes in the graph). Our results are displayed in Fig. 2. We find that typical quasi-planar instances with Union-Jack-like connectivity (as studied in Ref. [2]) can be solved to optimality for up to thousands of nodes within minutes, with both custom and generic commercial solvers on commodity hardware, without any instance-specific fine-tuning. For most instances (taken as 98th percentile) we can upper-bound the TTS needed by classical B&B or SA solvers through a runtime scaling of the form TTS = O(2aN); we find a = 0.0045 and a = 0.0128 for our B&B and SA solvers, respectively; these resultsset an interesting, putative bar for quantum algorithms to beat. In addition, we observe a relatively large spreadspanning several orders of magnitude in TTS, displaying significant instance-to-instance variations, even for fixed system size, thus motivating a more detailed analysis of problem hardness, as discussed next.

Figure 2. TTS as a function of system size. (Left) B&B solver: Problems with hundreds (thousands) of nodes can be solved to optimality in subsecond (minute) timescales. The solid line is the linear regression over instances whose TTS are in the highest 2%. (Right) SA solver: Time required to reach 99% success probability for the heuristic SA solver as a function of system size (how long the solver should run for a 99% chance of finding the optimal solution). For every system size, 1000 random UD instances have been considered.

Hardness parameter.We now consider algorithmic performance in terms of the hardness parameter HP that accounts for both the degeneracy of the ground as well as first excited states. Our resultsfor both the exact SLA as well as the heuristic SA solvers are displayed in Fig 3., showing a remarkably different behavior. The SA solver displays a strong dependence on the hardness parameter. Conversely, virtually no dependence is observed for the exact SLA solver, thereby demonstrating that the conductance-like hardness parameter HP successfully captures hardness for algorithms undergoing Markov-chain dynamics, while alternative algorithmic paradigms (like sweeping line) may require a different notion of hardness.

In particular, we find that for the SA solver the success probability PMIS fits well to the functional form PMIS=1-exp(-C HPa), where C refers to a positive fitted constant and smaller values of a yield larger success rate. We find 0.66 for our implementation of SA, competitive with a=0.63 as reported for the optimized quantum algorithm demonstrated in Ref. [2] (for smaller systems than studied here, and when restricting the analysis to graphs with minimum energy gaps sufficiently large to be resolved in the duration of the noisy quantum evolution). This is much better than the SA baseline results in Ref. [2] with 1.03. As such, the quantum speedup reported in Ref. [2] could be classified as limited sequential quantum speedup, based on comparing a quantum annealing type algorithm with a particular implementation of classical SA, while our analysis points at a potential next milestone, in the form of the experimental demonstration of a (more general) limited non-tailored quantum speedup, by comparing the performance of the quantum algorithm to the best-known generic classical optimization algorithm.

Figure 3. Dependence on hardness parameter HP (for different system sizes, for lattices with side length L=13 and about 135 nodes up to lattices with L=33 and about 870 nodes). (Left) Time-to-solution (TTS) for the exact SLA solver as a function of the hardness parameter HP. Virtually no dependence on HP is observed, showing that TTS is fully determined by the system size N~L^2. (Right) Conversely, for the Markov-chain based SA solver, we observe a strong correlation between algorithmic performance and the hardness parameter HP. Here we plot log(1 P_MIS), for UD graphs selected from the top two percentile of hardness parameter for each system size. Power-law fits to the form ~HP^(-a) are used to extract scaling performance with graph hardness.

Tuning problem hardness. We now study hardness as we gradually change the topology of the problem instances. Specifically, we analyze TTS following two protocols by either (i) systematically tuning the blockade radius or (ii) randomly rewiring edges of the graph. While protocol (i) prepares UD graphs (with varying connectivity), protocol (ii) explicitly breaks the UD structure via random (potentially long-range) interactions, ultimately preparing random structure-less Erds-Rnyi (ER) graphs. The results of this analysis are shown in Fig. 4. We find that TTS for the established B&B solver can be tuned systematically over several orders of magnitude. As such, these two protocols suggest a potential recipe to benchmark quantum algorithms on instances orders of magnitude harder for established classical solvers than previously studied, and motivate interesting future experiments towards quantum advantage; in particular, our protocols help identify small, but hard instances, as needed for thorough scaling analyses.

Figure 4. Hardness transition. (Left) Hardness transition as a function of the disk radius (in units of the lattice spacing), as given by the time-to-solution (TTS) for the B&B solver, shown here for systems with about 350 nodes, with 100 random seeds per radius. (Right) Hardness transition from unit-disk to random Erds-Rnyi (ER) graphs (denoted by the red shaded bands). Here TTS is given as a function of the fraction of edges rewired. Starting from Union-Jack-type UD graphs (left), edges are randomly selected and rewired, thereby gradually breaking the UD connectivity, and ultimately generating random ER graphs (right). While the original UJ graphs can be solved to optimality in ~10^(-2)s, we observe TTS potentially orders of magnitudes larger in both plots.

Our work provides an in-depth look into the hardness of themaximum independent set problem on unit-disk graphs, the problem native toRydberg atom arrays. Our results establish well-defined goal posts for quantum algorithms to beat. In particular, we have shown that the hardness parameter put forward in Ref. [2] captures problem hardness for a certain class of Markov chain Monte Carlo solvers, while virtually no dependence between time-to-solution and this parameter is observed for alternative solvers. Finally, we have identified protocols to systematically tune time-to-solution over several orders of magnitude, pinpointing problem instancesorders of magnitude harder for established classical solvers than previously studied.

These results should help identify the most promising directions for applications of Rydberg devices and direct the communitys on-going efforts towards quantum advantage, hopefully inspiring many interesting future experiments further exploring the hardness of the MIS problem with Rydberg atom arrays.

The content and opinions in this blog are those of the third-party author and AWS is not responsible for the content or accuracy of this blog.

Thisblogpost is for informational purposes only and is not intended as legal, tax, financial, investment, accounting or regulatory advice. Opinions expressed herein are the personal views of the individual(s) and do not represent the views of JPMorgan Chase & Co. The accuracy of any statements, linked resources, reported findings or quotations is not the responsibility of JPMorgan Chase & Co.

[1]R. S. Andrist, M. J. A. Schuetz, P. Minssen, R. Yalovetzky, S. Chakrabarti, D. Herman, N. Kumar, G. Salton, R. Shaydulin, Y. Sun, M. Pistoia, and H. G. Katzgraber, Hardness of the Maximum Independent Set Problem on Unit-Disk Graphs and Prospects for Quantum Speedups, Phys. Rev. Research 5, 043277 (2023); arXiv:2307.09442.

[2]S. Ebadi, A. Keesling, M. Cain, T. T. Wang, H. Levine, D. Bluvstein, G. Semeghini, A. Omran, J.-G. Liu, R. Samajdar, et al., Quantum optimization of Maximum Independent Set using Rydberg atom arrays, Science 376, 1209 (2022).

[3]J. Wurtz, P. L. S. Lopes, N. Gemelke, A. Keesling, and S. Wang, Industry applications of neutral-atom quantum computing solving Independent Set problems, arXiv:2205.08500 (2022).

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JPMorgan Chase and AWS study the prospects for quantum speedups with near-term Rydberg atom arrays | Amazon ... - AWS Blog

12:23

Researchers using Quix Quantums technology have successfully demonstrated the on-chip generation of so-called Greenberger-Horne-Zeilinger (GHZ) states, a critical component for the advancement of photonic quantum computing. The Dutch startup focusing on photonics-based quantum computing hails the result as a breakthrough that validates the companys roadmap towards building a scalable universal quantum computer.

The creation of GHZ states is necessary for photonic quantum computers. In a matter-based quantum computer, qubits are stationary, typically positioned on a specialized chip. By contrast, a photonic quantum computer uses flying qubits of light to process and transmit information. This information is constantly passed from one state to another through a process called quantum teleportation. The GHZ states entanglements across three photonic qubits are the crucial resource enabling the computer to maintain this information.

This milestone demonstrates the capability of photonic quantum computers to generate multi-photon entanglement in a way that advances the roadmap toward large-scale quantum computation. The generation of GHZ states is evidence of the transformative potential of Quix Quantums photonic quantum computing technology, commented CEO Stefan Hengesbach of Quix.

Quix next challenge is now making many of these devices. When comparing one GHZ state to a million GHZ states, think of it as the spark needed to create a blazing fire. The more GHZ states a photonic quantum computer contains, the more powerful it becomes, added Chief Scientist Jelmer Renema.

Read more from the original source:
Quix checks off another condition to build universal quantum computer Bits&Chips - Bits&Chips